ABC Algorithm.

Department of Mechanical Engineering
Zeal Education Society’s
Dnyanganga College Of Engineering and Research, Pune 41.
[2014-15]
A Seminar I On
“Artificial Bee Colony Algorithm”
By
Mr. Nayak V. R.
(Exam Seat No. 10669)
Guide
Dr. Kakandikar G. M.

Introduction
Nature inspired Algorithm
Artificial Bee Colony (ABC) Algorithm
Bee Behaviour
ABC Algorithm
Pseudo Code, Steps and Flowchart
Advantages
Limitations
Applications
Summary
References

Optimization is the art and science of allocating scarce
resources to the best possible effect.
Moving from world problem to the algorithm, model, or
solution techniques known as the real analysis.
Real world problem
Algorithm, Model, Solution
Technique
Computer Implementation
Analysis
Numerical Method
Validation
Verification

These algorithm techniques are mostly inspired
from nature and apply nature like processes to
solutions. Various nature inspired algorithms as
follows:
Nature
Inspired
Algorith
m
Firefly
Algorithm
Bat
Algorithm
Bumble
Bee
Algorithm
Cuckoo
Search
Algorithm
Genetic
Algorithm
Artificial
Bee Colony
Artificial
Fish
School
Fruit fly
Algorithm

Artificial Bee Colony (ABC) is one of the most recently
defined algorithms by Dervis Karaboga in 2005, motivated
by the intelligent behaviour of honey bees. ABC as an
optimization tool provides a population-based search
procedure in which individuals called foods positions are
modified by the artificial bees with time and the bee’s aim
is to discover the places of food sources with high nectar
amount and finally the one with the highest nectar.

Waggle dance to give successful foragers can share, with
other members of the colony.
the direction and distance to patches of flowers yielding
nectar.

Bee system consists of two essential components
1) Food Sources
2) Foragers
a) Unemployed foragers:
b) Employed foragers
c) Experienced foragers

The ABC consists of four main phases:
Initialization Phase
The initial food sources are randomly produced via the expression
xm = li + rand (0,1)*(ui - li) …………. (i)
Where ui and li are the upper & lower bound of the solution space of
objective function, rand (0, 1) is a random number within the range
[0, 1].
Employed Bee Phase
The neighbour food source vmi is determined and calculated by the
following equation.
vmi = xmi + ϕmi (xmi- xki)………. (ii)
Where i is a randomly selected parameter index, xk is a randomly
selected food source, ϕmi is a random number within the range [-1,
1]. The fitness is calculated by the following formula (3), after that a
greedy selection is applied between xm and vm.
fitm (xm )= and fitm (xm )= .… (iii)
Where, fm (xm) is the objective function value of xm.

Onlooker Bee Phase
The quantity of a food source is evaluated by its profitability
and the profitability of all food sources. Pm is determined by
the formula
Pm = …….(iv)
Where, fit m ( xm ) is the fitness of xm. Onlooker bees search
the neighborhoods of food source according to the
expression.
vmi = xmi + ϕmi (xmi- xki) ..……… (v)
Scout Phase
The new solutions are randomly searched by the scout bees.
The new solution xm will be discovered by the scout by using
the expression.
xm = li + rand (0,1)*(ui - li) …..……. (vi)
Where, rand (0, 1) is a random number within the range
[0,1], ui and li are the upper and lower bound of the solution
space of objective function.

1) Local training samples
2) Generate the initial population xi=1,2,3,…SN
3) Evaluate the Fitness (fi)of population
4) Set cycle to 1
5) Repeat
6) FOR each employed bee
{Produce new solution vi by using (6)
Calculate the value of fi
Apply greedy selection process}
7) Calculate the probability values Pi for the solution (xi) by (5)
8) FOR each onlooker bee
{Select a solution xi depending on Pi
Produce new solution vi
Calculate the value of fi
Apply greedy selection process}
9) If there is abandoned solution for the scout
Then replace it with new solution
which will be randomly produce by (7)
10) Memorise the best solution so far
11) Cycle = cycle + 1
12) Until cycle = M N C

1) Begin
2) Initialization the solution population xm , i = 1,2,….., SN
3) Evaluate Population
4) Cycle = 1
5) Repeat
6) General new solutions vmi for the employed bees using (ii)
and evaluate them.
7) Keep the best solution between current and candidate.
8) Select the visible solution for onlooker bees using (ii) and
evaluate them.
9) General new solutions vmi for the onlooker bees using (ii)
and evaluate them.
10) Keep the best solution between current and candidate.
11) Determine if exist an abandoned food source and replace it
using a scout bee.
12) Save in memory the best solution so for.
13) cycle = cycle +1
14) Until cycle = M N C (Maximum no of cycles)

Simplicity, flexibility and robustness
Ability to explore local solutions
Ease of implementation
Ability to handle the objective cost
Population of solutions
High flexibility, which allows adjustments
Broad applicability, even in complex functions

Lack of use of secondary information
Requires new fitness tests on the new
algorithm parameters
The possibility of losing relevant
information
High number of objective function
evaluations
Slow down when used in sequential
processing
The population of solutions increases the
computational cost

Benchmarking Optimization
Bioinformatics application
Scheduling Applications
Clustering and Mining Applications
Image processing Applications
Economic Dispatch Problems
Engineering Designs and Applications

In this report, the concept, classification and various
techniques of optimization are discussed. The ABC
optimization algorithm, working principle, stages, flow chart
and its application areas are presented. The Advantages and
disadvantages are also mentioned. This report shows
importance of using ABC as its having wide number of
advantages with applications.

Books
1) Lorenz T. Biegler, Nonlinear Programming: Concepts, Algorithms, and Applications
to Chemical Processes
2) A. Astolfi, OPTIMIZATION An introduction
Research Papers
1) Dervis Karaboga · Bahriye Basturk, A powerful and efficient algorithm for numerical
function optimization: artificial bee colony (ABC) algorithm J Glob Optim (2007)
39:459–471
2) Dervis Karaboga , Bahriye Akay, A comparative study of Artificial Bee Colony
algorithm, Applied Mathematics and Computation 214 (2009) 108–132
3) Dr. Dharmender Kumar, Balwant Kumar, Optimization of Benchmark Functions
Using Artificial Bee Colony (ABC) Algorithm, IOSR Journal of Engineering (IOSRJEN),
Vol. 3, Issue 10 (October. 2013), ||V4|| PP 09-14
4) Asaju La’aro Bolaji, Ahamad Tajudin Khader, Mohammed Azmi Al-betar And
Mohammed A. Awadallah, Artificial Bee Colony Algorithm, Its Variants And
Applications: A Survey, Journal of Theoretical and Applied Information Technology
20th January 2013. Vol. 47 No.2
5) Ali Hadidi, Sina Kazemzadeh Azad, Saeid Kazemzadeh Azad, Structural optimization
using artificial bee colony algorithm, 2nd International Conference on Engineering
Optimization, September 6 - 9, 2010, Lisbon, Portugal
6) Chin Soon Chong, Malcolm Yoke Hean Low, Appa Iyer Sivakumar, Kheng Leng Gay,
USING A BEE COLONY ALGORITHM FOR NEIGHBORHOOD SEARCH IN JOB SHOP
SCHEDULING PROBLEMS
7) Ž. Kanović, V. Bugarski, T. Bačkalić: Ship Lock Control System Optimization using GA,
PSO and ABC: A Comparative Review, Promet – Traffic&Transportation, Vol. 26, 2014,
No. 1, 23-31
Website:
1) http://mf.erciyes.edu.tr/abc/

ABC Algorithm.

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