1 / 11

Particle Swarm Optimization

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

reginald
Download Presentation

Particle Swarm Optimization

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Particle Swarm Optimization James Kennedy & Russel C. Eberhart

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

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

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

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

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

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

  8. 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.)

  9. 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

  10. 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?

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

More Related