Resampling in Particle Swarm Optimization

Abstract

Particle Swarm Optimization (PSO) is a population-based algorithm designed to find good solutions to optimization problems. Its characteristics have encouraged its adoption to tackle a variety of problems in different fields. However, when such problems are subject to noise, the performance of PSO suffers an immediate deterioration which demands the incorporation of noise handling mechanisms. One such mechanism comprises resampling methods, which re-evaluate the solutions multiple times in order to estimate their true objective values. The state-of-the-art integration with which the best results have been obtained utilizes the resampling method named Optimal Computing Budget Allocation (OCBA). This resampling method starts by estimating the objective values of all the solutions via Equal Resampling (ER), and then sequentially allocating further re-evaluations to the estimated best solutions. However, after having a first estimate via ER, we question the importance of the additional efforts to correctly select the true best solutions when a good-enough and accurate one can be selected. In this paper, we propose a new PSO algorithm based on ER in which the additional evaluations are allocated at once to the estimated best solutions, thus skipping the complexity of using OCBA. Experiments on 20 large-scale benchmark functions subject to different levels of noise show that the proposed algorithm produces similar results to PSO with OCBA in most cases.