Evolutionary algorithms have been shown to obtain good solutions for complex optimization problems in static and dynamic environments. It is important to understand the behaviour of evolutionary algorithms for complex optimization problems that also involve dynamic and/or stochastic components in a systematic way in order to further increase their applicability to real-world problems. We investigate the node weighted traveling salesperson problem (W-TSP), which provides an abstraction of a wide range of weighted TSP problems, in dynamic settings. In the dynamic setting of the problem, items that have to be collected as part of a TSP tour change over time. We first present a dynamic setup for the dynamic W-TSP parameterized by different types of changes that are applied to the set of items to be collected when traversing the tour. Our first experimental investigations study the impact of such changes on resulting optimized tours in order to provide structural insights of optimization solutions. Afterwards, we investigate simple mutation-based evolutionary algorithms and study the impact of the mutation operators and the use of populations with dealing with the dynamic changes to the node weights of the problem.

翻译：演化算法已被证明能在静态与动态环境中为复杂优化问题求得高质量解。为系统理解涉及动态和/或随机成分的复杂优化问题中演化算法的行为特征，从而进一步提升其在现实世界问题中的适用性，我们研究了动态环境下的节点加权旅行商问题(W-TSP)，该问题为一系列加权TSP问题提供了抽象模型。在该问题的动态设置中，需在TSP路径中收集的物品会随时间变化。我们首先提出一种参数化动态W-TSP实验框架，该框架通过作用于待收集物品集合的不同类型变化来模拟动态过程。初步实验研究分析了此类变化对优化路径的影响，以揭示优化解的结构性质。随后，我们研究了基于简单变异算子的演化算法，探讨了变异算子及种群策略在应对节点权重动态变化时的作用。