A multi-objective differential evolution algorithm with memory based population construction

Abstract

Different from most of the previous multi-objective differential evolutionary (MODE) algorithms focusing on the selection of control parameters or mutation strategies, this paper developed a new MODE algorithm in which the search history of each solution is memorized to construct a good new population for the next generation. This population construction strategy based on memory is motivated by the fact that a population with good quality and diversity can generally help to generate more promising new solutions. In this strategy, the non-dominated solutions obtained by each solution are memorized in an archive and subsequently a construction method is proposed to select solutions with good quality and diversity from the union of all archives to construct the new population. This strategy is incorporated into an adaptive MODE with multiple mutation operators. Computational results on benchmark problems show that the proposed strategy can significantly improve the search efficiency of MODE with traditional population update strategy. The results also reveal that the proposed MODE is superior to some state-of-the-art MODEs and multi-objective evolutionary algorithms in the literature.