Dynamic interval multiobjective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms (EAs) that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multiobjective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two subpopulations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances as well as a multiperiod portfolio selection problem and compared with five state-of-the-art EAs. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances.

中文翻译：

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动态区间多目标优化问题的一种基于相似性的协同协同进化算法

动态区间多目标优化问题 (DI-MOP) 在实际应用中非常普遍。然而，迄今为止，很少有适合处理 DI-MOP 的进化算法 (EA)。本文提出了一种基于区间相似性的动态区间多目标协同协同进化优化框架来处理DI-MOP。在该框架中，首先提出了一种决策变量分解策略，通过该策略将所有决策变量根据每个决策变量和区间参数之间的区间相似性分为两组。之后，利用两个亚群协同优化两组中的决策变量。此外，两种响应策略，即基于变化强度的策略和随机突变策略，用于快速跟踪优化问题的帕累托前沿变化。所提出的算法应用于八个基准优化实例以及一个多周期投资组合选择问题，并与五个最先进的 EA 进行了比较。实验结果表明，所提出的算法在大多数优化实例上都非常具有竞争力。