Multiobjective multitasking optimization (MTO) needs to solve a set of multiobjective optimization problems simultaneously, and tries to speed up their solution by transferring useful search experiences across tasks. However, the quality of transfer solutions will significantly impact the transfer effect, which may even deteriorate the optimization performance with an improper selection of transfer solutions. To alleviate this issue, this article suggests a new multiobjective multitasking evolutionary algorithm (MMTEA) with decomposition-based transfer selection, called MMTEA-DTS. In this algorithm, all tasks are first decomposed into a set of subproblems, and then the transfer potential of each solution can be quantified based on the performance improvement ratio of its associated subproblem. Only high-potential solutions are selected to promote knowledge transfer. Moreover, to diversify the transfer of search experiences, a hybrid transfer evolution method is designed in this article. In this way, more diverse search experiences are transferred from high-potential solutions across different tasks to speed up their convergence. Three well-known benchmark suites suggested in the competition of evolutionary MTO and one real-world problem suite are used to verify the effectiveness of MMTEA-DTS. The experiments validate its advantages in solving most of the test problems when compared to five recently proposed MMTEAs.

中文翻译：

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具有基于分解的传输选择的多目标多任务优化


多目标多任务优化（MTO）需要同时解决一组多目标优化问题，并试图通过跨任务转移有用的搜索经验来加速解决问题。然而，传输解的质量会显着影响传输效果，甚至可能因传输解选择不当而导致优化性能下降。为了缓解这个问题，本文提出了一种新的多目标多任务进化算法（MMTEA），具有基于分解的传输选择，称为 MMTEA-DTS。在该算法中，所有任务首先被分解为一组子问题，然后可以根据其相关子问题的性能改进率来量化每个解决方案的迁移潜力。仅选择高潜力的解决方案来促进知识转移。此外，为了使搜索体验的迁移多样化，本文设计了一种混合迁移进化方法。通过这种方式，更多样化的搜索体验从不同任务的高潜力解决方案中转移出来，以加速它们的收敛。使用进化 MTO 竞赛中提出的三个著名基准套件和一个现实世界问题套件来验证 MMTEA-DTS 的有效性。与最近提出的五个 MMTEA 相比，实验验证了它在解决大多数测试问题方面的优势。