Few existing studies cover the state-of-the-art multi-objective particle swarm optimization(MOPSO)algorithms.To fill the gap in this area,the research background of multi-objective optimization problems(MOPs)was introduced,and the fundamental theories of MOPSO were described.The MOPSO algorithms were divided into three categories according to their features:Pareto-dominated-based MOPSO,decomposition-based MOPSO,and indicator-based MOPSO,and a detailed description of their existing classical algorithms was also developed.Next,relevant evaluation indicators were described,and seven representative algorithms were selected for performance analysis.The experimental results demonstrated the strengths and weaknesses of each of the traditional MOPSO and three categories of improved MOPSO algorithms.Among them,the indicator-based MOPSO performed better in terms of convergence and diversity.Then,the applications of MOPSO algorithms in production scheduling,image processing,and power systems were briefly introduced.Finally,the limitations and future research directions of the MOPSO algorithm for solving complex optimization problems were discussed.
维普
万方数据