Benchmarking MOEAs for multi- and many-objective optimization using an unbounded external archive
Ryoji Tanabe, Akira Oyama · Proceedings of the Genetic and Evolutionary Computation Conference · 2017
While a large number of multi-objective evolutionary algorithms (MOEAs) for many-objective optimization problems (MaOPs) have been proposed in the past few years, an exhaustive benchmarking study has never been performed. Moreover, most previous studies evaluated the performance of MOEAs based on nondominated solutions in the final population at the end of the search. In this paper, we exhaustively investigate the convergence performance of 21 MOEAs using an unbounded external archive that stores all nondominated solutions found during the search process. Surprisingly, the experimental results for the WFG functions with up to six objectives indicate that several recently proposed MOEAs perform significantly worse than classical MOEAs. Moreover, the performance rank among the 21 MOEAs significantly depends on the number of function evaluations. Thus, the previously reported performance of MOEAs on MaOPs as well as the widely used bench-marking methodology must be carefully reconsidered.