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Analysis of leader selection strategies in a multi-objective Particle Swarm Optimizer

Algorithms based on the Particle Swarm Optimization (PSO) scheme have become popular to solve both single- and multi-objective optimization problems. In this paper, we focus on SMPSO, a PSO designed to cope with this second group of problems. Taking it as our starting point, we analyze different leader selection schemes, which give rise to four new variants of SMPSO. These new versions, along with the original algorithm, are compared using a benchmark composed of 21 problems. Our study reveals that SMPSOhv, a variant that uses the hypervolume indicator to guide leader selection, is the best performing algorithm in our comparison, outperforming also the original version of SMPSO. To further assess the performance of SMPSOhv, we compare it against NSGA-II and SMS-EMOA, achieving again the best overall results in this new comparative study. Based on these observations, we conclude that the use of the hypervolume for leader selection is a promising approach for multi-objective PSO algorithms.