In multi-objective evolutionary algorithms (MOEAs), convergence and diversity are two basic issues and keeping a balance between them plays a vital role. There are several studies that have attempted to address this problem, but this is still an open challenge. It is thus the purpose of this research to develop a dual-population competitive co-evolutionary approach to improving the balance between convergence and diversity. We utilize two populations to solve separate tasks. The first population uses Pareto-based ranking scheme to achieve better convergence, and the second one tries to guarantee population diversity via the use of a decomposition-based method. Next, by operating a competitive mechanism to combine the two populations, we create a new one with a view to having both characteristics (i.e. convergence and diversity). The proposed method's performance is measured by the renowned benchmarks of multi-objective optimization problems (MOPs) using the hypervolume (HV) and the inverted generational distance (IGD) metrics. Experimental results show that the proposed method outperforms cutting-edge co-evolutionary algorithms with a robust performance.

中文翻译：

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多目标进化算法的竞争协同进化方法


在多目标进化算法（MOEA）中，收敛性和多样性是两个基本问题，保持它们之间的平衡起着至关重要的作用。有几项研究试图解决这个问题，但这仍然是一个开放的挑战。因此，本研究的目的是开发一种双种群竞争协同进化方法来改善收敛性和多样性之间的平衡。我们利用两个群体来解决不同的任务。第一个群体使用基于帕累托的排序方案来实现更好的收敛性，第二个群体试图通过使用基于分解的方法来保证群体多样性。接下来，通过运行竞争机制将两个群体结合起来，我们创建一个新的群体，以期具有两种特征（即趋同性和多样性）。所提出的方法的性能是通过使用超体积（HV）和倒代距离（IGD）指标的多目标优化问题（MOP）的著名基准来衡量的。实验结果表明，该方法优于前沿的协同进化算法，具有鲁棒性。