Decision Space Scalability Analysis of Multi-Objective Particle Swarm Optimization Algorithms

Abstract

Particle swarm optimization (PSO) has been adapted to solve multi-objective optimization problems. However, these PSO-based multi-objective optimization algorithms typically face difficulties when the number of decision variables is increased and the problems turn into large-scale multi-objective problems (LSMOPs). This paper presents a decision space scalability analysis of five PSO-based multi-objective optimization algorithms, namely optimized multi-objective particle swarm op-timization (OMOPSO), speed-constrained multi-objective particle swarm optimization (SMPSO), multi-objective particle swarm optimization with multiple search strategies (MMOPSO), multi-guide particle swarm optimization (MGPSO), and competitive mechanism-based multi-objective particle swarm optimization (CMOPSO) for 24, 50, 100, 500 and 1000 dimensions (decision variables) to see how well each one of the algorithms scales as the number of decision variables is increased. The results indicate that, with an increase in the number of decision variables, MMOPSO and SMPSO had the best scalability, each dominating specific functions. Moreover, despite MGPSO's competitive performance on the 24-dimensional functions, it showed the worst overall scalability together with CMOPSO.