| Abstract: | The multi-objective differential evolution(MODE) algorithm is an effective method to solve multi-objective optimization problems. However, in the absence of any information of evolution progress, the optimization strategy of the MODE algorithm still appears as an open problem. In this paper, a dynamic multi-objective differential evolution algorithm, based on the information of evolution progress(DMODE-IEP), is developed to improve the optimization performance. The main contributions of DMODE-IEP are as follows. First, the information of evolution progress, using the fitness values, is proposed to describe the evolution progress of MODE. Second, the dynamic adjustment mechanisms of evolution parameter values, mutation strategies and selection parameter value based on the information of evolution progress, are designed to balance the global exploration ability and the local exploitation ability. Third, the convergence of DMODE-IEP is proved using the probability theory. Finally, the testing results on the standard multi-objective optimization problem and the wastewater treatment process verify that the optimization effect of DMODE-IEP algorithm is superior to the other compared state-of-the-art multi-objective optimization algorithms, including the quality of the solutions, and the optimization speed of the algorithm. |
