Multi-objective particle optimization algorithm based on sharing–learning and dynamic crowding distance

A multi-objective particle swarm optimization algorithm, based on share–learning and dynamic crowding distance (MOPSO-SDCD), 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, Gaussian mutation and greedy strategy are adopted to update personal best position and external archive, which make the algorithm approximate the Pareto front quickly and avoid premature convergence. Finally, MOPSO-SDCD maintains the external archive based on dynamic crowding distance sorting strategy, whose purpose is boosting the diversity and distribution of Pareto optimal solutions. The ZDT series test functions are used to test the performance of MOPSO-SDCD and compare with other three typical algorithms. Simulation results verify the superiority and effectiveness of the proposed algorithm.