Adaptive candidate estimation-assisted multi-objective particle swarm optimization

The selection of global best (Gbest) exerts a high influence on the searching performance multi-objective particle swarm optimization algorithm (MOPSO). candidates MOPSO in external archive are always estimated to select Gbest. However, most estimation methods, considered as Gbest fixed way, which is difficult adapt varying evolutionary requirements for balance between convergence and diversity MOPSO. To deal with this problem, an adaptive candidate estimation-assisted (ACE-MOPSO) proposed paper. First, state information, including both dominance information distribution non-dominated solutions, introduced describe states extract requirements. Second, method, based two evaluation distances, developed excellent leader balancing during dynamic process. Third, mutation strategy, using elite local search (ELS), devised improve ability ACE-MOPSO. Fourth, analysis given prove theoretical validity Finally, compared popular algorithms twenty-four benchmark functions. results demonstrate that ACE-MOPSO has advanced diversity.