Evolutionary Multitasking for Multiobjective Optimization With Subspace Alignment and Adaptive Differential Evolution

Liang, Zhengping; Dong, Hao; Liu, Cheng; Liang, Weiqi; Zhu, Zexuan

doi:10.1109/tcyb.2020.2980888

Cited by 78 publications

(19 citation statements)

References 56 publications

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“…Lin et al [31] explored a more positive transfer by transferring valuable solutions and proposed an algorithm for multi-objective MTOPs. Liang et al [32] incorporated subspace alignment and self-adaptive DE for solving MTOPs. Compared with these algorithms, the MKTDE is novel in that the MKTDE transfers meta-knowledge to enhance optimization results while the above algorithms mainly consider the task-specific knowledge transfer.…”

Section: Related Workmentioning

confidence: 99%

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A Meta-Knowledge Transfer-Based Differential Evolution for Multitask Optimization

Zhan

Tan

et al. 2022

IEEE Trans. Evol. Computat.

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Abstract-Knowledge transfer plays a vastly important role in solving multitask optimization problems (MTOPs). Many existing methods transfer task-specific knowledge such as the high-quality solution from one task to other tasks to enhance the optimization ability, which however may not work well or even have a negative effect if the tasks have very different task-specific knowledge. Hence, this paper proposes a meta-knowledge transfer-based differential evolution (MKTDE) algorithm by using a more general meta-knowledge transfer (MKT) method to solve MTOPs more efficiently. The meta-knowledge defined in this paper refers to the knowledge that can evolve task-specific knowledge during the evolutionary search. That is, the meta-knowledge is a kind of "knowledge of knowledge", which denotes the knowledge of "how to solve problem via evolution" and "the feature of evolving high-quality solution". The evolutionary search for solving different tasks can share common meta-knowledge even though these tasks involve heterogeneous data and have very different task-specific knowledge. Therefore, the MKT can associate the heterogeneous multi-source data of different tasks via transferring the meta-knowledge to help solve MTOPs more efficiently in a more general way. Moreover, to further enhance the MKTDE, two novel and efficient methods are proposed. One is a multiple populations for multiple tasks framework using a unified search space for making knowledge transfer flexibly. The other is an elite solution transfer method for achieving positive high-quality solution transfer. The superior performance of the proposed MKTDE is verified via extensive numerical experiments on both widely-used MTOP benchmark problems and real-world robot navigation problems, with comparisons with some state-of-the-art and the latest well-performing algorithms.

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Section: Related Workmentioning

confidence: 99%

“…For another example, Jin et al [30] studied the knowledge reuse among different populations for solving MTOPs more efficiently. Besides, other knowledge transfer methods between multiple populations have also been studied to enhance EMTO [31] [32].…”

Section: Introductionmentioning

confidence: 99%

A Meta-Knowledge Transfer-Based Differential Evolution for Multitask Optimization

Zhan

Tan

et al. 2022

IEEE Trans. Evol. Computat.

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“…It was very recently determined that a novel search space mapping mechanism, namely, subspace alignment (SA) could enable efficient and high-quality knowledge transfer among different tasks [115]. In particular, the SA strategy establishes the connection between two tasks using two transforming matrices, which can reduce the probability of negative transfer.…”

Section: How To Knowledge Transfer Explicitlymentioning

confidence: 99%

“…Applying different mutation operators on current population can generate different search directions and offspring populations. Multiple commonly-used mutation strategies (DE/rand/1, DE/best/1, DE/current-to-rand/1, DE/current-to-best/1, DE/rand/2, DE/best/2, and DE/best/1 + ρ) were investigated to accelerate the convergence speed in [23,115,149], where DE/best/1 + ρ is defined as follows:…”

Section: Adaptive Operator Selection Strategymentioning

confidence: 99%

“…Its rationale is that the current-found best solution is utilized adequately to guide the search to promising areas in the early phase, while an increased perturbation is also integrated subsequently for a diverse exploration [149]. Note that we selected the suitable mutation strategy randomly in [115] or adaptively according to their success rates in previous generations in [23].…”

Section: Adaptive Operator Selection Strategymentioning

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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An Improved Teaching-Learning-Based Optimization for Multitask Optimization Problems

Guo

Zou

Chen

et al. 2021

Intelligent Computing Theories and Application

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Evolutionary Multitasking for Multiobjective Optimization With Subspace Alignment and Adaptive Differential Evolution

Cited by 78 publications

References 56 publications

A Meta-Knowledge Transfer-Based Differential Evolution for Multitask Optimization

A Meta-Knowledge Transfer-Based Differential Evolution for Multitask Optimization

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

An Improved Teaching-Learning-Based Optimization for Multitask Optimization Problems

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