Overall constraint violation functions are commonly used in multiobjective evolutionary algorithms (MOEAs) for handling constraints. Constraints could cause these algorithms stuck in two stagnation states: 1) since the feasible region of a multiobjective optimization problem can consist of several disconnected feasible subregions, the search can be easily trapped in a feasible subregion which does not contain all the global Pareto optimal solutions and 2) an overall constraint violation function may have many nonzero minimal points, it can make the search stuck in an unfeasible area. To address these two issues, this article proposes a strategy to detect whether or not the search is stuck in these two stagnation states and then escape from them. Our proposed detect-and-escape strategy uses the feasible ratio and the change rate of overall constraint violation to detect stagnation, and adjusts the weight of the constraint violation for guiding the search to escape from stagnation states. We develop and implement a decomposition-based constrained MOEA with this strategy. Extensive experiments on a number of benchmark problems demonstrate the competitiveness of our proposed algorithm when compared to five other state-of-the-art constrained evolutionary algorithms.

中文翻译：

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具有检测和逃逸策略的约束多目标进化算法

总体约束违反函数通常用于多目标进化算法 (MOEA) 来处理约束。约束可能导致这些算法陷入两种停滞状态：1）由于多目标优化问题的可行区域可以由几个不相连的可行子区域组成，搜索很容易被困在不包含所有全局帕累托最优解的可行子区域中2) 一个整体约束违反函数可能有很多非零极小点，它会使搜索卡在一个不可行的区域。针对这两个问题，本文提出了一种策略来检测搜索是否陷入这两种停滞状态，然后摆脱它们。我们提出的检测和逃逸策略使用可行比率和总体约束违反的变化率来检测停滞，并调整约束违反的权重以引导搜索摆脱停滞状态。我们使用此策略开发并实施基于分解的约束 MOEA。与其他五种最先进的约束进化算法相比，对许多基准问题的大量实验证明了我们提出的算法的竞争力。