A many-objective algorithm based on staged coordination selection

Convergence and diversity are two performance requirements that should be paid attention to in evolutionary algorithms. Most multiobjective algorithms (MOEAs) try their best maintain a balance between the aspects, which poses challenge convergence of MOEAs early process. In this paper, many-objective optimization algorithm based on staged coordination selection, consists stages, is proposed stages considered separately each iteration. exploring stage, decomposition method adopted rapidly make population close true PF. maintenance mechanism same archive truncation SPEA2 used push distributed individuals The stage serves for second turns into first when it fails reach requirement so forth. Our compared with eight state-of-the-art DTLZ, WFG MaOP benchmark instances. Results show our outperformed comparison most test problems.