Multiobjective Multitasking Optimization Based on Incremental Learning

Lin, Jiabin; Liu, Hai Lin; Xue, Bing; Zhang, Mengjie; Gu, Fangqing

doi:10.1109/tevc.2019.2962747

Cited by 76 publications

(24 citation statements)

References 35 publications

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“…More recently, Lin et al [106] have utilized incremental Naive Bayes classifiers to select valuable solutions to be transferred during multi-task search, thus leading to the promising convergence of tasks. Furthermore, under the existing mapping strategies, tasks may be trapped in local Pareto Fronts with the guide of knowledge transfer.…”

Section: What To Transfermentioning

confidence: 99%

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

Wang

et al. 2021

Mathematics

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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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Section: What To Transfermentioning

confidence: 99%

“…Under the existing mapping strategies, tasks may be trapped in local Pareto Fronts with the guide of the knowledge transfer. Thus, with the aim of improving overall convergence behavior, a randomized mapping among tasks was added as follows, that enhances the exploration capacity of transferred solutions [106].…”

Section: How To Knowledge Transfer Implicitlymentioning

confidence: 99%

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

Wang

et al. 2021

Mathematics

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“…In addition, the improved crossover operator and search mechanism can be combined with a MTEA prejudice to improve the algorithm performance [35]. At the same time, the idea of machine learning can be well combined with multitasking optimization [36,37].…”

Section: Related Work On Mteamentioning

confidence: 99%

Analysis of Multitasking Evolutionary Algorithms under the Order of Solution Variables

Wang

Sun

et al. 2020

Complexity

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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 of the order of variables in the multitasking scenario has not been explored. To fill in this research gap, three orders of variables in the multitasking scenario are proposed in this paper, including full reverse order, bisection reverse order, and trisection reverse order. An important feature of these orders of variables is that an individual can recover as himself after two times of changing the order of variables. In order to verify our idea, these orders of variables are embedded into MTEA. The experiment results revealed that the effect of the different orders of variables is universal but not significant enough in the practical application. Furthermore, tasks with high similarity and high degree of intersection are sensitive to the order of variables and get great impact between tasks.

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“…Thereafter, many multiobjective EMT algorithms have been proposed. According to the way of knowledge transfer, these algorithms can be divided into the following three categories: (1) adaptive knowledge transfer strategy [9][10][11][12]; (2) search space mapping strategy [13][14][15][16][17]; (3) valuable knowledge selection strategy [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective multitasking optimization assisted by multidirectional prediction method

Dang

Gao

Gong

2022

Complex Intell. Syst.

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Multiobjective multitasking optimization (MTO) is an emerging research topic in the field of evolutionary computation, which has attracted extensive attention, and many evolutionary multitasking (EMT) algorithms have been proposed. One of the core issues, designing an efficient transfer strategy, has been scarcely explored. Keeping this in mind, this paper is the first attempt to design an efficient transfer strategy based on multidirectional prediction method. Specifically, the population is divided into multiple classes by the binary clustering method, and the representative point of each class is calculated. Then, an effective prediction direction method is developed to generate multiple prediction directions by representative points. Afterward, a mutation strength adaptation method is proposed according to the improvement degree of each class. Finally, the predictive transferred solutions are generated as transfer knowledge by the prediction directions and mutation strengths. By the above process, a multiobjective EMT algorithm based on multidirectional prediction method is presented. Experiments on two MTO test suits indicate that the proposed algorithm is effective and competitive to other state-of-the-art EMT algorithms.

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Multiobjective Multitasking Optimization Based on Incremental Learning

Cited by 76 publications

References 35 publications

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

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

Analysis of Multitasking Evolutionary Algorithms under the Order of Solution Variables

Multiobjective multitasking optimization assisted by multidirectional prediction method

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