Surrogate model assisted cooperative coevolution for large scale optimization

It has been shown that cooperative coevolution (CC) can effectively deal with large scale optimization problems (LSOPs) through a ‘divide-and-conquer’ strategy. However, its performance is severely restricted by the current context-vector-based sub-solution evaluation method, since this method needs to invoke the original high dimensional simulation model when evaluating each sub-solution, thus requiring many computation resources. To alleviate this issue, this study proposes a novel surrogate model assisted cooperative coevolution (SACC) framework. SACC constructs a surrogate model for each sub-problem and employs it to evaluate corresponding sub-solutions. The original simulation model is only adopted to reevaluate a small number of promising sub-solutions selected by surrogate models, and these really evaluated sub-solutions will in turn be employed to update surrogate models. By this means, the computation cost could be greatly reduced without significantly sacrificing evaluation quality. By taking the radial basis function (RBF) and the success-history based adaptive differential evolution (SHADE) as surrogate model and optimizer, respectively, this study further designs a concrete SACC algorithm named RBF-SHADE-SACC. RBF and SHADE have only been proved to be effective on small and medium scale problems. This study scales them up to LSOPs under the SACC framework, where they are tailored to a certain extent for adapting to the characteristics of LSOPs and SACC. Empirical studies on IEEE CEC 2010 benchmark functions demonstrate that SACC can significantly enhance the sub-solution evaluation efficiency, and even with much fewer computation resources, RBF-SHADE-SACC can find much better solutions than traditional CC algorithms.