Multi-objective particle swarm optimization algorithm based on sharing-learning and Cauchy mutation

摘要

A multi-objective particle swarm optimization algorithm, based on share-learning and Cauchy mutation (MOPSO-SCM), is proposed to improve the convergence accuracy and keep the diversity of the Pareto optimal solutions. First, the sharing-learning factor is applied to modify the velocity updating formulas, which improves both the global search ability and local search accuracy of the algorithm. Meanwhile, Cauchy mutation is adopted to update personal best position and external archive, which makes the algorithm approximate the Pareto front quickly and avoid premature convergence. Finally, the ZDT series test functions are used to test the performance of MOPSO-SCM and compare with other three typical algorithms. Simulation results verify the superiority and effectiveness of the proposed algorithm.