Multiple Swarms Multi-objective Particle Swarm Optimization Based on Decomposition

摘要

Particle swarm optimization is a very competitive swarm intelligence algorithm for multi-objective optimization problems, but because of it is easy to fall into local optimum solution, and the convergence and accuracy of Pareto solution set is not satisfactory. So we proposed a multi-swarm multi-objective particle swarm optimization based on decomposition (MOPSO_MS), in the algorithm each sub-swarm corresponding to a sub-problem which decomposed by multi-objective decomposition method, and we constructed a new updates strategy for the velocity. Finally, through simulation experiments and compare with the state-of-the-art multi-objective particle swarm algorithm on ZDT test function proved the convergence and the accuracy of the algorithm.