Transcript Particle Swarm Optimization - Department of Computer

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 (9h to 3min)
• 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