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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...
In this paper, we propose a multilevel cooperative coevolution (MLCC) framework for large scale optimization problems. The motivation is to improve our previous work on grouping based cooperative coevolution (EACC-G), which has a hard-to-determine parameter, group size, in tackling problem decomposition. The problem decomposer takes group size as parameter to divide the objective vector into low dimensional...
In this paper we investigate several self-adaptive mechanisms to improve our previous work on NSDE, which is a recent DE variant for numerical optimization. The self-adaptive methods originate from another DE variant, SaDE, but are remarkably modified and extended to fit our NSDE. And thus a self-adaptive NSDE (SaNSDE) is proposed to improve NSDEpsilas performance. Three self-adaptive mechanisms are...
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