A minimum population search hybrid for large scale global optimization

Abstract

Large-scale global optimization is a challenging task which is embedded in many scientific and engineering applications. Among large scale problems, multimodal functions present an exceptional challenge because of the need to promote exploration. In this paper we present a hybrid heuristic specifically designed for optimizing large scale multimodal functions. The hybrid is based on the unbiased exploration ability of Minimum Population Search. Minimum Population Search is a recently developed metaheuristic able to efficiently optimize multimodal functions. However, MPS lacks techniques for exploiting search gradients. To overcome this limitation, we combine its exploration power with the intense local search of the CMA-ES algorithm. The proposed algorithm is evaluated on the test functions provided by the LSGO competition of IEEE Congress of Evolutionary Computation (CEC 2013).