Evaluation of the mean-variance mapping optimization for solving multimodal problems

Based on swarm intelligence principles and an enhanced mapping scheme, the extension of the original single-particle mean-variance mapping optimization (MVMO) to its swarm variant (MVMO^S) is investigated in this paper. Numerical experiments and comparisons with other heuristic optimization methods, which were conducted on several composition test functions, demonstrate the feasibility and effectiveness of MVMO^S when solving multimodal optimization problems. Sensitivity analysis of the algorithm parameters highlights its robust performance.