Multitasking differential evolution with difference vector sharing mechanism

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI:10.1109/SSCI44817.2019.9002698

Yiqiao Cai, Deining Peng, Shunkai Fu, H. Tian

引用次数: 8

Abstract

As a new emerging research topic in the field of evolutionary computation, evolutionary multitasking optimization (EMTO) is presented to solve multiple optimization tasks concurrently by transferring knowledge across them. However, the promising search directions found during the evolutionary process have not been shared and utilized effectively in most EMTO algorithms. Therefore, this paper puts forward a difference vector sharing mechanism (DVSM) for multitasking differential evolution (MDE), with the purpose of capturing, sharing and utilizing the useful knowledge across different tasks. The performance of the proposed algorithm, named MDE with DVSM (MDE-DVSM), is evaluated on a suite of single-objective multitasking benchmark problems. The experimental results have demonstrated the superiority of MDE-DVSM when compared with other competitive algorithms.

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基于差分向量共享机制的多任务差分演化

进化多任务优化(EMTO)是进化计算领域中一个新兴的研究课题，它通过知识的传递来同时解决多个优化任务。然而，在演化过程中发现的有希望的搜索方向在大多数EMTO算法中并没有得到有效的共享和利用。为此，本文提出了一种多任务差分进化(MDE)的差分向量共享机制(DVSM)，以捕获、共享和利用不同任务间的有用知识。在一组单目标多任务基准问题上对该算法的性能进行了评估。实验结果表明，与其他竞争算法相比，MDE-DVSM具有优势。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2019 IEEE Symposium Series on Computational Intelligence (SSCI)

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