Cooperation coevolution with fast interdependency identification for large scale optimization

Abstract

Cooperative coevolution (CC) provides a powerful divide-and-conquer architecture for large scale global optimization (LSGO). However, its performance relies highly on decomposition. To make near-optimal decomposition, most developed decomposition strategies either cannot obtain the correct interdependency information or require a lot of fitness evaluations (FEs) in the identification. To alleviate the limitations in previous works, in this paper we propose a fast interdependency identification (FII) algorithm for CC in LSGO. The proposed algorithm firstly identifies separable and nonseparable variables efficiently. Then, the interdependency information of nonseparable variables is further investigated. To make near-optimal decomposition for CC, our algorithm avoids the necessity of obtaining the full interdependency information of nonseparable variables. Therefore, a significant number of FEs can be saved. Extensive experiments have been conducted on two suites of LSGO benchmark functions with up to 2000 variables. FII correctly identified the interdependency information on most benchmark functions with much fewer FEs in comparison with three state-of-the-art algorithms. Furthermore, combined with CC and coupled with a differential evolution variant serving as the optimizer, FII has shown its promising performance in LSGO.