Promising Area Exploration Based on Hybrid Niching: A Metaheuristic Search Framework for Multimodal Optimization

Multimodal optimization aims to find multiple optimal or near-optimal solutions in solving a single-objective optimization problem. In this paper we propose a metaheuristic framework, which utilizes several niching methods including speciation, crowding, and clearing to keep population diversity and search multiple areas in the solution space in parallel. It also uses an archive to store inferior solutions to refresh the population to explore promising areas. The performance of the proposed framework is verified by comparing it with four existing algorithms using the CEC2013 benchmark. The results confirm the positive effects of the proposed ideas and show that our framework provides competitive search ability.