The growing interest in dynamic optimisation has accelerated the development of genetic algorithms with specific mechanisms for these problems. To ensure that these developed mechanisms are capable of solving a wide range of practical problems it is important to have a diverse set of benchmarking functions to ensure the selection of the most appropriate Genetic Algorithm. However, the currently available benchmarking sets are limited to unconstrained problems with predominantly continuous characteristics. In this paper, the existing range of dynamic problems is extended with 15 novel constrained multi-objective functions. To determine how genetic algorithms perform on these constrained problems, and how this behaviour relates to unconstrained dynamic optimisation, 6 top-performing dynamic genetic algorithms are compared alongside 4 re-initialization strategies on the proposed test set, as well as the currently existing unconstrained cases. The results show that there are no differences between constrained/unconstrained optimisation, in contrast to the static problems. Therefore, dynamicity is the prevalent characteristic of these problems, which is shown to be more important than the discontinuous nature of the search and objective spaces. The best performing algorithm overall is MOEA/D, and VP is the best re-initialisation strategy. It is demonstrated that there is a need for more dynamic specific methodologies with high convergence, as it is more important to performance on dynamic problems than diversity.

人们对动态优化的兴趣日益增长，加速了具有这些问题特定机制的遗传算法的开发。为了确保这些已开发的机制能够解决各种实际问题，拥有多种基准功能以确保选择最合适的遗传算法非常重要。但是，当前可用的基准测试集仅限于具有主要连续特征的不受约束的问题。本文利用15种新颖的约束多目标函数扩展了动态问题的存在范围。为了确定遗传算法在这些受限问题上的执行方式，以及该行为与无约束动态优化的关系，在提议的测试集以及当前存在的无约束情况下，对6种性能最高的动态遗传算法以及4种重新初始化策略进行了比较。结果表明，与静态问题相比，约束优化/无约束优化之间没有差异。因此，动态性是这些问题的普遍特征，它比搜索空间和目标空间的不连续性更为重要。总体上，性能最佳的算法是MOEA / D，而VP是最佳的重新初始化策略。事实证明，需要更多具有高度收敛性的动态特定方法，因为对于动态问题的性能比多样性更重要。结果表明，与静态问题相比，约束优化/无约束优化之间没有差异。因此，动态性是这些问题的普遍特征，它比搜索空间和目标空间的不连续性更为重要。总体上，性能最佳的算法是MOEA / D，而VP是最佳的重新初始化策略。事实证明，需要更多具有高度收敛性的动态特定方法，因为对于动态问题的性能比多样性更重要。结果表明，与静态问题相比，约束优化/无约束优化之间没有差异。因此，动态性是这些问题的普遍特征，它比搜索空间和目标空间的不连续性更为重要。总体上，性能最佳的算法是MOEA / D，而VP是最佳的重新初始化策略。事实证明，需要更多具有高度收敛性的动态特定方法，因为对于动态问题的性能比多样性更重要。它被显示比搜索空间和目标空间的不连续性更为重要。总体上，性能最佳的算法是MOEA / D，而VP是最佳的重新初始化策略。事实证明，需要更多具有高度收敛性的动态特定方法，因为对于动态问题的性能比多样性更重要。它被显示比搜索空间和目标空间的不连续性更为重要。总体上，性能最佳的算法是MOEA / D，而VP是最佳的重新初始化策略。事实证明，需要更多具有高度收敛性的动态特定方法，因为对于动态问题的性能比多样性更重要。