Many-hard-objective optimization using differential evolution based on two-stage constraint-handling
Kiyoharu Tagawa, Akihiro Imamura · 2013
This paper focus on the Many-Hard-objective Optimization Problem (MHOP) in which a lot of objectives are limited by a goal point. In order to obtain an approximation of Pareto-optimal feasible solution set for MHOP, a new algorithm called Differential Evolution for Many-Hard-objective Optimization (DEMHO) is proposed. For sorting non dominated solutions, DEMHO uses Pairwise Exclusive Hypervolume (PEH) with a newly proposed fast calculation algorithm. Besides, for handing the infeasible solutions of MHOP, a new two-stage truncation method is employed. Through the numerical experiment and the statistical test conducted on some instances of MHOP, the performance of DEMHO is assessed. As a case study, the usefulness of DEMHO is also demonstrated on an optimum design of SAW duplexer.