This paper proposes a multiobjective multitasking optimization evolutionary algorithm based on decomposition with dual neighborhood. In our proposed algorithm, each subproblem not only maintains a neighborhood based on the Euclidean distance among weight vectors within its own task, but also keeps a neighborhood with subproblems of other tasks. Gray relation analysis is used to define neighborhood among subproblems of different tasks. In such a way, relationship among different subproblems can be effectively exploited to guide the search. Experimental results show that our proposed algorithm outperforms four state-of-the-art multiobjective multitasking evolutionary algorithms and a traditional decomposition-based multiobjective evolutionary algorithm on a set of test problems.

中文翻译：

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基于双邻域分解的多目标多任务优化

提出了一种基于双邻域分解的多目标多任务优化进化算法。在我们提出的算法中，每个子问题不仅在其自身任务中基于权重向量之间的欧几里得距离维持邻域，而且还与其他任务的子问题保持邻域。灰色关联分析用于定义不同任务子问题之间的邻域。这样，可以有效地利用不同子问题之间的关系来指导搜索。实验结果表明，在一系列测试问题上，我们提出的算法优于四种最新的多目标多任务进化算法和传统的基于分解的多目标进化算法。