Xue Xiaoming, Zhang Kai, Tan Kay Chen, Feng Liang, Wang Jian, Chen Guodong, Zhao Xinggang, Zhang Liming, Yao Jun
IEEE Trans Cybern. 2022 Jul;52(7):6217-6231. doi: 10.1109/TCYB.2020.3036393. Epub 2022 Jul 4.
Evolutionary multitasking (EMT) is a newly emerging research topic in the community of evolutionary computation, which aims to improve the convergence characteristic across multiple distinct optimization tasks simultaneously by triggering knowledge transfer among them. Unfortunately, most of the existing EMT algorithms are only capable of boosting the optimization performance for homogeneous problems which explicitly share the same (or similar) fitness landscapes. Seldom efforts have been devoted to generalize the EMT for solving heterogeneous problems. A few preliminary studies employ domain adaptation techniques to enhance the transferability between two distinct tasks. However, almost all of these methods encounter a severe issue which is the so-called degradation of intertask mapping. Keeping this in mind, a novel rank loss function for acquiring a superior intertask mapping is proposed in this article. In particular, with an evolutionary-path-based representation model for optimization instance, an analytical solution of affine transformation for bridging the gap between two distinct problems is mathematically derived from the proposed rank loss function. It is worth mentioning that the proposed mapping-based transferability enhancement technique can be seamlessly embedded into an EMT paradigm. Finally, the efficacy of our proposed method against several state-of-the-art EMTs is verified experimentally on a number of synthetic multitasking and many-tasking benchmark problems, as well as a practical case study.
进化多任务处理（EMT）是进化计算领域中一个新兴的研究课题，其旨在通过触发多个不同优化任务之间的知识转移，同时提高这些任务的收敛特性。不幸的是，现有的大多数EMT算法仅能够提升针对明确共享相同（或相似）适应度景观的同类问题的优化性能。很少有研究致力于将EMT推广到求解异构问题上。一些初步研究采用域自适应技术来增强两个不同任务之间的可迁移性。然而，几乎所有这些方法都遇到了一个严重的问题，即所谓的任务间映射退化。考虑到这一点，本文提出了一种用于获得卓越任务间映射的新型秩损失函数。具体而言，借助一种基于进化路径的优化实例表示模型，从所提出的秩损失函数中数学推导得出用于弥合两个不同问题之间差距的仿射变换解析解。值得一提的是，所提出的基于映射的可迁移性增强技术可以无缝嵌入到EMT范式中。最后，在多个合成多任务和多目标基准问题以及一个实际案例研究中，通过实验验证了我们所提方法相对于几种最先进EMT方法的有效性。
