Many real-world applications can be modelled as dynamic constrained optimization problems (DCOPs). Due to the fact that objective function and/or constraints change over time, solving DCOPs is a challenging task. Although solving DCOPs by evolutionary algorithms has attracted increasing interest in the community of evolutionary computation, the design of benchmark test functions of DCOPs is still insufficient. Therefore, we propose a test suite for DCOPs. A dynamic unconstrained optimization benchmark with good time-varying characteristics, called moving peaks benchmark, is chosen to be the objective function of our test suite. In addition, we design adjustable dynamic constraints, by which the size, number, and change severity of the feasible regions can be flexibly controlled. Furthermore, the performance of three dynamic constrained optimization evolutionary algorithms is tested on the proposed test suite, one of which is presented in this paper, named dynamic constrained optimization differential evolution (DyCODE). DyCODE includes three main phases: 1) the first phase intends to enter the feasible region from different directions promptly via a multi-population search strategy; 2) in the second phase, some excellent individuals chosen from the first phase form a new population to search for the optimal solution of the current environment; and 3) the third phase combines the memory individuals of the first two phases with some randomly generated individuals to re-initialize the population for the next environment. From the experiments, one can understand the strengths and weaknesses of the three compared algorithms for solving DCOPs in depth. Moreover, we also give some suggestions for researchers to apply these three algorithms on different occasions.

许多现实世界的应用程序可以建模为动态约束优化问题（DCOP）。由于目标函数和/或约束随时间变化，解决 DCOP 是一项具有挑战性的任务。尽管通过进化算法求解DCOP引起了进化计算界越来越多的兴趣，但DCOP的基准测试函数的设计仍然不足。因此，我们提出了 DCOP 的测试套件。选择具有良好时变特性的动态无约束优化基准（称为移动峰值基准）作为我们测试套件的目标函数。此外，我们设计了可调节的动态约束，通过该约束可以灵活控制可行区域的大小、数量和变化严重程度。此外，在所提出的测试套件上测试了三种动态约束优化进化算法的性能，本文提出了其中一种，称为动态约束优化差分进化（DyCODE）。 DyCODE包括三个主要阶段：1）第一阶段旨在通过多群体搜索策略从不同方向快速进入可行区域； 2）第二阶段，第一阶段选出的一些优秀个体组成新的种群，搜索当前环境的最优解； 3）第三阶段将前两个阶段的记忆个体与一些随机生成的个体结合起来，为下一个环境重新初始化种群。通过实验，我们可以深入了解三种比较算法在求解 DCOP 时的优缺点。此外，我们还为研究人员在不同场合应用这三种算法提供了一些建议。