An efficient variable interdependency-identification and decomposition by minimizing redundant computations for large-scale global optimization

Abstract

Although many variable decomposition methods for cooperative co-evolution (CC) have been proposed, researches on scalable and efficient decomposition have rarely been done, particularly, for the large-scale global optimization (LSGO) problems. In this paper, we propose an efficient variable interdependency identification and decomposition method, called EVIID. Different from existing studies focusing on only limited scale efficient or accurate variable decomposition, our purpose is to develop a scalable variable decomposition method with high efficiency and accuracy even on very high-dimensional problems. EVIID utilizes three core strategies: a binary variable space search, a dynamic perturbation caching, and a pre-variable sorting. Their synergy effect enables scalable and efficient variable decomposition without sacrificing decomposition accuracy by pruning many redundant computations required to identify interdependencies among decision variables. In comprehensive experiments, EVIID showed highly scalable decomposition ability on 1000 to 10,000 dimensional benchmark problems compared against the state-of-the-art variable decomposition methods. Moreover, when EVIID was embedded into practical CC frameworks, it showed good optimization performance and also fast convergence.