A diversity enhanced multiobjective particle swarm optimization

摘要: Abstract Multiobjective particle swarm optimizations (MOPSOs) are confronted with convergence difficulty as well diversity deviation, due to combined learning orientations and premature phenomenons. Numerous adaptations of MOPSO have been introduced around the elite definition leader selection in previous studies. Meanwhile unique leader-oriented updating which reflects some properties evolving, may provide control assistance under particular conditions. However, repetition inefficient works on determination exist, seldomly studies taken PSO’s evolve rhythms into consideration adjust optimize strategy adaptively. In view above problems, aim balance during searching procedure, a novel enhanced multiobjective optimization (DEMPSO) is proposed this paper. The method mainly focuses following innovations. First, simplified formulation PSO introduced. Second, through taking full advantages mechanism extracting particles velocity information, intersection measurement for decision variable analysis enhancement proposed. Third, an adaptive two-fold presented. experimental results benchmark test instances illustrate that DEMPSO outperforms other PSO-cored algorithms, greatly improves maintain ability high-dimensional objective spaces comparison state-of-the-art decomposition-based dominated-based evolutionary algorithms.