Particle swarm optimization
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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

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Particle Swarm Optimization

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Particle swarm optimization

Particle Swarm Optimization

James Kennedy & Russel C. Eberhart


Idea originator

Idea Originator

  • Landing of Bird Flocks

  • Function Optimization

  • Thinking is Social

  • Collisions are allowed


Simple model

Simple Model

  • Swarm of Particles

  • Position in Solution Space

  • New Position by Random Steps

  • Direction towards current Optimum

  • Multi-Dimensional Functions


First feedbacks

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

Algorithm Updates

  • Storage of individual Best [Kennedy]

  • Move between individual & global Best

  • Constriction Factor [Shi&Eberhart]

  • Tracking Changing Extreme [Carlisle]


Hybrid pso

Hybrid PSO

  • Breed & Sub-population

  • Combine Adv. of PSO & EA

  • Anal. comparison PSO vs. GA [Angeline]

  • Idea: Increase Diversification


Hybrid approach breeding

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

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

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

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

Reading Room

  • “Swarm Intelligence”

    by Kennedy & Eberhart [2001]

  • Bibliography

    www.computelligence.org/pso/bibliography.htm


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