Improved MOPSO algorithm based on Cloud Membership

An improved MOPSO algorithm based on Cloud Membership is designed to cope with the problem of quantitative ParetoSort,as well as convergence rate and variety in solution distribution. In this paper, logistic mapping is adopted to optimize the initial population. In addition, PSO shares global best solution pool with Cuckoo Search, which enhances the ability of global optimization and cooperation among searching process. Most importantly, Pareto Cloud Membership is developed for the first time to measure and evaluate particles. Moreover, the concept of Cloud Similarity is treated as a novel convergence indicator. Finally, experiment results of test function set ZDT, show that proposed algorithm is more excellent in both convergence and variety compared with MOPSO and NSGA-II.