A Particle Swarm Optimization (PSO) Primer

A Particle Swarm Optimization (PSO) Primer - A Particle...

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A Particle Swarm Optimization (PSO) Primer With Applications Brian Birge
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Overview p Introduction p Theory p Applications p Computational Intelligence p Summary
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Introduction p Subset of Evolutionary Computation n Genetic Algorithms n Evolutionary Programming n Evolution Strategies n Genetic Programming p Behaviorally Inspired n Developed out of attempts to model bird flocks and fish schools p Emergence n Complex behavior from simple rules
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Theory - Qualitative Details p PSO Algorithm in a nutshell p Searches Hyperspace of Problem for Optimum n Define problem to search p How many dimensions? p Solution criteria? n Initialize Population p Random initial positions p Random initial velocities n Determine Global Best Position n Determine Personal Best Position n Update Velocity and Position Equations
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Theory - Equations p The Basic PSO algorithm consists of the velocity: p ..and position: p i – particle index p k – discrete time index p v – velocity of ith particle p x – position of ith particle p p – best position found by ith particle (personal best) p G – best position found by swarm (global best, best of personal bests) p γ 1,2 – random numbers on the interval [0,1] applied to ith particle ( 29 ( 29 ( 29 ( 29 ( 29 ( 29 12 1 i i i i i ii v k v k p x k G xk gg + =+- +- ( 29 ( 29 ( 29 11 i x k x k vk + = ++
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Theory - Equations . .
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This note was uploaded on 05/15/2010 for the course NETWORKING optimaizat taught by Professor Eamir during the Spring '10 term at University of Advancing Technology.

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A Particle Swarm Optimization (PSO) Primer - A Particle...

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