Swarm intelligance (1)


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Swarm intelligance (1)

  1. 1. Scientific Research Group in Egypt (SRGE) Swarm Intelligence (I) Particle swarm optimization Dr. Ahmed Fouad Ali Suez Canal University, Dept. of Computer Science, Faculty of Computers and informatics Member of the Scientific Research Group in Egypt Company LOGO
  2. 2. Company LOGO Scientific Research Group in Egypt www.egyptscience.net
  3. 3. Company LOGO Meta-heuristics techniques
  4. 4. Company LOGO 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
  5. 5. Company LOGO 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.
  6. 6. Company LOGO 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).
  7. 7. Company LOGO 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.
  8. 8. Company LOGO 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
  9. 9. Company LOGO 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
  10. 10. Company LOGO 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
  11. 11. Company LOGO • 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
  12. 12. Company LOGO 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)
  13. 13. Company LOGO 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; For each particle p in P do v = v + c1*rand*(pBest – p) + c2*rand*(gBest – p); p = p + v; end end
  14. 14. Company LOGO 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
  15. 15. Company LOGO PSO Algorithm (parameter setting) •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.
  16. 16. Company LOGO Advantage / disadvantage Advantage Disadvantage Simple implementation Slow convergence in refined search stage Few parameters to adjust Efficient in global search Weak local search ability
  17. 17. Company LOGO 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.
  18. 18. Company LOGO 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
  19. 19. Company LOGO 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
  20. 20. Company LOGO Thank you Ahmed_fouad@ci.suez.edu.eg http://www.egyptscience.net