Scientific Research Group in Egypt (SRGE) Swarm Intelligence (I) Particle swarm optimization Scientific Research Group in Egypt (SRGE) Dr. Ahmed Fouad Ali Suez Canal University, Dept. of Computer Science, Faculty of Computers and informatics Member of the Scientific Research Group in Egypt

Scientific Research Group in Egypt www.egyptscience.net

Meta-heuristics techniques

Outline 1. Swarm intelligence (Main idea) 2. History of Particle swarm optimization 3. Particle swarm optimization (PSO) 4. PSO Algorithm 5. Advantage / disadvantage 6. Comparison with Genetic algorithm 7. References

Swarm intelligence (Main Idea) Suppose you and a group of friends are on a treasure finding mission. Each one in the group has a metal detector and can communicate the signal and current position to the n nearest neighbors. Each person therefore knows whether one of his neighbors is nearer to the treasure than him. If this is the case, you can move closer to that neighbor. In doing so, your chances are improved to find the treasure. Also, the treasure may be found more quickly than if you were on your own.

Swarm intelligence (Main Idea) A swarm can be defined as a structured collection of interacting organisms (or agents). Within the computational study of swarm intelligence, individual organisms have included ants, bees, wasps, termites, fish (in schools) and birds (in flocks).

Swarm intelligence (Main Idea) The global behavior of a swarm of social organisms therefore emerges in a nonlinear manner from the behavior of the individuals in that swarm The interaction among individuals plays a vital role in shaping the swarm's behavior. Interaction among individuals aids in refining experiential knowledge about the environment, and enhances the progress of the swarm toward optimality.

History of particle swarm optimization Proposed by James Kennedy & Russell Eberhart in 1995 Inspired by simulation social behavior Related to bird flocking, fish schooling and swarming theory - steer toward the center - match neighbors’ velocity - avoid collisions Combines self-experience with social experience Population-based optimization

Particle swarm optimization (concepts) Set of agents (particles) that constitute a swarm moving around in the search space looking for the best solution Each particle in search space adjusts its “flying” according to its own flying experience as well as the flying experience of other particles

Particle swarm optimization (concepts) Movement towards a promising area to get the global optimum Each particle keeps track: Its best solution, personal best, pbest The best value of any particle, global best, gbest

Particle swarm optimization (concepts) Each particle modifies its position according to: its current position its current velocity the distance between its current position and pbest the distance between its current position and gbest

Particle swarm optimization (concepts) Swarm: a set of particles (S) Particle: a potential solution Position, Velocity: Each particle maintains Individual best position (PBest) Swarm maintains its global best (GBest)

Particle swarm optimization Algorithm P = Particle_Initialization(); For i=1 to it_max For each particle p in P do fp = f(p); If fp is better than f(pBest)             pBest = p; end end gBest = best p in P; v = v + c1*rand*(pBest – p) + c2*rand*(gBest – p); p = p + v; end end

Particle swarm optimization Algorithm Personal influences Inertia vi(t+1) = vi (t)+ c1*rand*(pBest(t) – p(t)) + c2*rand*(gBest(t) – p(t)); Social influence Particle’s velocity

PSO Algorithm (parameter setting) The right way This way Or this way Number of particles (10—50) are reported as usually sufficient. C1 (importance of personal best) C2 (importance of neighborhood best) Usually C1+C2 = 4. Vmax – too low: too slow too high: too unstable.

Advantage / disadvantage Simple implementation Slow convergence in refined search stage Few parameters to adjust Weak local search ability Efficient in global search

Comparison with genetic algorithm (GA) Commonalities PSO and GA are both population based stochastic optimization Both algorithms start with a group of a randomly generated population, Both have fitness values to evaluate the population. Both update the population and search for the optimium with random techniques. Both systems do not guarantee success.

Comparison with genetic algorithm (GA) Differences PSO does not have genetic operators like crossover and mutation. Particles update themselves with the internal velocity. Particles do not die. The information sharing mechanism in PSO is significantly different There is no selection in PSO

References Computational Intelligence An Introduction Andries P. Engelbrecht, University of Pretoria South Africa Some slides adapted from a presentation “The Particle Swarm Optimization Algorithm” By Andry Pinto, Hugo Alves, Inês Domingues, Luís Rocha Susana Cruz. Particle Swarm Optimization http://www.particleswarm.info/ http://www.swarmintelligence.org

Thank you Ahmed_fouad@ci.suez.edu.eg http://www.egyptscience.net