Enhancing Cooperative Coevolution for Large Scale Optimization by Exploiting Decomposition Solutions

Problem decomposition plays a vital role in ensuring the performance of cooperative coevolution (CC) for large scale global optimization (LSGO). However, existing high-precision decomposition algorithms require evaluating numerous solutions generally generated in a fixed style and directly abandon them after the decomposition process. This leads to the waste of many computation resources. Directing against this issue, this study attempts to sample decomposition solutions in a local-search manner such that these solutions not only satisfy the decomposition requirements but also possess high quality, and thus can provide a good initial population for the subsequent optimization process. Following this research idea, this study develops a new decomposition algorithm named recursive differential grouping with local search ability (LS-RDG) by embedding the Solis Wets local search operator into the recently developed RDG algorithm. LS-RDG can obtain more promising solutions without consuming extra fitness evaluations. Comprehensive experimental results on two widely used LSGO benchmark suites verify the effectiveness of LS-RDG and indicate that LS-RDG can help CC achieve better optimization performance.