Dynamic multi-objective evolutionary algorithms for single-objective optimization - Jiao, Ruwang; Zeng, Sanyou; Alkasassbeh, Jawdat S.; Li, Changhe (Applied soft computing, 2017)
•Convert a single-objective optimization problem with many local optima into an equivalent dynamic multi-objective optimization problem.•The converted two objectives include the original objective and a niche-count objective, niche-count aims to maintain the population diversity.•The niche radius provides a proper balance between the exploitation and the exploration by gradually pushing the niche radius to zero. 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.