A many-objective evolutionary algorithm based on dominance and decomposition with reference point adaptation

Abstract Achieving balance between convergence and diversity is a challenge in many-objective optimization problems (MaOPs). Many-objective evolutionary algorithms (MaOEAs) based on dominance and decomposition have been developed successfully for solving partial MaOPs. However, when the optimization problem has a complicated Pareto front (PF), these algorithms show poor versatility in MaOPs. To address this challenge, this paper proposes a co-guided evolutionary algorithm by combining the merits of dominance and decomposition. An elitism mechanism based on cascading sort is exploited to balance the convergence and diversity of the evolutionary process. At the same time, a reference point adaptation method is designed to adapt to different PFs. The performance of our proposed method is validated and compared with seven state-of-the-art algorithms on 200 instances of 27 widely employed benchmark problems. Experimental results fully demonstrate the superiority and versatility of our proposed method on MaOPs with regular and irregular PFs.