New multi-objective particle swarm optimization based on extended individual memory
基于粒子记忆体的多目标微粒群算法*

To deal with the problem of diversity distribution of solution in multi-objective particle swarm optimization,this paper proposed a diversity pbest based multi-objective particle swarm optimization algorithm (dp-MOPSO).In dp-MOPSO,allocated each particle an individual memory to save the solution set of non-dominated pbest which were fiound in the searching process, avoiding the loss of searching information.Used an external archive to save all the Pareto solutions, and introduced the dynamic neighborhood strategy to select the global optimal solution from the external archive.Tested several multi-objective benchmark functions for comparing the performance of dp-MOPSO with two famous multi-objective evolutionary algorithm m-DNPSO and SPEA2.The results show that dp-MOPSO converges to the true Pareto front more closely, and also all the Pareto solutions are well-distributed.