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.

在多目标优化问题 (MaOP) 中，实现收敛性和多样性之间的平衡是一项挑战。基于优势和分解的多目标进化算法 (MaOEA) 已成功开发用于解决部分 MaOPs。然而，当优化问题具有复杂的帕累托前沿 (PF) 时，这些算法在 MaOP 中的通用性较差。为了应对这一挑战，本文提出了一种结合支配和分解优点的协同引导进化算法。利用基于级联排序的精英机制来平衡进化过程的收敛性和多样性。同时设计了一种参考点自适应方法来适应不同的PF。我们提出的方法的性能得到了验证，并在 27 个广泛使用的基准问题的 200 个实例上与七种最先进的算法进行了比较。实验结果充分证明了我们提出的方法在具有规则和不规则 PF 的 MaOP 上的优越性和通用性。