A new dominance-relation metric balancing convergence and diversity in multi- and many-objective optimization

作者：

Highlights：

• A more structured metric is proposed to promote the balance between convergence and diversity in many-objective optimization.

• A distance-based diversity maintenance scheme is used to each non-dominated front to maintain population diversity.

• Make full use of the neighborhood information in mating selection, which significantly improves the efficiency of the algorithm.

• At most one solution in each sub-region of current Pareto front is selected in selection operation, which improves the efficiency of the algorithm.

摘要

•A more structured metric is proposed to promote the balance between convergence and diversity in many-objective optimization.•A distance-based diversity maintenance scheme is used to each non-dominated front to maintain population diversity.•Make full use of the neighborhood information in mating selection, which significantly improves the efficiency of the algorithm.•At most one solution in each sub-region of current Pareto front is selected in selection operation, which improves the efficiency of the algorithm.

论文关键词：Convergence,Diversity,Many-objective optimization,Pareto dominance,Decomposition

论文评审过程：Received 20 March 2019, Revised 21 May 2019, Accepted 23 May 2019, Available online 23 May 2019, Version of Record 28 May 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.05.032