Brain storm algorithm combined with covariance matrix adaptation evolution strategy for optimization

Abstract

With the development of computational intelligence, many intelligence algorithms have attracted the attention of the scientific community, and a great deal of work on optimizing these algorithms is in full swing. One of the optimization techniques that we focus on is the hybridization of algorithms. Brain storm optimization algorithm (BSO), belonging to the swarm intelligence algorithms, is proposed by taking inspiration of human brain storming behavior. Meanwhile, the covariance matrix adaptive evolutionary strategy algorithm (CMA-ES) which belongs to the field of evolutionary strategy is also concerned. The purpose of this paper is to combine the search capability of BSO with the search efficiency of CMA-ES to achieve a relatively balanced and effective solution.