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Particle Swarm Optimization: Swarm Intelligence for Global Search

This research project delves into Particle Swarm Optimization (PSO), inspired by bird flocking and swarm behavior in animals like bees. it explores how PSO uses social interactions and randomized movement to find global optima in complex search spaces. The study evaluates various social influence mechanisms, particle interactions, and benchmarks different PSO variants. Conclusions highlight the efficacy of Dynamically-Informed Particle Swarm in achieving optimal results.

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Particle Swarm Optimization: Swarm Intelligence for Global Search

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  1. Particle Swarm Optimization and Social Interaction Between Agents Kenneth Lee TJHSST 2008

  2. Overview of PSO Background Research Project Goals Types of Social Interactions Project State/Results Conclusion Overview

  3. Overview Of PSO • Originally designed to model birds • Overtime became more analogous to a swarming animal (bees)‏ • Search for Global Optima • Infinite search spaces

  4. Overview Of PSO • “Particles” (vectors)‏ • Random Position • Random Velocity • Influences on Velocity • Cognitive Influence • Social Influence • Convergence(?)‏

  5. 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

  6. 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.

  7. Social Interactions • Variance of k value (# of neighbors)‏ • Through research k values between 3-5 seem to work best • Topology? • Cliques • Random • Share/Follow

  8. Project State • 5 Interactions • NIPS • SIPS • RIPS • FIPS • DIPS • 3 Benchmark Functions • Rastrigin, Six Camel Hump, Sphere

  9. NIPS (Non-Informed Particle Swarm)‏

  10. SIPS(Singly-Informed Particle Swarm)‏

  11. RIPS(Ring Informed Particle Swarm)‏

  12. FIPS(Fully Informed Particle Swarm)‏

  13. DIPS(Dynamically Informed Particle Swarm)‏

  14. Conclusions • DIPS seems to perform best • Time only • DIPS and RIPS have 100% success rate • FIPS converges fastest

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