Niching Clonal Selection Algorithm for multimodal function optimization

Interest in multimodal function optimization problems is expanding rapidly since real-world optimization problems often require the location of multiple optima in the search space. In this paper, a new niching strategy, deterministic replacement policy, is put forward in the proposed algorithm named Niching Clonal Selection Algorithm (NCSA). In order to improve the algorithm's performance, it advances a new selection method-meme selection, based on the knowledge of meme. The numerical experiment on four typical multimodal function optimization problems attests to the proposed algorithm's validity, Finally, the study compares NCSA with some niching evolution algorithms. From the experimental results, we can see that the proposed algorithm is superior to them on all of the tested functions.