Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review

Qingzheng Xu, Na Wang, Lei Wang, Wei Li and Qian Sun
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Qingzheng Xu: College of Information and Communication, National University of Defense Technology, Xi’an 710106, China
Na Wang: College of Information and Communication, National University of Defense Technology, Xi’an 710106, China
Lei Wang: School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong 723001, China
Wei Li: School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
Qian Sun: School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China

Mathematics, 2021, vol. 9, issue 8, 1-44

Abstract: Traditional evolution algorithms tend to start the search from scratch. However, real-world problems seldom exist in isolation and humans effectively manage and execute multiple tasks at the same time. Inspired by this concept, the paradigm of multi-task evolutionary computation (MTEC) has recently emerged as an effective means of facilitating implicit or explicit knowledge transfer across optimization tasks, thereby potentially accelerating convergence and improving the quality of solutions for multi-task optimization problems. An increasing number of works have thus been proposed since 2016. The authors collect the abundant specialized literature related to this novel optimization paradigm that was published in the past five years. The quantity of papers, the nationality of authors, and the important professional publications are analyzed by a statistical method. As a survey on state-of-the-art of research on this topic, this review article covers basic concepts, theoretical foundation, basic implementation approaches of MTEC, related extension issues of MTEC, and typical application fields in science and engineering. In particular, several approaches of chromosome encoding and decoding, intro-population reproduction, inter-population reproduction, and evaluation and selection are reviewed when developing an effective MTEC algorithm. A number of open challenges to date, along with promising directions that can be undertaken to help move it forward in the future, are also discussed according to the current state. The principal purpose is to provide a comprehensive review and examination of MTEC for researchers in this community, as well as promote more practitioners working in the related fields to be involved in this fascinating territory.

Keywords: multi-task optimization; multi-task evolutionary computation; knowledge transfer; evolutionary algorithm; assortative mating; unified search space (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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