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Particle Swarm Optimization and Social Interaction Between Agents Kenneth Lee TJHSST 2008
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Overview Overview of PSO Background Research Project Goals Types of Social Interactions Project State/Results Conclusion
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Overview Of PSO Originally designed to model birds Overtime became more analogous to a swarming animal (bees) Search for Global Optima Infinite search spaces
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Overview Of PSO “Particles” (vectors) Random Position Random Velocity Influences on Velocity Cognitive Influence Social Influence Convergence(?)
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Particle Swarm Optimization 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 Determining lbest Social Interaction Adjusting Position
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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.
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Social Interactions Variance of k value (# of neighbors) Through research k values between 3-5 seem to work best Topology? Cliques Random Share/Follow
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Project State 5 Interactions NIPS SIPS RIPS FIPS DIPS 3 Benchmark Functions Rastrigin, Six Camel Hump, Sphere
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NIPS (Non-Informed Particle Swarm)
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SIPS (Singly-Informed Particle Swarm)
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RIPS (Ring Informed Particle Swarm)
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FIPS (Fully Informed Particle Swarm)
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DIPS (Dynamically Informed Particle Swarm)
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Conclusions DIPS seems to perform best Time only DIPS and RIPS have 100% success rate FIPS converges fastest
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