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