An adaptive coevolutionary Differential Evolution algorithm for large-scale optimization

Zhenyu Yang, Jingqiao Zhang, Ke Tang, Xin Yao, Arthur C. Sanderson

Abstract: In this paper, we propose a new algorithm, named JACC-G, for large scale optimization problems. The motivation is to improve our previous work on grouping and adaptive weighting based cooperative coevolution algorithm, DECC-G [1], which uses random grouping strategy to divide the objective vector into subcomponents, and solve each of them in a cyclical fashion. The adaptive weighting mechanism is used to adjust all the subcomponents together at the end of each cycle. In the new JACC-G algorithm: (1) A most recent and efficient Differential Evolution (DE) variant, JADE [2], is employed as the subcomponent optimizer to seek for a better performance; (2) The adaptive weighting is time-consuming and expected to work only in the first few cycles, so a detection module is added to prevent applying it arbitrarily; (3) JADE is also used to optimize the weight vector in adaptive weighting process instead of using a basic DE in previous DECC-G. The efficacy of the proposed JACC-G algorithm is evaluated on two sets of widely used benchmark functions up to 1000 dimensions.