Abstract:Recently, it was demonstrated that multitasking evolutionary algorithm (MTEA), a newly proposed algorithm, can solve multiple optimization problems simultaneously through a single run, breaking through the limitations of traditional evolutionary algorithms (EAs), with good convergence and exploration performance. As a novel algorithm, MTEA still has a lot of unexplored space. Generally speaking, the order of solution variables has no significant influence on the single-tasking EAs. To our knowledge, the effect… Show more
“…Wang et al analyzed the influence of the order of decision variables on single-task optimization (STO) and MTO problems, respectively. In addition, three orders of decision variables were proposed in [146,147]: full reverse order, bisection reverse order, and trisection reverse order. An important feature of these orders of decision variables is that an individual can recover as himself after two times of changing the order of decision variables.…”
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.
“…Wang et al analyzed the influence of the order of decision variables on single-task optimization (STO) and MTO problems, respectively. In addition, three orders of decision variables were proposed in [146,147]: full reverse order, bisection reverse order, and trisection reverse order. An important feature of these orders of decision variables is that an individual can recover as himself after two times of changing the order of decision variables.…”
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.
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