A differential evolution with adaptive neighborhood mutation and local search for multi-modal optimization

In this paper, an adaptive neighborhood mutation based memetic differential evolution is proposed for multimodal optimization. In the proposed method, an adaptive neighborhood mutation (ANM) strategy is devised to allow the individuals to conduct a diverse search at the early stage of evolution and then gradually switch to an intensive search at the later stage of evolution. Further, the ANM is devised such that encouraging promising individuals for exploitation while unpromising individuals for exploration during evolution to appropriate search the multimodal space. In addition, an adaptive Gaussian based local search strategy, which considers the difference between the offspring and their paired individuals with successful replacements, is developed to appropriately improve promising individuals during evolution. The proposed method has been extensively accessed on a suite of twenty multimodal benchmark functions and the performance of which is compared with sixteen related multimodal algorithms. The results clearly demonstrate the superiority of proposed method as well as the merit of devised strategies.