The Traveling Salesman Problem: A Neural Network Perspective

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The Traveling Salesman Problem:
A Neural Network Perspective
Jean-Yves Potvin
Centre de Recherche sur les Transports
Université de Montréal
C.P. 6128, Succ. A,
Montréal (Québec)
Canada H3C 3J7
potvin@iro.umontreal.ca
Abstract. This paper surveys the "neurally" inspired problem-
solving approaches to the traveling salesman problem, namely, t h e
Hopfield-Tank network, the elastic net, and the self-organizing map.
The latest achievements in the neural network domain are reported
and numerical comparisons are provided with the classical solution
approaches of operations research. An extensive bibliography with
more than one hundred references is also included.
Introduction
The Traveling Salesman Problem (TSP) is a classical
combinatorial optimization problem, which is simple to state b u t
very difficult to solve. The problem is to find the shortest possible
tour through a set of N vertices so that each vertex is visited exactly
once. This problem is known to be NP-complete, and cannot b e
solved exactly in polynomial time.
Many exact and heuristic algorithms have been devised in t h e
field of operations research (OR) to solve the TSP. We refer readers to
[15, 64, 65] for good overviews of the TSP. In the sections that follow,
we briefly introduce the OR problem-solving approaches to the TSP.
Then, the neural network approaches for solving that problem a r e
discussed.
Exact Algorithms
The exact algorithms are designed to find the optimal solution to
the TSP, that is, the tour of minimum length. They a r e
computationally expensive because they must (implicitly) consider

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all feasible solutions in order to identify the optimum. The exact
algorithms are typically derived from the integer linear
programming (ILP) formulation of the TSP
Min ΣiΣj dijxij
subject to:
Σj xij = 1 , i=1,...,N
Σi xij = 1 , j=1,...,N
(xij) X
xij = 0 or 1 ,
where dij is the distance between vertices i and j and the xij's are the
decision variables: xij is set to 1 when arc (i,j) is included in the tour,
and 0 otherwise. (xij) X denotes the set of subtour-breaking
constraints that restrict the feasible solutions to those consisting of a
single tour.
Although the subtour-breaking constraints can be formulated in
many different ways, one very intuitive formulation is
Σi,j SV
xij ≤ |Sv| - 1 (Sv V; 2 ≤ |Sv| ≤ N-2) ,
where V is the set of all vertices, Sv is some subset of V and |Sv| is
the cardinality of Sv. These constraints prohibit subtours, that is,
tours on subsets with less than N vertices. If there were such a
subtour on some subset of vertices Sv, this subtour would contain |Sv|
arcs. Consequently, the left-hand side of the inequality would b e
equal to |Sv|, which is greater than |Sv|-1, and the constraint would be
violated for this particular subset. Without the subtour-breaking
constraints, the TSP reduces to an assignment problem (AP), and a
solution like the one shown in Figure 1 would then be feasible.
Branch and bound algorithms are commonly used to find a n
optimal solution to the TSP, and the AP-relaxation is useful to
generate good lower bounds on the optimum value. This is true in
particular for asymmetric problems, where dij ≠ dji for some i,j. For
symmetric problems, like the Euclidean TSP (ETSP), the AP-solutions
often contain many subtours with only two vertices. Consequently,

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these problems are better addressed by specialized algorithms that
can exploit their particular structure. For instance, a specific ILP
formulation can be derived for the symmetric problem which allows
for relaxations that provide sharp lower bounds (e.g., the shortest
spanning one-tree[46]).
(a) (b)(a) (b)
Fig. 1. (a) Solving the TSP, (b) Solving the assignment problem.
It is worth noting that problems with a few hundred vertices
can now be routinely solved to optimality. Also, instances involving
more than 2,000 vertices have been addressed. For example, t h e
optimal solution to a symmetric problem with 2,392 vertices was
identified after two hours and forty minutes of computation time on
a powerful vector computer, the IBM 3090/600.[76,77] On the other
hand, a classical problem with 532 vertices took five and a half hours
on the same machine, indicating that the size of the problem is not
the only determining factor for computation time. We refer t h e
interested reader to [64] for a complete description of the state of the
art with respect to exact algorithms.
Heuristic Algorithms
Running an exact algorithm for hours on an expensive computer
may not be very cost-effective if a solution, within a few percent of
the optimum, can be found quickly on a microcomputer. Accordingly,
heuristic or approximate algorithms are often preferred to exact
algorithms for solving the large TSPs that occur in practice (e.g.,
drilling problems).

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Generally speaking, TSP heuristics can be classified as tour
construction procedures, tour improvement procedures, a n d
composite procedures, which are based on both construction a n d
improvement techniques.
(a) Construction procedures. The best known procedures in this
class gradually build a tour by selecting each vertex in turn and b y
inserting them one by one into the current tour. Various metrics a r e
used for selecting the next vertex and for identifying the best place
to insert it, like the proximity to the current tour and the minimum
detour.[88]
(b) Improvement procedures. Among the local improvement
procedures, the k-opt exchange heuristics are the most widely used,
in particular, the 2-opt, 3-opt, and Lin-Kernighan heuristics.[67,68]
These heuristics locally modify the current solution by replacing k
arcs in the tour by k new arcs so as to generate a new improved tour.
Figure 2 shows an example of a 2-opt exchange. Typically, t h e
exchange heuristics are applied iteratively until a local optimum is
found, namely a tour which cannot be improved further via t h e
exchange heuristic under consideration. In order to overcome t h e
limitations associated with local optimality, new heuristics like
simulated annealing and tabu search are being used.[25,39,40,60]
Basically, these new procedures allow local modifications that
increase the length of the tour. By this means, the method can escape
from local minima and explore a larger number of solutions.
The neural network models discussed in this paper are often
compared to the simulated annealing heuristic described in [60]. I n
this context, simulated annealing refers to an implementation based
on the 2-opt exchanges of Lin[67], where an increase in the length of
the tour has some probability of being accepted (see the description
of simulated annealing in Section 3).
(c) Composite procedures. Recently developed composite
procedures, which use both construction and improvement
techniques, are now among the most powerful heuristics for solving
TSPs. Among the new generation of composite heuristics, the most
successful ones are the CCAO heuristic,[41] the GENIUS heuristic,[38]
and the iterated Lin-Kernighan heuristic.[53]
For example, the iterated Lin-Kernighan heuristic can routinely
find solutions within 1% of the optimum for problems with up to

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10,000 vertices.[53] Heuristic solutions within 4% of the optimum for
some 1,000,000-city ETSPs are reported in [12]. Here, the tour
construction procedure is a simple greedy heuristic. At the start, each
city is considered as a fragment, and multiple fragments are built in
parallel by iteratively connecting the closest fragments together until
a single tour is generated. The solution is then processed by a 3-opt
exchange heuristic. A clever implementation of this procedure solved
some 1,000,000-city problems in less than four hours on a VAX
8550.
i j
l k
i j
l k
Fig. 2. Exchange of links (i,k),(j,l) for links (i,j),(k,l).
Artificial Neural Networks
Because of the simplicity of its formulation, the TSP has always
been a fertile ground for new solution ideas. Consequently, it is not
surprising that many problem-solving approaches inspired b y
artificial neural networks have been applied to the TSP.
Currently, neural networks do not provide solution quality that
compares with the classical heuristics of OR. However, the technology
is quite young and spectacular improvements have already been
achieved since the first attempts in 1985.[51] All of these efforts for
solving a problem that has already been quite successfully addressed
by operations researchers are motivated, in part, by the fact that
artificial neural networks are powerful parallel devices. They a r e
made up of a large number of simple elements that can process their
inputs in parallel. Accordingly, they lend themselves naturally to
implementations on parallel computers. Moreover, many neural

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network models have already been directly implemented in
hardware as "neural chips."
Hence, the neural network technology could provide a means to
solve optimization problems at a speed that has never been achieved
before. It remains to be seen, however, whether the quality of t h e
neural network solutions will ever compare to the solutions produced
by the best heuristics in OR. Given the spectacular improvements in
the neural network technology in the last few years, it would
certainly be premature at this time to consider this line of research
to be a "dead end."
In the sections that follow, we review the three basic neural
network approaches to the TSP, namely, the Hopfield-Tank network,
the elastic net, and the self-organizing map. Actually, the elastic nets
and self-organizing maps appear to be the best approaches for
solving the TSP. But the Hopfield-Tank model was the first to b e
applied to the TSP and it has been the dominant neural approach for
solving combinatorial optimization problems over the last decade.
Even today, many researchers are still working on that model, trying
to explain its failures and successes. Because of its importance, a
large part of this paper is thus devoted to that model and its
refinements over the years.
The paper is organized along the following lines. Sections 1 and 2
first describe the Hopfield-Tank model and its many variants.
Sections 3 and 4 are then devoted to the elastic net and the self-
organizing map, respectively. Finally, concluding remarks are made
in Section 5. Each basic model is described in detail and no deep
understanding of neural network technology is assumed. However,
previous exposure to an introductory paper on the subject could help
to better understand the various models.[61] In each section,
computation times and numerical comparisons with other OR
heuristics are provided when they are available. However, the OR
specialist must understand that the computation time for simulating
a neural network on a serial digital computer is not particularly
meaningful, because such an implementation does not exploit t h e
inherent parallelism of the model. For this reason, computation times
are often missing in neural network research papers.
A final remark concerns the class of TSPs addressed by neural
network researchers. Although the Hopfield-Tank network has been
applied to TSPs with randomly generated distance matrices,[106]

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virtually all work concerns the ETSP. Accordingly, Euclidean
distances should be assumed in the sections that follow, unless it is
explicitly stated otherwise.
The reader should also note that general surveys on the use of
neural networks in combinatorial optimization may be found in
[22, 70]. An introductory paper about the impacts of neurocomputing
on operations research may be found in [29].
Section 1. The Hopfield-Tank Model
Before going further into the details of the Hopfield model, it is
important to observe that the network or graph defining the TSP is
very different from the neural network itself. As a consequence, t h e
TSP must be mapped, in some way, onto the neural network
structure.
For example, Figure 3a shows a TSP defined over a
transportation network. The artificial neural network encoding that
problem is shown in Figure 3b. In the transportation network, t h e
five vertices stand for cities and the links are labeled or weighted b y
the inter-city distances dij (e.g., dNY,LA is the distance between New
York and Los Angeles). A feasible solution to that problem is the tour
Montreal-Boston-NY-LA-Toronto-Montreal, as shown by the bold
arcs.
In Figure 3b, the Hopfield network[50] is depicted as a 5x5
matrix of nodes or units that are used to encode solutions to the TSP.
Each row corresponds to a particular city and each column to a
particular position in the tour. The black nodes are the activated
units that encode the current solution (namely, Montreal is in first
position in the tour, Boston in second position, NY in third, etc.).
Only a few connections between the units are shown in Figure
3b. In fact, there is a connection between each pair of units, and a
weight is associated with each connection. The signal sent along a
connection from unit i to unit j is equal to the weight Tij if unit i is
activated. It is equal to 0 otherwise. A negative weight thus defines
an inhibitory connection between the two units. In such a case, it is
unlikely that both units will be active or "on" at the same time,
because the first unit that turns on immediately sends an inhibitory
signal to the other unit through that connection to prevent its
activation. On the other hand, it is more likely for both units to be on
at the same time if the connection has a positive weight. In such a

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case, the first unit that turns on sends a positive excitatory signal to
the other unit through that connection to facilitate its activation.
Montreal
Boston
NY
LA
Toronto
Boston
Montreal
LA
NY
Toronto
1 2 3 4 5
TSP problem(a)
Neural network representation(b)
d
NY,LA
TLA5,NY5
Fig. 3. Mapping a TSP onto the Hopfield network.

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In the TSP context, the weights are derived in part from t h e
inter-city distances. They are chosen to penalize infeasible tours and,
among the feasible tours, to favor the shorter ones. For example,
TLA5,NY5 in Figure 3b denotes the weight on the connection between
the units that represent a visit to cities LA and NY both in the fifth
position on a tour. Consequently, that connection should be inhibitory
(negative weight), because two cities cannot occupy the same exact
position. The first unit to be activated will inhibit the other unit via
that connection, so as to prevent an infeasible solution to occur.
In Section 1.1, we first introduce the Hopfield model, which is a
network composed of binary "on/off" or "0/1" units, like the artificial
neural network shown in Figure 3b. We will then describe t h e
Hopfield-Tank model, which is a natural extension of the discrete
model to units with continuous activation levels. Finally, t h e
application of the Hopfield-Tank network to the TSP will b e
described.
1.1 The Discrete Hopfield Model
The original Hopfield neural network model[50] is a fully
interconnected network of binary units with symmetric connection
weights between the units. The connection weights are not learned
but are defined a priori from problem data (the inter-city distances
in a TSP context). Starting from some arbitrarily chosen initial
configuration, either feasible or infeasible, the Hopfield network
evolves by updating the activation of each unit in turn (i.e., a n
activated unit can be turned off, and an unactivated unit can b e
turned on). The update rule of any given unit involves the activation
of the units it is connected to as well as the weights on t h e
connections. Via this update process, various configurations a r e
explored until the network settles into a stable configuration. In this
final state, all units are stable according to the update rule and do
not change their activation status.
The dynamics of the Hopfield network can be described formally
in mathematical terms. To this end, the activation levels of t h e
binary units are set to zero and one for "off" and "on," respectively.
Starting from some initial configuration {Vi}i=1,...,L, where L is t h e
number of units and Vi is the activation level of unit i, the network
relaxes to a stable configuration according to the following update
rule

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set Vi to 0 if ΣjTijVj < θi
set Vi to 1 if ΣjTijVj > θi
do not change Vi if ΣjTijVj = θi ,
where Tij is the connection weight between units i and j, and θi is t h e
threshold of unit i.
The units are updated at random, one unit at a time. Since t h e
configurations of the network are L-dimensional, the update of one
unit from zero to one or from one to zero moves the configuration of
the network from one corner to another of the L-dimensional unit
hypercube.
The behavior of the network can be characterized by a n
appropriate energy function. The energy E depends only on t h e
activation levels Vi (the weights Tij and the thresholds θi are fixed
and derived from problem data), and is such that it can only
decrease as the network evolves over time. This energy is given by
E = -1/2 ΣiΣj TijViVj + Σi θiVi . (1.1)
Since the connection weights Tij are symmetric, each term TijViVj
appears twice within the double summation of (1.1). Hence, this
double summation is divided by 2.
It is easy to show that a unit changes its activation level if a n d
only if the energy of the network decreases by doing so. In order to
prove that statement, we must consider the contribution Ei of a given
unit i to the overall energy E, that is,
Ei = - Σj TijViVj + θiVi .
Consequently,
if Vi = 1 then Ei = - Σj TijVj + θi
if Vi = 0 then Ei = 0 .
Hence, the change in energy due to a change ∆Vi in the activation
level of unit i is
∆Ei = - ∆Vi (Σj TijVj - θi) .

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Now, ∆Vi is one if unit i changed its activation level from zero to
one, and such a change can only occur if the expression between t h e
parentheses is positive. As a consequence, ∆Ei is negative and t h e
energy decreases. This same line of reasoning can be applied when a
unit i changes its activation level from one to zero (i.e., ∆Vi = -1).
Since the energy can only decrease over time and the number of
configurations is finite, the network must necessarily converge to a
stable state (but not necessarily the minimum energy state). In t h e
next section, a natural extension of this model to units with
continuous activation levels is described.
1.2 The Continuous Hopfield-Tank Model
In [51], Hopfield and Tank extended the original model to a fully
interconnected network of nonlinear analog units, where t h e
activation level of each unit is a value in the interval [0,1]. Hence, t h e
space of possible configurations {Vi}i=1,...,L is now continuous rather
than discrete, and is bounded by the L-dimensional hypercube
defined by Vi = 0 or 1. Obviously, the final configuration of t h e
network can be decoded into a solution of the optimization problem
if it is close to a corner of the hypercube (i.e., if the activation value
of each unit is close to zero or one).
The main motivation of Hopfield and Tank for extending t h e
discrete network to a continuous one was to provide a model that
could be easily implemented using simple analog hardware.
However, it seems that continuous dynamics also facilitate
convergence.[47]
The evolution of the units over time is now characterized by t h e
following differential equations (usually called "equations of motion")
dUi/dt = Σj TijVj + Ii - Ui, i=1,...,L (1.2)
where Ui, Ii and Vi are the input, input bias, and activation level of
unit i, respectively. The activation level of unit i is a function of its
input, namely
Vi = g(Ui) = 1/2 (1 + tanh Ui/Uo) = 1/ (1+ e-2Ui/Uo) . (1.3)
The activation function g is the well-known sigmoidal function,
which always returns a value between 0 and 1. The parameter Uo is

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used to modify the slope of the function. In Figure 4, for example, the
Uo value is lower for curve (2) than for curve (1).
Ui
g(Ui)
0
(1)
(2)
(2)
(1)
g(Ui) = 1
g(Ui) = 0
Fig. 4. The sigmoidal activation function.
The energy function for the continuous Hopfield-Tank model is
now
Vi
E = -1/2 ΣiΣj TijViVj - ΣiViIi + ∫ g-1(x)dx . (1.4)
0
Note in particular that d U i/dt = -dE/dVi. Accordingly, when t h e
units obey the dynamics of the equations of motion, the network is
performing a gradient descent in the network's configuration space
with respect to that energy function, and stabilizes at a local
minimum. At that point, dUi/dt = 0 and the input to any given unit i
is the weighted sum of the activation levels of all the other units plus
the bias, that is
Ui = Σj TijVj + Ii .

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1.3 Simulation of the Hopfield-Tank Model
In order to simulate the behavior of the continuous Hopfield-
Tank model, a discrete time approximation is applied to t h e
equations of motion
{Ui(t+∆t) - Ui(t)} / ∆t = Σj TijVj(t) + Ii - Ui(t) ,
where ∆t is a small time interval. This formula can be rewritten as
Ui(t+∆t) = Ui(t) + ∆t (Σj TijVj(t) + Ii - Ui(t)) .
Starting with some initial values {Ui(0)}i=1,...,L at time t=0, t h e
system evolves according to these equations until a stable state is
reached. During the simulation, ∆t is usually set to 10-5 or 10-6.
Smaller values provide a better approximation of the analog system,
but more iterations are then required to converge to a stable state.
In the literature, the simulations have mostly been performed
on standard sequential machines. However, implementations on
parallel machines are discussed in [10, 93]. The authors report that it
is possible to achieve almost linear speed-up with the number of
processors. For example, a Hopfield-Tank network for a 100-city TSP
took almost three hours to converge to a solution on a single
processor of the Sequent Balance 8000 computer.[10] The
computation time was reduced to about 20 minutes using eight
processors.
1.4 Application of the Hopfield-Tank Model to the TSP
In the previous sections, we have shown that the Hopfield-Tank
model performs a descent towards a local minimum of the energy
function E. The "art" of applying that model to the TSP is to
appropriately define the connection weights Tij and the bias Ii so
that the local minima of E will correspond to good TSP solutions.
In order to map a combinatorial optimization problem like t h e
TSP onto the Hopfield-Tank model, the following steps are suggested
in [83, 84]:
(1) Choose a representation scheme which allows the activation
levels of the units to be decoded into a solution of t h e
problem.

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(2) Design an energy function whose minimum corresponds to t h e
best solution of the problem.
(3) Derive the connectivity of the network from the energy
function.
(4) Set up the initial activation levels of the units.
These ideas can easily be applied to the design of a Hopfield-
Tank network in a TSP context:
(1) First, a suitable representation of the problem must be chosen.
In [51], the TSP is represented as an NxN matrix of units,
where each row corresponds to a particular city and each
column to a particular position in the tour (see Figure 1).
If the activation level of a given unit VXi is close to 1, it is then
assumed that city X is visited at the ith position in the tour. I n
this way, the final configuration of the network can b e
interpreted as a solution to the TSP. Note that N2 units a r e
needed to encode a solution for a TSP with N cities.
(2) Second, the energy function must be defined. The following
function is used in [51]
E = A/2 (ΣX Σi Σj i VXiVXj)
+B/2 (Σi ΣX ΣY X VXiVYi)
+C/2 (ΣXΣi VXi-N)2
+D/2 (ΣX ΣY X Σi dXYVXi (VYi+1 + VYi-1)) , (1.5)
where the A, B, C, and D parameters are used to weight t h e
various components of the energy.
The first three terms penalize solutions that do not correspond
to feasible tours. Namely, there must be exactly one activated
unit in each row and column of the matrix. The first a n d
second terms, respectively, penalize the rows and columns
with more than one activated unit, and the third term requires
a total of N activated units (so as to avoid the trivial solution
VXi=0 for all Xi). The fourth term ensures that the energy
function will favor short tours over longer ones. This t e r m
adds the distance dXY to the energy value when cities X and Y
are in consecutive positions in the tour (note that subscripts
are taken modulo N, so that VX,N+1 is the same as VX1).

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(3) Third, the bias and connection weights are derived. To do so,
the energy function of Hopfield and Tank (1.5) is compared to
the generic energy function (1.6), which is a slightly modified
version of (1.4): each unit has now two subscripts (city a n d
position) and the last term is removed (since it does not play
any role here)
E = -1/2 ΣXiΣYj TXiYjVXiVYj - ΣXi VXiIXi . (1.6)
Consequently, the weights TXiYj on the connections of t h e
Hopfield-Tank network are identified by looking at t h e
quadratic terms in the TSP energy function, while the bias IXi
is derived from the linear terms. Hence,
TXiYj = - A δXY (1- δij)
- B δij (1- δXY)
- C
- D dXY (δj,i+1 + δj,i-1) ,
IXi = + CNe ,
where δij=1 if i=j and 0 otherwise.
The first and second terms in the definition of the connection
weights stand for inhibitory connections within each row a n d
each column, respectively. Hence, a unit whose activation level
is close to 1 tends to inhibit the other units in the same row
and column. The third term is a global inhibitor term. The
combined action of this term and the input bias IXi, which a r e
both derived from the C term in the energy function (1.5),
favor solutions with a total of N activated units. Finally, t h e
fourth term is called the "data term" and prevents solutions
with adjacent cities that are far apart (namely, the inhibition
is stronger between two units when they represent two cities
X, Y in consecutive positions in the tour, with a large inter-city
distance dXY).
In the experiments of Hopfield and Tank, the parameter Ne in
the definition of the bias IXi = CNe does not always correspond
exactly to the number of cities N. This parameter is used b y
Hopfield and Tank to adjust the level of the positive bias
signal with respect to the negative signals coming through t h e
other connections, and it is usually slightly larger than N. Note

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finally that there are O(N4) connections between the N2 units
for a TSP with N cities.
(4) The last step is to set the initial activation value of each unit
to 1/N plus or minus a small random perturbation (in this way
the sum of the initial activations is approximately equal to N).
With this model, Hopfield and Tank were able to solve a
randomly generated 10-city ETSP, with the following parameter
values: A=B=500, C=200, D=500, Ne=15. They reported that for 2 0
distinct trials, using different starting configurations, the network
converged 16 times to feasible tours. Half of those tours were one of
the two optimal tours. On the other hand, the network was much less
reliable on a randomly generated 30-city ETSP (900 units). Apart
from frequent convergence to infeasible solutions, the network
commonly found feasible tours with a length over 7.0, as compared
to a tour of length 4.26 generated by the Lin-Kernighan exchange
heuristic.[68]
Three years later, it was claimed in [105] that the results of
Hopfield and Tank were quite difficult to reproduce. For the 10-city
ETSP of Hopfield and Tank, using the same parameter settings, t h e
authors report that on 100 different trials, the network converged to
feasible solutions only 15 times. Moreover, the feasible tours were
only slightly better than randomly generated tours. Other
experiments by the same authors, on various randomly generated
10-city ETSP problems, produced the same kind of results.
The main weaknesses of the original Hopfield-Tank model, a s
pointed out in [105] are the following.
(a) Solving a TSP with N cities requires O(N2) units and O(N4)
connections.
(b) The optimization problem is not solved in a problem space of
O(N!), but in a space of O(2N2
) where many configurations
correspond to infeasible solutions.
(c) Each valid tour is represented 2N times in the Hopfield-Tank
model because any one of the N cities can be chosen as t h e
starting city, and the two orientations of the tour a r e
equivalent for a symmetric problem. This phenomenon is
referred to as "2N-degeneracy" in neural network terminology.

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(d) The model performs a gradient descent of the energy function
in the configuration space, and is thus plagued with t h e
limitations of "hill-climbing" approaches, where a local
optimum is found. As a consequence, the performance of t h e
model is very sensitive to the initial starting configuration.
(e) The model does not guarantee feasibility. In other words,
many local minima of the energy function correspond to
infeasible solutions. This is related to the fact that t h e
constraints of the problem, namely that each city must b e
visited exactly once, are not strictly enforced but rather
introduced into the energy function as penalty terms.
(f) Setting the values of the parameters A, B, C, and D is much
more an art than a science and requires a long "trial-and-
error" process. Setting the penalty parameters A, B, and C to
small values usually leads to short but infeasible tours.
Alternatively, setting the penalty parameters to large values
forces the network to converge to any feasible solution
regardless of the total length. Moreover, it seems to b e
increasingly difficult to find "good" parameter settings as t h e
number of cities increases.
(g) Many infeasible tours produced by the network visit only a
subset of cities. This is due to the fact that the third term in
the energy function (C term) is the only one to penalize such a
situation. The first two terms (A and B terms), as well as t h e
fourth term (D term), benefit from such a situation.
(h) It usually takes a large number of iterations (in t h e
thousands) before the network converges to a solution.
Moreover, the network can "freeze" far from a corner of t h e
hypercube in the configuration space, where it is not possible
to interpret the configuration as a TSP solution. This
phenomenon can be explained by the shape of the sigmoidal
activation function which is very flat for large positive a n d
large negative Ui's (see Figure 2). Consequently, if t h e
activation level Vi of a given unit i is close to zero or one, even
large modifications to Ui will produce only slight modifications
to the activation level. If a large number of units are in this
situation, the network will evolve very slowly, a phenomenon
referred to as "network paralysis."
Paralysis far from a corner of the hypercube can occur if t h e
slope of the activation function is not very steep. In that case,
the flat regions of the sigmoidal function extend further a n d

1 8
affect a larger number of units (even those with activation
levels far from zero and one).
(i) The network is not adaptive, because the weights of t h e
network are fixed and derived from problem data, rather than
taught from it.
The positive points are that the model can be easily
implemented in hardware, using simple analog devices, and that it
can also be applied to non-Euclidean TSPs, in particular, problems
that do not satisfy the triangle inequality and cannot be interpreted
geometrically.[106] This is an advantage over the geometric
approaches that are presented in Sections 3 and 4.
Section 2. Variants of the Hopfield-Tank Model
Surprisingly enough, the results of Wilson and Pawley did not
discourage the community of researchers but rather stimulated t h e
search for ways to improve the original Hopfield-Tank model. There
were also numerous papers providing in-depth analysis of the model
to explain its failures and propose various improvements to t h e
method. [4,5,6,7,17,24,56,86,87,90,108]
The modifications to the original model can be classified into six
distinct categories: modifications to the energy function, techniques
for estimating "good" parameter settings, addition of hard constraints
to the model, incorporation of techniques to escape from local
minima, new problem representations, and modifications to t h e
starting configurations. We now describe each category, a n d
emphasize the most important contributions.
2.1 Modifications to the Energy Function
The first attempts were aimed at modifying the energy function
to improve the performance of the Hopfield-Tank model. Those
studies, which add or modify terms to push the model towards
feasible solutions, are mostly empirical.
(a) In [18, 75, 78, 95], the authors suggest replacing either the third
term (C term) or the three first terms (A, B, and C terms) of t h e
original energy function (1.5) by
F/2 ΣX (Σi VXi -1)2 + G/2 Σi (ΣX VXi -1)2 .

1 9
This modification helps the model to converge towards feasible
tours, because it heavily penalizes configurations that do not have
exactly one active unit in each row and each column. In particular, it
prevents many solutions that do not incorporate all cities. Note that a
formulation where the A, B, and C terms are replaced by the two new
terms can be implemented with only O(N3) connections. In [14], t h e
author proposes an alternative approach to that problem by adding
an additional excitatory bias to each unit so as to get a larger number
of activated units.
(b) In [18], the authors suggest the addition of a new penalty t e r m
in the energy function (1.5). This term attempts to drive the search
out from the center of the hypercube (and thus towards the corners
of the hypercube) so as to alleviate the network paralysis problem.
The additional term is
F/2 ΣXi VXi (1-VXi) = F/2 (N2/4 - ΣXi (VXi - 1/2)2) .
In the same paper, they also propose a formulation where t h e
inter-city distances are only used in the linear components of t h e
energy function. Hence, the distances are provided to the network
via the input bias rather than encoded into the connection weights. I t
is a great advantage for a hardware implementation, like a neural
chip, because the connection weights do not change from one TSP
instance to another and can be fixed into the hardware at fabrication
time. However, a new representation of the problem with O(N3) units
is now required.
Brandt and his colleagues[18] report that the Hopfield-Tank
model with the two modifications suggested in (a) and (b)
consistently converged to feasible tours for randomly generated 1 0 -
city ETSPs. Moreover, the average length was now much better than
the length of randomly generated tours. Table 1 shows these results
for problems with 10, 16, and 32 cities. Note that the heading
"Manual" in the Table refers to tours constructed manually by hand.

2 0
Table 1. Comparison of Results for Three Solution Procedures
2.2 Finding Good Settings for the Parameter Values
In [44, 45], the authors experimentally demonstrate that various
relationships among the parameters of the energy function must b e
satisfied in order for the Hopfield-Tank model to converge to feasible
tours. Their work also indicates that the region of good settings in the
parameter space quickly gets very narrow as the number of cities
grows. This study supports previous observations about the difficulty
to tune the Hopfield-Tank energy function for problems with a large
number of cities.
Cuykendall and Reese[28] also provide ways of estimating
parameter values from problem data, as the number of cities
increases. In [4, 5, 6, 7, 27, 57, 58, 74, 86, 87] theoretical
relationships among the parameters are investigated in order for
feasible tours to be stable. The work described in these papers is
mostly based on a close analysis of the eigenvalues and eigenvectors
of the connection matrix.
Wang and Tsai[102] propose to gradually reduce the value of
some parameters over time. However, time-varying parameters
preclude a simple hardware implementation. Lai and Coghill[63]
propose the genetic algorithm, as described in [49], to find good
parameter values for the Hopfield-Tank model.
Along that line of research, the most impressive practical results
are reported in [28]. The authors generate feasible solutions for a
165-city ETSP, by appropriately setting the bias of each unit and t h e
U0 parameter in the sigmoidal activation function (1.3). Depending on
the parameter settings, it took between one hour and 10 hours of
computation time on an APOLLO DN4000 to converge to a solution.
The computation times were in a range of 10 to 30 minutes for
Number of Number of
Average Tour Length
Cities Problems Brandt Manual Random
1 0 2 1 2.97 2.92 5.41
1 6 1 0 3.70 3.57 9.77
3 2 4 5.48 4.57 17.12

2 1
another 70-city ETSP. Unfortunately, no comparisons are provided
with other problem-solving heuristics.
2.3 Addition of Constraints to the Model
The approaches that we now describe add new constraints to t h e
Hopfield-Tank model, so as to restrict the configuration space to
feasible tours.
(a) In [81, 97, 98] the activation levels of the units are normalized
so that Σi VXi =1 for all cities X. The introduction of these additional
constraints is only one aspect of the problem-solving methodology,
which is closely related to the simulated annealing heuristic.
Accordingly, the full discussion is deferred to Section 2.4, where
simulated annealing is introduced.
(b) Other approaches are more aggressive and explicitly restrict t h e
configuration space to feasible tours. In [96], the authors calculate
the changes required in the remaining units to maintain a feasible
solution when a given unit is updated. The energy function is then
evaluated on the basis of the change to the updated unit and all t h e
logically implied changes to the other units. This approach converges
consistently to feasible solutions on 30-city ETSPs. The tours are only
5% longer on average than those generated by the simulated
annealing heuristic.
In [69], the author updates the configurations using Lin a n d
Kernighan's exchange heuristic.[68] Foo and Szu[35] use a "divide-and-
conquer" approach to the problem. They partition the set of cities
into subsets and apply the Hopfield-Tank model to each subset. The
subtours are then merged back together into a single larger tour with
a simple heuristic. Although their approach is not conclusive, t h e
integration of classical OR heuristics and artificial intelligence within
a neural network framework could provide interesting research
avenues for the future.
2.4 Incorporation of Techniques to Escape from Local Minima
The Hopfield-Tank model converges to a local minimum, and is
thus highly sensitive to the starting configuration. Hence, various
modifications have been proposed in the literature to alleviate this
problem.

2 2
(a) In [1, 2, 3], a Boltzmann machine[48] is designed to solve the TSP.
Basically, a Boltzmann machine incorporates the simulated annealing
heuristic[25,60] within a discrete Hopfield network, so as to allow t h e
network to escape from bad local minima.
The simulated annealing heuristic performs a stochastic search
in the space of configurations of a discrete system, like a Hopfield
network with binary units. As opposed to classical hill-climbing
approaches, simulated annealing allows modifications to the current
configuration that increase the value of the objective or energy
function (for a minimization problem). More precisely, a modification
that reduces the energy of the system is always accepted, while a
modification that increases the energy by ∆E is accepted with
Boltzmann probability e-∆E/T, where T is the temperature parameter.
At a high temperature, the probability of accepting an increase to t h e
energy is high. This probability gets lower as the temperature is
reduced. The simulated annealing heuristic is typically initiated at a
high temperature, where most modifications are accepted, so as to
perform a coarse search of the configuration space. The temperature
is then gradually reduced to focus the search on a specific region of
the configuration space.
At each temperature T, the configurations are modified
according to the Boltzmann update rule until the system reaches a n
"equilibrium." At that point, the configurations follow a Boltzmann
distribution, where the probability of the system being in
configuration s' at temperature T is
e -E(s')/T
PT(s') = _________ . (2.1)
Σs e -E(s)/T
Here, E(s') is the energy of configuration s', and the denominator is
the summation over all configurations. According to that probability,
configurations of high energy are very likely to be observed at high
temperatures and much less likely to be observed at low
temperatures. The inverse is true for low energy configurations.
Hence, by gradually reducing the temperature parameter T and b y
allowing the system to reach equilibrium at each temperature, t h e
system is expected to ultimately settle down at a configuration of low
energy.

2 3
This simulated annealing heuristic has been incorporated into
the discrete Hopfield model to produce the so-called Boltzmann
machine. Here, the binary units obey a stochastic update rule, rather
than a deterministic one. At each iteration, a unit is randomly
selected and the consequence of modifying its activation level (from
zero to one or from one to zero) on the energy is evaluated. The
probability of accepting the modification is then
1
____________ ,
1 + e ∆E/T
where ∆E is the modification to the energy.
This update probability is slightly different from the one used in
simulated annealing. In particular, the probability of accepting a
modification that decreases the energy of the network (i.e., ∆E < 0) is
not one here, but rather a value between 0.5 and one. However, this
new update probability has the same convergence properties as t h e
one used in simulated annealing and, in that sense, the two
expressions are equivalent.
Aarts and Korst[1,2,3] design a Boltzmann machine for solving t h e
TSP based on these ideas. Unfortunately, their approach suffers from
very slow convergence and, as a consequence, only 30-city TSPs have
been solved with this model.
(b) In [43], the Boltzmann Machine is generalized to units with
continuous activation levels. A truncated exponential distribution is
used to compute the activation level of each unit. As for the discrete
Boltzmann machine, the model suffers from slow convergence, a n d
only small 10-city ETSPs have been solved.
(c) The research described in [81, 97, 98], which is derived from t h e
mean-field theory, is probably the most important contribution to
the literature relating to the Hopfield-Tank model since its original
description in [51]. The term "mean-field" refers to the fact that t h e
model computes the mean activation levels of the stochastic binary
units of a Boltzmann machine.
This section focuses on the model of Van den Bout a n d
Miller,[97,98] but the model of Peterson and Soderberg[81] is similar.
We first introduce the iterative algorithm for updating t h e

2 4
configurations of the network. Then, we explain the relationships
between this model and the Boltzmann machine.
The neural network model introduced in [97] is characterized b y
a new simplified energy function
E = dmax/2 (Σi ΣX ΣY X VXiVYi)
+ ΣX ΣY X Σi dXYVXi (VYi+1 + VYi-1) . (2.2)
The first summation penalizes solutions with multiple cities at t h e
same position, while the second summation computes the tour length.
Note that the penalty value is weighted by the parameter dmax.
Starting from some arbitrary initial configuration, the model
evolves to a stable configuration that minimizes (2.2), via t h e
following iterative algorithm:
1. Set the temperature T.
2. Select a city X at random.
3. Compute
UXi = - dmax ΣY X VYi - ΣY X dXY(VYi+1 + VYi-1), i=1,...,N.
4. Compute
e UXi/T
VXi = _________ , i=1,...,N .
Σj e UXj/T
5. Evaluate the energy E.
6. Repeat Steps 2 to 5 until the energy no longer decreases
(i.e., a stable configuration has been reached).
We note that the activation levels always satisfy the constraints
Σi VXi = 1 , for all cities X.
Accordingly, each value VXi can be interpreted as the probability that
city X occupies position i. When a stable configuration is reached, t h e
activation levels VXi satisfy the following system of equations (called
the "mean-field equations")

2 5
e UXi/T
VXi = _________ , (2.3)
Σj e UXj/T
where UXi = - dE/dVXi (see Step 3 of the algorithm).
In order to understand the origin of the mean-field equations,
we must go back to the evolution of a discrete Hopfield network with
binary units, when those units are governed by a stochastic update
rule like the Boltzmann rule (see the description of the simulated
annealing heuristic and the Boltzmann machine in point (a)).
It is known that the configurations of that network follow a
Boltzmann distribution at equilibrium (i.e., after a large number of
updates). Since the network is stochastic, it is not possible to know
what the exact configuration will be at a given time. On the other
hand, the average or mean activation value of each binary unit a t
Boltzmann equilibrium at a given temperature T is a deterministic
value which can be computed as follows
<VXi> = Σs PT(s)VXi(s) = ΣsXi PT(sXi) .
In this equation, the summations are restricted to the configurations
satisfying Σj VXj = 1, for all cities X (so as to comply with the model of
Van den Bout and Miller), PT(s) is the Boltzmann probability of
configuration s at temperature T, VXi(s) is the activation level of unit
Xi in configuration s, and sXi denotes the configurations where
VXi = 1. Hence, we have
ΣsXi e
−E(sXi)/T
<VXi> = _________________ .
Σj ΣsXj e
−E(sXj)/T
In this formula, the double summation in the denominator is
equivalent to a single summation over all configurations s, because
each configuration contains exactly one activated unit in {Xj}j=1,...,N
(Σj VXj = 1 and VXj is either zero or one).
Now, we can apply the so-called "mean-field approximation" to
<VXi>. Rather than summing up over all configurations, we assume
that the activation levels of all units that interact with a given unit Xj
are fixed at their mean value. For example, rather than summing u p

2 6
over all configurations sXi in the numerator (configurations where
VXi=1), we fix the activation levels of all the other units to their
mean value. In this way, the summation can be removed. By
applying this idea to both the numerator and the denominator, a n d
by observing that -UXi is the contribution of unit Xi to the energy
(2.2) when VXi=1, the expression can be simplified to
e<UXi>/T
<VXi> = ____________ ,
Σj e<UXj>/T
where
<UXi> = - dmax ΣY≠X <VYi> - ΣY≠X dXY(<VYi+1> + <VYi-1>) .
These equations are the same as the equations of Van den Bout
and Miller (2.3). Hence, the VXi values computed via their iterative
algorithm can be interpreted as the mean activation levels of t h e
corresponding stochastic binary units at Boltzmann equilibrium (at a
given temperature T). At low temperatures, the low energy
configurations have high Boltzmann probability and they dominate in
the computation of the mean values <VXi>. Hence, the stable
configuration computed by the algorithm of Van den Bout and Miller
is expected to be of low energy, for a sufficiently small parameter
value T, because the stable configuration is composed of those mean
values <VXi>.
As noted in [98], all the activation levels are the same at high
temperatures, that is, VXi → 1/N when T → . As the temperature
parameter is lowered, each city gradually settles into a single
position, because such configurations correspond to low energy
states. In addition, the model also prevents two cities from occupying
the same position, because a penalty of dm a x /2 is incurred in t h e
energy function. If the parameter dmax is set to a value slightly
larger than twice the largest distance between any two cities, t h e
network can find a configuration with lower energy simply b y
moving one of the two cities into the empty position. Feasible tours
are thus guaranteed through the combined actions of the new energy
function and the additional constraints imposed on the activation
levels VXi (once again, for a sufficiently small parameter value T).
It is clear that the key problem is to identify a "good" value for
the parameter T. By gradually decreasing the temperature, Van d e n

2 7
Bout and Miller identified a critical value Tc where all the energy
minimization takes place. Above the critical value Tc, the units rarely
converge to zero or one and feasible tours do not emerge. Below Tc,
all the tours generated are feasible, and the best tours emerge when
T is close to Tc. Obviously, the critical temperature value is highly
dependent on the particular TSP to be solved. In [97, 98], Van d e n
Bout and Miller describe a methodology for estimating that value
from the inter-city distances. Using various T and dmax parameter
values, their best tour on a 30-city TSP had a length of 26.9, a s
compared to 24.1 for a tour obtained with the simulated annealing
heuristic.
Peterson and Soderberg[81] test a similar model on much larger
problems, ranging in size from 50 to 200 cities. They observe that
the length of the tours generated by the neural network approach
are about 8% longer on average than the tours generated with a
simulated annealing heuristic. Moreover, no tour was more than 10%
longer than the corresponding simulated annealing tour. However,
the average tour lengths are not provided (rather, the results a r e
displayed as small histograms). Also, no computation times a r e
reported.
It is quite interesting to note that Bilbro et al.[13] have shown
that the evolution of the above model is equivalent to the evolution
of the Hopfield-Tank network governed by the equations of motion
(see Section 1). However, convergence to a stable configuration is
much faster by solving the mean-field equations. This increased
convergence speed explains the successes of Peterson and Soderberg
who routinely found feasible solutions to the TSP with up to 2 0 0
cities, those being the largest problems ever solved with models
derived from the work of Hopfield and Tank.
(d) Mean-field annealing refers to the application of the mean-field
algorithm, as described in (c), with a gradual reduction of t h e
temperature from high to low values, as in the simulated annealing
heuristic.[13,19,80,107] As pointed out in [97], this approach is of little
use if the critical temperature, where all the energy minimization
takes place, can be accurately estimated from problem data. If t h e
estimate is not accurate, then it can be useful to gradually decrease
the temperature.
(e) In [8, 26, 66], random noise is introduced into the activation
level of the units in order to escape from local minima. The random

2 8
noise follows a normal distribution with mean zero, and its intensity
is modulated by a temperature parameter which is gradually
reduced, as in the simulated annealing heuristic.
(f) Finally, Wells[103] describes a network of neural oscillators. The
network evolves towards sustained oscillations between equivalent
configurations, as opposed to the classical Hopfield-Tank model
which is evolving towards a single stable configuration.
2.5 Using New Problem Representations
In [54, 55], a new problem representation based on an integer
linear programming formulation of the TSP is proposed. Each unit
VXY now stands for an arc (X,Y) in the graph, and that unit is on
when arc (X,Y) is in the tour. This "adjacency representation"
requires O(N2) units and only O(N3) connections. Moreover, the inter-
city distances now occur in the linear terms of the energy function
and are thus provided to the network via the input bias
E = A/2 ΣX (ΣX Y VXY - 2)2 + B/2 ΣY (ΣX Y VXY - 2)2
+ C/2 ΣX ΣY VXY dXY .
In this new energy function, the first two terms penalize cities that
do not have exactly two neighbors.
Basically, this model solves the assignment problem associated
to the TSP when the subtour-breaking constraints are relaxed.
Accordingly, the network usually ends up with multiple subtour
solutions. In order to address that problem, a second binary layer is
connected to the Hopfield-Tank layer, so as to break the stability of
multiple subtour solutions and drive the network towards feasible
solutions.
Xu and Tsai[106] use the same representation and energy
function. However they address the multiple subtours problem in a
different way. When the neural network reaches a multiple subtour
solution, they add a higher-order connection between the units
involved in a subtour, so as to forbid any later occurrence of that
subtour (a higher-order connection can involve an arbitrary number
of units, as opposed to a standard connection which involves only
two units). Higher-order connections are incrementally added in this
way, until the network stabilizes with a feasible solution. That

2 9
procedure is usually slow to converge. In the worst case, all possible
subtours must be forbidden for a valid solution to emerge!
In practice, the authors often stop the procedure before a
feasible solution is reached, and then merge the remaining subtours
into a single tour via the "patching" heuristic proposed in [59]. This
simple heuristic connects two subtours by deleting one link in each
subtour and by introducing two new links to generate a single larger
tour. In Figure 5, for example, the links (i,j) and (k,l) have been
replaced by the links (i,l) and (j,k). The links to be replaced a r e
chosen so as to minimize the length of the new enlarged tour. As
noted by Xu and Tsai, the number of solutions with multiple subtours
tends to increase when the number of cities increases, and t h e
patching heuristic thus plays an important role in large problems.
i
j k
l
Fig. 5. The patching heuristic.
Euclidean problems with up to 150 cities have been addressed
with this approach. However, the authors have shown that better
solutions can be generated by applying 25 distinct runs of a 2-opt
exchange heuristic from different randomly generated initial
solutions, and by taking the best overall solution.
The authors also apply their model to TSPs with randomly
generated distance matrices. In that context, the model provided
better solutions than the 2-opt (30 runs) and the Lin-Kernighan
heuristics (30 runs). These results are supported by the probabilistic
analysis in [59], where it is shown that solving the assignment
problem first and then applying the patching heuristic provides near
optimal solutions to TSPs with random distance matrices.

3 0
2.6 Starting from Different Initial Configurations
In [98], the authors suggest starting from a good initial
configuration by forcing cities that are close in distance to b e
adjacent in the tour (which means that some units are preset to one).
This technique is called "heuristic seeding." Other authors suggest
starting from the center of the unit hypercube, that is, setting t h e
initial activation value of all units to 1/2.[44]
This discussion shows that the Hopfield-Tank model and its
many variants are still restricted to relatively small TSPs with 2 0 0
cities or less. The main results with respect to this model a r e
summarized in Table 2.
Paper Largest TSP
(Number of
Cities)
Length of
Tour from
Hopfield-
Tank Models
TSP
Heuristic
Length of
Tour from
TSP
Heuristic
Hopfield and
Tank
(1985)
3 0 5.07 LK 4.26
Brandt et al.
(1988)
3 2 5.48 MT 4.57
Cuykendall
and Reese
(1989)
1 6 5 9.409-9.933a NA NA
Peterson and
Soderberg
(1989)
2 0 0 NA SA NA
Xu and
Tsai
(1991)
1 0 0
1 5 0
592.3
9.529b
TO
LK
570.2c
9.616d
a Computation time of 1 to 10 hours on an Apollo DN4000
b Best of 5 runs with post-optimization by Lin-Kernighan
c Best of 25 runs starting from randomly generated tours
d Best of 30 runs starting from randomly generated tours
LK L i n - K e r n i g h a n

3 1
MT Manual Tour
NA Not Available
SA Simulated Annealing
TO 2-opt
Table 2. Comparison of Results Produced by Hopfield-Tank Models
and TSP Heuristics
Although the work in [81, 97, 98] has shown that it is possible to
design models that consistently converge to feasible tours (one of t h e
problems with the original formulation), those tours do not y e t
compare with the tours generated by other TSP heuristics, like
simulated annealing. Furthermore, the convergence is quite slow
when the model is executed on a sequential computer. These
observations have led individuals to explore new lines of research, in
particular, elastic nets and self-organizing maps.
Section 3. The Elastic Net
The elastic net approach[32] is fundamentally different from t h e
approach of Hopfield and Tank. It is a geometrically inspired
algorithm, originating in the work of Willshaw and Von d e r
Malsburg[104], and is closely related to the self organizing map, a s
described in Section 4.
The elastic net algorithm is an iterative procedure where M
points, with M larger than the number of cities N, are lying on a
circular ring or "rubber band" originally located at the center of t h e
cities. The rubber band is gradually elongated until it passes
sufficiently near each city to define a tour. During that process two
forces apply: one for minimizing the length of the ring, and the other
one for minimizing the distance between the cities and the points on
the ring. These forces are gradually adjusted as the procedure
evolves.
Figures 6a, 6b and 6c show how the elastic net typically evolves
over time. In the figure, the small black circles are the points located
on the ring which are migrating towards the cities. The final solution
is shown in Figure 6d.
The elastic net algorithm will now be introduced more formally.
Let Xi be the position of the ith city, i=1,...,N and let Yj be the position
of the jth point on the ring, j=1,...,M. In order to get a feasible tour,
one point in the ring should converge to each city. Accordingly, a t

3 2
each step, the ring points are updated in parallel according to t h e
following equations
∆Yj = α Σi wij (Xi-Yj) + βK (Yj+1 + Yj-1 - 2Yj) , j=1,...,M (3.1)
where
φ(dXiYj, K)
wij = ________________ ,
Σk φ(dXiYk,K)
φ(d,K) = e-d2/2K2
.

3 3
Montreal
NY
LA
Toronto Boston
(c)
Montreal
NY
LA
Toronto
(a)
Boston
(d)
(b)
Boston
Montreal
NY
LA
Toronto
Montreal
NY
LA
Toronto Boston
Fig. 6. Evolution of the elastic net over time (a) (b) (c)
and the final tour (d).
In equation (3.1), α and β are constant parameters, K is t h e
"scale" parameter, dXiYj is the Euclidean distance between Xi and Yj,
and wij is a measure of the "influence" of city i on point j. The α t e r m
drives the points on the ring towards the cities, so that for each city i
there is at least one ring point j within distance K. The β term is
aimed at keeping neighboring points together so as to produce a
short tour.

3 4
Figure 7 illustrates the two forces that impact point j. The first
force (1), derived from the α term in the update equations, is quite
easy to understand and is aimed at driving point j towards city i. The
second force (2), derived from the β term, can be more easily
interpreted by considering the equivalence
Yj+1 + Yj-1 - 2Yj = (Yj+1 - Yj) + (Yj-1 - Yj) .
Accordingly, this force defines a tension on the ring that keeps
neighboring points together.
The update equations can be expressed as the derivatives of an
appropriate energy function E, namely
∆Yj = -K dE/dYj ,
where
E = - α K Σi ln Σj φ(dXiYj, K) + β/2 Σj dYjYj+1
2 . (3.2)
Consequently, this algorithm finds a local minimum of t h e
energy function (3.2) by performing a gradient descent in t h e
(continuous) two-dimensional Euclidean space. Note also that when
K → 0 and M/N → , the global minimum of the energy function
solves the TSP. Accordingly, as the algorithm evolves, the scale
parameter K is gradually reduced (like the temperature parameter in
the simulated annealing heuristic). When K is 0, the energy reduces
to the sum of the squared distances between the points on the ring.
This is equivalent to minimizing the tour length at the limit M/N →
.
city i
j
j-1
j+1
(1)
(2)

3 5
Fig. 7. Forces that impact ring point j.
With this algorithm, Durbin and Willshaw[32] solved five
different sets of randomly generated 50-city ETSPs. It took about
1,250 iterations to converge and the tour lengths were, on average,
only 2% longer than those obtained with the simulated annealing
heuristic (average of five distinct runs). The authors report that t h e
computation times for both the elastic net and the simulated
annealing heuristic are approximately the same. The elastic n e t
algorithm also solved a 100-city ETSP and generated a tour about 1%
longer than the best tour obtained after eight million trials of t h e
simulated annealing heuristic. In this experiment, the following
parameter settings were used: M = 2.5N, α=0.2, β=2.0, Kmax = 0.2, a n d
Kmin = 0.02.
In a follow-up paper,[31] Durbin and his colleagues analyze how
the shape of the energy function (3.2) is modified by the value of t h e
scale parameter. It is shown that when K → 0, the local mimima of
the energy function are such that each city Xi is matched by at least
one Yj. Moreover, each Xi is matched by exactly one Yj if parameters
α and β satisfy the condition α/β < A, where A is the shortest
distance from a point being attracted to some city to any other point
not being attracted to that city.
As pointed out by Simmen[92], the above conditions ensure that
each city is matched by exactly one point on the ring, but a point can
still match two or more cities, if those cities are sufficiently close.
Although the problem could be solved by using so many points on
the ring that the typical spacing between neighboring points would
be much less than the minimum inter-city distance, Simmen
provides additional conditions on the α/β ratio to achieve the same
result, and avoid the computational burden associated with a large
M.
Two variants of this algorithm have been proposed in t h e
literature.
(a) Burr[23] reports improvements to the convergence speed of t h e
elastic net algorithm, by using the new update equations

3 6
∆Yj = α Σi wij (Xi-Yj) + α Σi sij (Xi-Y(Xi)) + βK (Yj+1 + Yj-1 - 2Yj) ,
where
Y(Xi) is the closest ring point to Xi ,
φ(dY(Xi)Yj, K)
sij = ___________________ .
Σk φ(dY(Xi)Yk, K)
The first and third terms in the new update equations
correspond to the two original terms in (3.1). The middle term is
added by the author to favor short tours by keeping the points
evenly distributed on the ring. The update equations are applied
with a varying K parameter whose value is computed at each
iteration, and derived from the average squared distance between
each city and its closest point on the tour.
The author reports that on the five sets of 50-city ETSPs
described in [32], the solutions are of similar quality as those of t h e
original elastic net algorithm. However, it took only 30 iterations on
average to converge to a solution, as compared to 1,250 iterations for
the model of Durbin and Willshaw.
(b) In [16], the ring points are updated by only considering t h e
cities that have a significant influence on them. The authors report
that this filtering mechanism reduces the computation times without
affecting the quality of the solution. The authors have solved
problems ranging in size from 100 to 1,000 cities. Their solutions a r e
about 5% to 10% longer than those of the Lin-Kernighan heuristic.
However, on the 1,000-city problems, the elastic net algorithm took
only half the time of the Lin-Kernighan heuristic.
To summarize this section, a benchmark study in [79] compares
five different problem-solving approaches on randomly generated
ETSPs ranging in size from 50 to 200 cities. In this study, the elastic
net and the model of Peterson and Soderberg[81], a variant of t h e
Hopfield-Tank model, are compared. The main results are shown in
Table 3.

3 7
These results clearly indicate that the elastic net is superior to
the model of Peterson and Soderberg. Another comparison in favor of
the elastic net can be established by comparing the figures in Durbin
and Willshaw[32] and in Hopfield and Tank[51]. On the same 30-city
ETSP, the best tour generated by the Hopfield-Tank model had a
length of 5.07, while the elastic net found a tour of length 4.26 on a
single trial (the same tour as the one found by the Lin-Kernighan
exchange heuristic[68]).
As a final remark, interesting relationships can be established
between the elastic net model and the Hopfield-Tank network. I n
particular, Simic[91] demonstrates that the elastic net and t h e
Hopfield-Tank model can both be derived from statistical mechanics.
The differences observed between the two approaches stem from
different ways of handling the constraints. The elastic net, by its
very nature, strictly enforces some TSP constraints, while t h e
Hopfield-Tank model transforms these constraints into penalties.
Yuille[109] provides a similar study relating elastic nets to deformable
templates, which are simple parameterized shapes that dynamically
evolve to fit an image.
a Best of five runs on each problem
EN Elastic Net
PS Peterson and Soderberg
Table 3. Comparison of Results for the Elastic Net and the Model of
Peterson and Soderberg
Number of
Average Tour Length
Cities EN PSa
5 0 5.62 6.61
1 0 0 7.69 8.58
2 0 0 11.14 12.66

3 8
Section 4. The Self-organizing Map
The self-organizing maps are instances of so-called "competitive
neural networks," which are used by many unsupervised learning
systems to cluster or classify data. As opposed to supervised models,
where a desired response is externally provided to the network, t h e
unsupervised systems must find useful correlations in the input data
by themselves. Hence, they are said to be "self-organizing." They
self-organize in an adaptive way, by gradually adjusting the weights
on their connections (as opposed to the Hopfield model, where t h e
connection weights are fixed).
Conceptually, this model is closely related to the elastic net. I n
both cases, a ring is defined and gradually modified until it passes
sufficiently near each city to define a tour. However, the two
approaches differ in the way they update the coordinates of t h e
points on the ring.
In Section 4.1, we first describe the competitive networks in
general terms. Then, we introduce the self-organizing map a n d
describe its application to the TSP.
4.1 Competitive Networks
A typical two-layer competitive network with two input units
and M output units is depicted in Figure 8. Since we are considering
two-dimensional TSPs in this paper, we have shown only two input
units. However, there can be a large number I of inputs. In Figure 8,
T1M and T2M denote the weights on the connections from the two
input units to output unit M, and TM = (T1M, T2M) is the weight
vector of unit M.
The aim of the network is to group a set of I-dimensional input
patterns into K clusters (K ≤ M). The classes or clusters are not known
in advance and, consequently, the network must discover by itself
structure in the input data. At the end of the self-organization
process, each cluster is associated with a distinct output unit. More
precisely, each output unit gets activated only for a particular subset
of input patterns (defining a cluster).

3 9
Input Output
X1
X2
O1
O2
O
T2M
M
T1M
TM T2M= ( ),T1M
Fig. 8. A two-layer network.
The model is quite simple and works in the following way. After
initializing the network with small random connection weights Tij,
the input patterns are presented sequentially to the network. For
each presentation of a given I-dimensional pattern X, the network
initiates the competition among the output units, and the winning
unit j* is the one which maximizes its net input, namely
netj* = maxj netj, j=1,...,M
where netj = Σi TijXi .
The winning unit thus associates the current input pattern to
cluster j* and updates its weight vector Tj*= {Tij*}i=1,...,I, so as to get
closer to that input pattern. The update rule is
Tj* new = Tj* old + µ (X - Tj* old) ,

4 0
where µ is a fractional learning rate. Typically, the other output units
do not modify their weight vectors.
In order to get a stable classification, the whole set of input
patterns must be presented many times to the network (referred to
as "passes" or "cycles" through the set of inputs). As the network
evolves, the weight vectors of the output units move in the I -
dimensional space and tend to settle after a while into distinct
regions of that space. When this phenomenon occurs, the winning
output unit associated with each input pattern does not change from
one pass through the data to another and the clustering process
stabilizes. The final weight vector of each unit is then interpreted a s
the prototype vector of a cluster (that is, the centroid or "average
pattern" of the cluster).
4.2 The Self-organizing Map
A competitive network groups the inputs on the basis of
similarity, namely via the correlation between the input patterns X
and the weight vectors Tj. In the TSP context, the input patterns a r e
the two-dimensional coordinates of the cities, and consequently, t h e
Euclidean distance must be used as the similarity measure
(maximizing the inner product is equivalent to minimizing t h e
Euclidean distance only when all input and weight vectors a r e
normalized to the same length). Moreover, the objective is now to
map these coordinates onto a ring of output units, so as to define a n
ordering of the cities.
Those observations suggest the use of Kohonen's self-organizing
map.[62] This model is aimed at generating mappings from higher to
lower dimensional spaces, so that the relationships among the inputs
are reflected as faithfully as possible in the outputs (topological
mapping). As a consequence, topological relationships are established
among the output units, and those relationships are respected when
the weights are updated. Namely, the output units that are "close" to
the winning unit, according to the topology, also update their weight
vectors so as to move in the same direction as the winning unit.
In the TSP context, a set of two-dimensional coordinates, locating
the cities in Euclidean space, must be mapped onto a set of one
dimensional positions in the tour, so as to preserve the neighborhood
relationships among the cities. For example, the competitive network
depicted in Figure 8 can be transformed into a self-organizing map,

4 1
by choosing M ≥ N and by linking the output units to form the ring
O1-O2-...-OM-O1 (each output unit has two immediate neighbors).
The evolution of the self-organizing map is quite similar to t h e
evolution of the competitive network of Section 4.1. However, there
are two main differences. First, the winning output unit is the one
whose weight vector is closest in Euclidean distance to the vector of
coordinates of the current city (current input pattern). Second, t h e
neighbors of the winning unit also move their weight vector in t h e
same direction as the winning unit, but with decreasing intensity
along the ring.
As a consequence, the winning unit j* satisfies
dX,Tj* = minj dX,Tj , j=1,...,M ,
and the connection weights are then updated according to t h e
formula
Tj new = Tj old + µ G(d'jj*) (X - Tjold) , (4.1)
where dX,Tj is the Euclidean distance between input vector X a n d
weight vector Tj, and G is a function of the "proximity" of output
units j and j* on the ring. Usually, the units on the ring are indexed
from 1 to M, and d'jj* is based on that indexing, so that
d'jj* = min (|j - j*|, M- |j - j*|) ,
where |x| denotes absolute value. In particular, the distance between
a given unit and its two immediate neighbors on the ring is one.
Obviously, the value of G must decrease as the d'jj* value
increases. Moreover, the modifications to the weight vectors of t h e
neighboring units should ideally be reduced over time. As a n
example, the function G suggested in [21] is
G = (1 - d'jj*/L)β , if d'jj* < L
= 0 , otherwise ,
where L= N/2, and the value of β is increased after each complete
pass through the set of coordinates. Note that G is always equal to
one for the winning unit j*, and the value decreases for the other
units j as d'jj* and β increase.

4 2
Multiple passes through the entire set of coordinates a r e
performed until there is a weight vector sufficiently close to each
city. Figure 6 is still a good way to visualize the evolution of the self-
organizing map. However, the little black circles on the ring now
represent the output units, and the location of each output unit Oj is
determined by its weight vector Tj.
Different self-organizing maps are described in the literature
based on that scheme.
(a) Fort[36] was one of the first with Angeniol et al.,[9] Hueter,[52]
and Ritter and Schulten[85] to apply Kohonen's ideas to the TSP. Fort
notes, in particular, that the speed of convergence can be increased
by reducing the neighborhood of the winning unit and by reducing
the modification to the weights of the neighboring units over time. I n
his experiments, Fort uses 2N output units and solves problems with
up to 400 cities. With respect to the 400-city problem, he generates a
tour of length 15.73, as compared to an estimated optimum of 14.98
derived from Stein's formula[94].
(b) The work of Angeniol et al.[9] is based on the distinctive feature
that units in the ring are dynamically created and deleted. A unit is
duplicated if it wins for two different cities after a complete pass
through the set of coordinates. It is deleted, if it does not win after
three complete passes. Starting with a single unit in the ring, t h e
authors report that up to twice as many units as cities may b e
created during the procedure.
In their weight update equations, there is no learning rate
parameter µ (it is implicitly set to one), but there is an additional
parameter K in the definition of the function G, namely
G(d'jj*, K) = 1/√2 (e-d'jj*
2/K2
) .
When K→ , all units move towards the current city with
strength 1/√2. On the other hand, when K→ 0 only the winning unit j*
moves toward the city. Consequently, the K value is decreased after
each pass through the set of coordinates according to the formula
K ← (1- α) K ,
with 0 < α < 1.

4 3
The authors report good results on the five sets of 50-city
problems of Durbin and Willshaw.[32] Their results also show that a
single run of the elastic net algorithm is superior to a single run of
the self-organizing map (see Table 4). The same kind of results a r e
reported in [21].
City Average Tour Length
Sets
EN SA SA+3 SOM SOM+
1 5.98 5.88 5.84 6.06 5.84
2 6.09 6.01 5.99 6.25 6.00
3 5.70 5.65 5.57 5.83 5.58
4 5.86 5.81 5.70 5.87 5.60
5 6.49 6.33 6.17 6.70 6.19
EN Elastic Net
SA+3 Best solution found by SA and many distinct runs of 3-opt starting
from randomly generated tours
SOM Self-organizing Map (α = 0.2)
SOM+ Best solution found by SOM over 4,000 different runs
(by processing the cities in various orders)
Table 4. Comparison of Results for Five Solution Procedures
on Five Sets of 50-city Problems
The authors also solved a randomly generated 1,000-city ETSP
with their model. It took 20 minutes on a "classical sequential
machine" to solve the problem with α set to 0.2. Slightly better
solutions were found by setting α to 0.02, but the computation time
increased to 12 hours. Unfortunately, no comparisons are provided
with other problem-solving heuristics for the 1,000-city problem.
Fritzke and Wilke[37] describe a similar approach where units in
the ring are dynamically created. They also introduce various
simplifications in order to reduce complexity (e.g., the neighborhood
of the winning unit only includes its two immediate neighbors). The
authors were able to solve nine problems taken from the OR
literature ranging in size from 8 to 2,392 cities. Since the optimum

4 4
for each one of these problems is known, they observed that their
tours were never more than 10% longer than the optimum. However,
the difference increases steadily from the 8-city problem (3%) to t h e
2,392-city problem (10%). It took about three hours to solve t h e
largest problem on a SPARC 2 workstation.
The authors also observe that better solutions are produced b y
constructing a tour with the nearest neighbor heuristic and b y
applying the 2-opt exchange heuristic to this initial tour. On t h e
2,392-city problem, the solution obtained was about 6% larger than
the optimum.
(c) Favata and Walker[34] add a third "normalizing" dimension to
the coordinates of the cities, so that all the input vectors have t h e
same length. In their experiments, the authors note that it sometimes
occurs that two or more cities are mapped to the same unit in t h e
ring. They interpret that phenomenon as meaning that the network
does not care about the local ordering of those cities in the solution.
For small problems, the use of a local optimization procedure for
defining a complete ordering slightly improved the solutions. On
larger problems, however, an arbitrary insertion proved to b e
equally good.
Favata and Walker were able to solve problems with up to
10,000 cities with their approach. On randomly generated 30-city
ETSPs, their solutions are about 5% longer than the solutions
produced by the simulated annealing heuristic. On a 1,000-city
problem, they got a tour of length 26.82 after half an hour of
computation time on a VAX 3600, as compared to a length of 25.95
with the simulated annealing heuristic after one hour of computation.
They report that their approach is 10 times faster than simulated
annealing on the 10,000-city problem, but no additional details a r e
provided.
(d) In [20, 21], the authors use the "conscience" mechanism[30] to
solve the problem related to the mapping of multiple cities to t h e
same unit in the ring. Basically, the conscience mechanism inhibits
units that are winning too often, so that other units also get a chance
to win. The conscience mechanism avoids the artificial
"deletion/duplication" step of the procedure described in [9], and t h e
number of units on the ring is fixed to the number of cities N

4 5
throughout the algorithm. The computational results show that their
approach (called the "guilty net") is better than the Hopfield-Tank
model, but is outperformed by the elastic net on four sets of
randomly generated problems ranging in size from 10 to 100 cities
(see Table 5). The authors also note that their model converges much
faster than the elastic net (500 iterations on the 100-city problems,
as compared to 7,000 for the elastic net).
a Optimum estimated via Stein's formula[94]
EN Elastic Net
GN Guilty Net
HT Hopfield-Tank
NA Not Available
Table 5. Comparison of Results for Five Solution Procedures
on Random Problems
(e) Matsuyama[71] adds a new term, previously introduced in
Durbin and Willshaw,[32] to the weight update equations (4.1)
Tjnew = Tjold + µ G(d'jj*) (X - Tjold) + β (Tj+1 + Tj-1 - 2Tj) .
As previously noted in the description of the elastic net algorithm,
the last term is aimed at shortening the length of the ring. It acts in
synergy with the second term to allow the neighbors of the winning
unit to move in the same direction as the winning unit.
Number of
Average Tour Length
Cities EN GN HT SA Optimuma
1 0 2.45 2.78 2.92 2.74 2.43
3 0 4.51 4.81 5.12 4.75 4.21
5 0 5.58 6.21 6.64 6.13 5.43
1 0 0 8.23 8.81 NA 8.40 7.68

4 6
Table 6 summarizes the main results with respect to the self-
organizing map.

4 7
Paper Largest TSP
(Number of
Cities)
Length of Tour
from
Self-organizing
Map
TSP
Heuristic
Length of
Tour from
TSP
Heuristic
Fort
(1988)
5 0
4 0 0
6.035
15.73
SA
NA
5.735
NA
Angeniol
et al.
(1988)
1,000 18,036-18,800a NA NA
Fritzke and
Wilke
(1991)
2,392 NAb NN+TO NA
Favata and
Walker
(1991)
1,000
10,000
26.82c
NA
SA
NA
25.95
NA
Burke and
Damany
(1992)
1 0 0 8.81 SA
EN
8.40
8.23
a Computation time of 20 minutes to 12 hours on a sequential computer (not
identified)
b Computation time of 3 hours on a SPARC 2 workstation
c Computation time of 30 minutes on a VAX 3600
EN Elastic Net
NA Not Available
NN+TO Nearest Neighbor + 2-opt
Table 6. Comparison of Results Produced by Self-Organizing Maps
and TSP Heuristics
As a final remark, it is interesting to note that Van den Bout a n d
Miller[99] describe a way to implement Kohonen's self-organizing
map in hardware with VLSI components (i.e., a neural chip).

4 8
Assuming a clock frequency of 10 MHz, they report that their neural
chip could process an input vector each 6.9 microseconds, regardless
of the number of units in the network.
Section 5. Concluding Remarks
In summary, the self-organizing map and the elastic net can
now find good solutions to very large ETSPs with up to 10,000 cities.
Both approaches outperform the Hopfield-Tank network, which is
restricted to relatively small problems with 200 cities or less (but
can be applied as well to problems with no geometric interpretation).
The results in the literature also indicate that the elastic net is better
than the self-organizing map with respect to solution quality.[9,21]
On the other hand, the gap between the elastic net or the self-
organizing map and the best heuristics of OR is still quite large. For
example, the study of Fritzke and Wilke[37] shows that the tours
generated by their self-organizing map are 5% to 10% longer than the
optimal tours for ETSPs with 500 to 2,392 cities. These results can
hardly be compared with the tours generated by the heuristics of
Johnson and Bentley.[12,53] In the first case, solutions within 1% of
the optimum are reported for problems with 10,000 cities, while in
the second case, solutions within 4% of the optimum are reported on
problems with 1,000,000 cities.
However, the rapid evolution of neural network technology can
hardly be overlooked. Just eight years ago, Hopfield and Tank were
struggling on a 10-city problem! The aim of this paper is to inform
the OR community about the rapid progress made in the neural
network field, and to demonstrate its potential for new research
developments, in particular, via harmonious integration of OR a n d
neural network technologies. As an example, Wacholder and his
colleagues[100,101] used the well-known equivalence between the TSP
and the multiple traveling salesman problem[11] to design Hopfield-
Tank models for the latter problem.
Recently, various extensions to the TSP have been considered b y
neural network researchers. In [33, 42, 72, 73], multiple rings of
output units are used to solve the multiple traveling salesman
problem and the vehicle routing problem with the self-organizing
map. In [89], an extension of the Hopfield-Tank model for dynamic
TSPs is described. In these problems, the traveling costs change
according to the time of the day. Finally, in [82], a neural network is

4 9
trained to perform a vehicle dispatching task. The network learned
to weight various criteria used by human experts to evaluate
dispatching situations and to assign service requests to vehicles. This
application is well suited to neural network technology, because
many subjective assessments are made by the dispatcher, and neural
networks are known to be particularly useful for such ill-defined
problems.
Acknowledgments. I would like to thank Professor Guy Lapalme and t h r e e
anonymous referees for their very useful comments. Thanks also to Dr. Bruce
L. Golden who gave to me some interesting papers on the subject. Finally, t h i s
research would not have been possible without the financial support of t h e
Natural Sciences and Engineering Council of Canada (NSERC) and the Fonds
pour la Formation de Chercheurs et l’Aide a la Recherche of Quebec
Government (FCAR).
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