Toward Adaptive Knowledge Transfer in Multifactorial Evolutionary Computation
Toward Adaptive Knowledge Transfer in Multifactorial Evolutionary Computation
复制标题
多因素进化计算中的自适应知识转移
DOI：
10.1109/tcyb.2020.2974100
复制
发表时间：
2021-05-01
影响因子：
11.8
通讯作者：
Chen, Chao
中科院分区：
文献类型：
--
作者：
Zhou, Lei;Feng, Liang;Chen, Chao
多因素进化算法(MFEA)是近年来提出的一种同时优化多个优化任务的进化多任务算法。通过设计不同任务之间的知识转移，MFEA在收敛速度和解质量方面都表现出了优于单任务的能力。在MFEA中，跨任务的知识转移是通过具有不同技能因素的解之间的交叉来实现的。因此，这种交叉对于MFEA的性能至关重要。然而，我们注意到，目前的MFEA及其大多数现有的变体只使用单个交叉来进行知识转移，并在整个进化搜索过程中修复它。由于不同的交叉算子在产生后代时有不同的偏向，为了解决不同的问题，在MFEA中选择合适的交叉算子进行知识传递对于提高搜索性能是必要的。然而，就我们所知，目前还没有关于多边有限元分析中交叉的适应性配置以进行知识转移的努力，因此，本文试图填补这一空白。特别是，在这里，我们首先研究了不同类型的交叉如何影响MFEA中单目标(SO)和多目标(MO)连续优化问题的知识转移。此外，为了提高多任务优化的鲁棒性和效率，本文提出了一种自适应知识转移的多任务进化算法(MFEA-AKT)，其中用于知识转移的交叉算子根据进化搜索过程中收集的信息进行自适应调整。为了验证该方法的有效性，我们对SO和MO多任务基准进行了全面的实证研究。实验结果表明，MFEA-AKT能够为不同的优化问题，甚至在搜索过程中的不同优化阶段找到合适的知识转移交叉，从而使其性能优于固定知识转移交叉算子的MFEA。
A multifactorial evolutionary algorithm (MFEA) is a recently proposed algorithm for evolutionary multitasking, which optimizes multiple optimization tasks simultaneously. With the design of knowledge transfer among different tasks, MFEA has demonstrated the capability to outperform its single-task counterpart in terms of both convergence speed and solution quality. In MFEA, the knowledge transfer across tasks is realized via the crossover between solutions that possess different skill factors. This crossover is thus essential to the performance of MFEA. However, we note that the present MFEA and most of its existing variants only employ a single crossover for knowledge transfer, and fix it throughout the evolutionary search process. As different crossover operators have a unique bias in generating offspring, the appropriate configuration of crossover for knowledge transfer in MFEA is necessary toward robust search performance, for solving different problems. Nevertheless, to the best of our knowledge, there is no effort being conducted on the adaptive configuration of crossovers in MFEA for knowledge transfer, and this article thus presents an attempt to fill this gap. In particular, here, we first investigate how different types of crossover affect the knowledge transfer in MFEA on both single-objective (SO) and multiobjective (MO) continuous optimization problems. Furthermore, toward robust and efficient multitask optimization performance, we propose a new MFEA with adaptive knowledge transfer (MFEA-AKT), in which the crossover operator employed for knowledge transfer is self-adapted based on the information collected along the evolutionary search process. To verify the effectiveness of the proposed method, comprehensive empirical studies on both SO and MO multitask benchmarks have been conducted. The experimental results show that the proposed MFEA-AKT is able to identify the appropriate knowledge transfer crossover for different optimization problems and even at different optimization stages along the search, which thus leads to superior or competitive performances when compared to the MFEAs with fixed knowledge transfer crossover operators.