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Particle Swarm Optimization and Social Interaction Between Agents. Kenneth Lee TJHSST 2008. Overview of PSO Background Research Project Goals Types of Social Interactions Project State/Results Conclusion. Overview. Overview Of PSO. Originally designed to model birds
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Particle Swarm Optimization and Social Interaction Between Agents Kenneth Lee TJHSST 2008
Overview of PSO Background Research Project Goals Types of Social Interactions Project State/Results Conclusion Overview
Overview Of PSO • Originally designed to model birds • Overtime became more analogous to a swarming animal (bees) • Search for Global Optima • Infinite search spaces
Overview Of PSO • “Particles” (vectors) • Random Position • Random Velocity • Influences on Velocity • Cognitive Influence • Social Influence • Convergence(?)
for k = 1 to number of particles n do if (fitness(k) < fitness_lbest(k)) lbest(k) = pos(k) endif end do for k = 1 to number of particles n do social(k) enddo for k = 1 to number of particles n do for I = 1 to number of dimensions d do R1 = randomNumber R2 = randomNumber V[k][I] = w * (C1 * R1 * (pos-lbest) + C2 * R2 * (pos-gbest)) X[k][I] = pos + V[k][I] enddo enddo Particle Swarm Optimization Determining lbest Social Interaction Adjusting Position
Importance of Social Interaction • Influences Velocity • V = ??? • X’ = X + V • Encourages Exploration • Through Social Interaction, Particles are able to communicate information and extrapolate data about the objective function.
Social Interactions • Variance of k value (# of neighbors) • Through research k values between 3-5 seem to work best • Topology? • Cliques • Random • Share/Follow
Project State • 5 Interactions • NIPS • SIPS • RIPS • FIPS • DIPS • 3 Benchmark Functions • Rastrigin, Six Camel Hump, Sphere
Conclusions • DIPS seems to perform best • Time only • DIPS and RIPS have 100% success rate • FIPS converges fastest