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Particle swarm optimization (PSO) is attracting an ever-growing attention andmore than ever it has found many application areas for many challengingoptimization problems. It is, however, a known fact that PSO has a severedrawback in the update of its global best (gbest) particle, which has a crucialrole of guiding the rest of the swarm. In this paper, we propose threeefficient solutions to remedy this problem using the SPSA Algorithm. In thefirst approach, gbest is updated with respect to a global estimation of thegradient and can avoid getting trapped into a local optimum. The secondapproach is based on the formation of an alternative or artificial global bestparticle, the so-called aGB, which can replace the native gbest particle for abetter guidance, the decision of which is held by a fair competition betweenthe two. The third approach is based on the update of the swarm particle. Forthis purpose we use simultaneous perturbation stochastic approximation (SPSA)for its low cost. Since SPSA is applied only to the gbest (not to the entireswarm) or to the entire swarm, both approaches result thus in a negligibleoverhead cost for the entire PSO process. Both approaches are shown tosignificantly improve the performance of PSO over a wide range of non-linearfunctions, especially if SPSA and PSO parameters are well selected to fit theproblem at hand. As in the basic PSO application, experimental results showthat the proposed approaches significantly improved the quality of theOptimization process as measured by a statistic analysis.

Comparison study of the combination of the SPSA algorithm and the PSO algorithm