A new high-level relay hybrid metaheuristic for black-box optimization problems

Abstract

In this paper, a high-level relay hybridization of three metaheuristics with different properties is proposed. Our objective is to investigate the use of this kind of hybridization to tackle black-box optimization problems. Indeed, without any knowledge about the nature of the problem to optimize, combining the strengths of different algorithms, belonging to different classes of metaheuristics, may increase the probability of success of the optimization process. The proposed hybrid algorithm combines the multiple local search algorithm for dynamic optimization, the success-history based adaptive differential evolution, and the standard particle swarm optimization 2011 algorithm. An experimental analysis using two well-known benchmarks is presented, i.e. the Black-Box Optimization Benchmarking (BBOB) 2015 and the Black Box optimization Competition (BBComp). The proposed algorithm obtains promising results on both benchmarks. The ones obtained at BBComp show the relevance of the proposed hybridization.