Dynamic multi-objective evolutionary algorithms for single-objective optimization
Affiliation:
1. Department of Control Science and Engineering, Tongji University, 4800 Cao''an Road, Shanghai 201804, China;2. BEACON Center, Michigan State University, 567 Wilson Road, East Lansing, MI 48824, USA
Abstract:
This paper proposes a new method for handling the difficulty of multi-modality for the single-objective optimization problem (SOP). The method converts a SOP to an equivalent dynamic multi-objective optimization problem (DMOP). A new dynamic multi-objective evolutionary algorithm (DMOEA) is implemented to solve the DMOP. The DMOP has two objectives: the original objective and a niche-count objective. The second objective aims to maintain the population diversity for handling the multi-modality difficulty during the search process. Experimental results show that the performance of the proposed algorithm is significantly better than the state-of-the-art competitors on a set of benchmark problems and real world antenna array problems.