Lecture 10 swarm intelligence
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Swarm intelligence is exemplified by collective behaviors in flocking birds, schooling fish, and insects, characterized by principles such as homogeneity, locality, collision avoidance, velocity matching, and flock centering. Various collective dynamical behaviors are identified, including milling, dynamic parallel groups, swarms, and highly parallel groups. Key algorithms in swarm intelligence include Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), both inspired by natural social behaviors.
Lecture 10 swarm intelligence
- 2. Introduction • Swarm behavior can be seen in bird flocks, fish schools, as well as in insects like mosquitoes and midges. • The main principles of the collective behavior are: • homogeneity: every bird in flock has the same behavior model. The flock moves without a leader, even though temporary leaders seem to appear. • locality: the motion of each bird is only influenced by its nearest flock mates. Vision is considered to be the most important senses for flock organization. • collision avoidance: avoid collision with nearby flock mates. • velocity matching: attempt to match velocity with nearby flock mates. • flock centering: attempt to stay close to nearby flock mates.
- 3. Collective dynamical behaviours • Torus: individuals perpetually rotate around an empty core (milling). The direction of rotation is random.
- 4. Collective dynamical behaviours • Dynamic parallel group: the individuals are polarized and move as a coherent group, but individuals can move throughout the group and density and group form can fluctuate.
- 5. Collective dynamical behaviours • Swarm : an aggregate with cohesion, but a low level of polarization (parallel alignment) among members
- 6. Collective dynamical behaviours • Highly parallel group: much more static in terms of exchange of spatial positions within the group than the dynamic parallel group and the variation in density and form is minimal.
- 7. The most popular algorithms in the swarm intelligence domain • Particle Swarm Optimization (PSO) is a population based stochastic optimization technique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by social behavior of bird flocking or fish schooling. PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). • Ant Colonies Optimization (ACO) the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs