Particle Swarm Optimization James Kennedy Russel C Eberhart

Particle Swarm Optimization James Kennedy & Russel C. Eberhart

Idea Originator • Landing of Bird Flocks • Function Optimization • Thinking is Social • Collisions are allowed

Simple Model • Swarm of Particles • Position in Solution Space • New Position by Random Steps • Direction towards current Optimum • Multi-Dimensional Functions

First Feedbacks • Fast in Uni-Modal Functions • Neuronal-Network Training (9 h to 3 min) • Able to compete with GA (overhead) • But, Algorithm is based on Broadcasting • Multi-modal Function Optimization

Algorithm Updates • Storage of individual Best [Kennedy] • Move between individual & global Best • Constriction Factor [Shi&Eberhart] • Tracking Changing Extreme [Carlisle]

Hybrid PSO • Breed & Sub-population • Combine Adv. of PSO & EA • Anal. comparison PSO vs. GA [Angeline] • Idea: Increase Diversification

Hybrid Approach - Breeding • Steps Select Breeding Population (pb – prob. ) Select two random Parents Replace Parents by Offspring • Offspring Creation arithmetic crossover for position & velocity

Hybrid Approach – Sub-Popul. • Steps Divide into multiple Subpopul. Spread particles over solution space Use Breeding approach • Sub-Popul. Selection Breeding over diff. Poul. (psb – prob. )

Hyb. Results • Usage of 4 multi-dim. Functions • In uni-modal function GA & std. PSO better • In multi-modal function hyp. PSO better convergence & solution • Subpopulation results in no gains

Conclusion • New Research Area First PSO in 1995, First Conf. Last Year • Highly accepted Increasing Research & Evol. Comp. Special • Can we learn from GA & PSO a improved method with reduced overhead?

Reading Room • “Swarm Intelligence” by Kennedy & Eberhart [2001] • Bibliography www. computelligence. org/pso/bibliography. htm