Solving Large-scale Many-objective Optimization Problems by Covariance Matrix Adaptation Evolution Strategy with Scalable Small Subpopulations


Despite the recent development in evolutionary multi- and many-objective optimization, the problems with large-scale decision variables still remain challenging. In this work, we propose a scalable small subpopulations based covariance matrix adaptation evolution strategy, namely S3-CMA-ES, for solving many-objective optimization problems with large-scale decision variables. The proposed S3-CMA-ES attempts to approximate the set of Pareto-optimal solutions using a series of small subpopulations instead of a whole population, where each subpopulation converges to only one solution. In the proposed S3-CMA-ES, a diversity improvement strategy is designed to generate and select new solutions. The performance of S3-CMA-ES is compared with five representative algorithms on 36 test instances with 5–15 objectives and 500–1500 decision variables. The empirical results demonstrate the superiority of the proposed S3-CMA-ES.

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