A novel PSDE algorithm for multi-objective optimization

Differential evolution for multi-objective optimization is a useful and straightforward evolutionary optimization algorithm. Since the algorithm randomly searches in problem space, it may get into trouble when we adapt it to solve multi-objective problems. With regard to the limitation of DE, we propose a new algorithm PSDE to solve multi-objective functions based on the PSO operator and DE operator. In this algorithm, we utilize DE operator to promote the uniform spread of solutions and use PSO operator to obtain good local search ability by retaining the best solutions obtained so far. Experimental results on a set of benchmark test functions sustain the effectiveness of the proposed algorithm.