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

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

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(?)‏

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

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

NIPS (Non-Informed Particle Swarm)‏

SIPS (Singly-Informed Particle Swarm)‏

RIPS (Ring Informed Particle Swarm)‏

FIPS (Fully Informed Particle Swarm)‏

DIPS (Dynamically Informed Particle Swarm)‏

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