To promote research on dynamic constrained multiobjective optimization, we first propose a group of generic test problems with challenging characteristics, including different modes of the true Pareto front (e.g., convexity–concavity and connectedness–disconnectedness) and the changing feasible region. Subsequently, motivated by the challenges presented by dynamism and constraints, we design a dynamic constrained multiobjective optimization algorithm with a nondominated solution selection operator, a mating selection strategy, a population selection operator, a change detection method, and a change response strategy. The designed nondominated solution selection operator can obtain a nondominated population with diversity when the environment changes. The mating selection strategy and population selection operator can adaptively handle infeasible solutions. If a change is detected, the proposed change response strategy reuses some portion of the old solutions in combination with randomly generated solutions to reinitialize the population, and a steady-state update method is designed to improve the retained previous solutions. The experimental results show that the proposed test problems can be used to clearly distinguish the performance of algorithms, and that the proposed algorithm is very competitive for solving dynamic constrained multiobjective optimization problems in comparison with state-of-the-art algorithms.

中文翻译：

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动态约束多目标优化问题的新进化算法

为了促进动态约束多目标优化的研究，我们首先提出了一组具有挑战性特征的通用测试问题，包括真实帕累托前沿的不同模式（例如，凸-凹和连通-断开）和变化的可行区域。随后，在动力和约束带来的挑战的推动下，我们设计了一种动态约束多目标优化算法，该算法具有非支配解选择算子、交配选择策略、种群选择算子、变化检测方法和变化响应策略。设计的非支配解选择算子可以在环境变化时获得具有多样性的非支配种群。交配选择策略和种群选择算子可以自适应地处理不可行的解决方案。如果检测到变化，提出的变化响应策略会重用部分旧解决方案结合随机生成的解决方案重新初始化种群，并设计稳态更新方法来改进保留的先前解决方案。实验结果表明，所提出的测试问题可以用来清楚地区分算法的性能，并且与最先进的算法相比，所提出的算法在解决动态约束多目标优化问题方面非常具有竞争力。提议的变更响应策略重用部分旧解决方案结合随机生成的解决方案来重新初始化种群，并设计了一种稳态更新方法来改进保留的先前解决方案。实验结果表明，所提出的测试问题可以用来清楚地区分算法的性能，并且与最先进的算法相比，所提出的算法在解决动态约束多目标优化问题方面非常具有竞争力。提出的变更响应策略重用部分旧解决方案结合随机生成的解决方案来重新初始化种群，并设计了一种稳态更新方法来改进保留的先前解决方案。实验结果表明，所提出的测试问题可以用来清楚地区分算法的性能，并且与最先进的算法相比，所提出的算法在解决动态约束多目标优化问题方面非常具有竞争力。