A Simplified Recombinant PSO

Dan Bratton,Tim Blackwell

doi:10.1155/2008/654184

Abstract

Simplified forms of the particle swarm algorithm are very beneficial in contributing to understanding how a particle swarm optimization (PSO) swarm functions. One of these forms, PSO with discrete recombination, is extended and analyzed, demonstrating not just improvements in performance relative to a standard PSO algorithm, but also significantly different behavior, namely, a reduction in bursting patterns due to the removal of stochastic components from the update equations.

Highlights

Conceived as a modification to the standard particle swarm optimization (PSO) algorithm for use on self-reconfigurable adaptive systems used in on-chip hardware processes, PSO with discrete recombination (PSO-DR) introduces several appealing and effective modifications, resulting in a simpler variant of the original [1]
To avoid the problem of the probabilistic nature of t-tests potentially affecting results when conducting multiple significance tests, a modified Bonferroni procedure was applied to values of α for successive tests [10]. This procedure involves inversely ranking observations by ascending values of p, setting α=. Results for these statistical tests on PSO-DR model 3 and SPSO are shown in Table 4 and confirm that the performance is significantly improved on 3 of the 14 tested functions, equivalent for 10 functions, and worsened for 1 function for PSO-DR model 3 versus SPSO with ring topology
For the two problems on which SPSO outperformed PSO-DR model 3 ( f2, f3), the same early performance is seen with PSO-DR model 3 surpassing SPSO in performance early in the optimization process; in these cases, SPSO eventually repasses the other algorithm by 50 k function evaluations

Summary

INTRODUCTION

Conceived as a modification to the standard PSO algorithm for use on self-reconfigurable adaptive systems used in on-chip hardware processes, PSO with discrete recombination (PSO-DR) introduces several appealing and effective modifications, resulting in a simpler variant of the original [1]. It is one of the more interesting advances in PSO research over the last few years because these simplifications apparently do not degrade performance yet they remove various issues associated with the stochasticity of the PSO acceleration parameters that hinder theoretical analysis of PSO. The final section together draws together the experimental results of this paper and advances some ideas for the immediate future of PSO research

PSO WITH DISCRETE RECOMBINATION

SIMPLIFYING RECOMBINANT PSO

PERFORMANCE EXPERIMENTS

EXAMINATION OF BURSTING

CONCLUSIONS