Most many-objective evolutionary algorithms balance convergence and diversity by improving environmental selection strategies, with less attention paid to the contribution of offspring to the algorithm performance. This study proposes a many-objective evolutionary algorithm based on a decision variable classification mutation and indicator (MaOEA-DI) that improves the quality of offspring while balancing convergence and diversity to enhance the algorithm’s performance. In MaOEA-DI, a dual-archive guided decision variable classification mutation strategy is designed. This strategy utilizes the decision variable information of elite individuals from the convergence and diversity elite archives, which are updated every generation, to guide the mutation process in generating high-quality offspring. To enhance the quality of the candidate solution set, the newly generated offspring are filtered using the current population information. In addition, an indicator- and density-based environmental selection strategy is developed to balance convergence and diversity. Experimental results on 27 benchmark problems, two real-world optimization problems, and a multiline distance minimization problem show that MaOEA-DI outperforms six advanced algorithms.
原文链接
分享
引用
