•Introduction PSO
•Pitfalls of PSO
•Design Considerations(FlowChart)
•Recent PSO extensions
•Applications of PSO
•Multi-objective Job shop scheduling
•Multimode Resource Constrained Project Scheduling
•Summary
Kennedy, J. and Eberhart, R., “Particle Swarm Optimization,” Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia 1995, pp. 1942-1945.
PSO features
•The motivation of PSO algorithm was social behavior such as bird flocking, and fish schooling.
•PSO is very similar to GA, but it does not have genetic operators (crossover and mutation).
–A particle moves with the velocity:
–its own experience,
–experience from all particles.
•The idea is similar to bird flocks searching for food.
Properties of Particles
1.ability to exchange information with its neighbors
2.ability to memorize a previous position
3.ability to use information to make a decision.
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