A Multifactorial Evolutionary Algorithm for Multitasking Under Interval Uncertainties

Jiang, Yi; Bai, Junren; He, Haibo; Zhou, Wei; Yao, Lizhong

doi:10.1109/tevc.2020.2975381

Cited by 56 publications

(15 citation statements)

References 37 publications

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“…Zheng et al [36] attempted to automatically adapt the intensity of cross-domain knowledge transfer based on the relevance between different tasks and then proposed an efficient self-regulated evolutionary multi-task optimization algorithm. Yi et al [37] proposed MFEA for solving problems with interval uncertainties. Zhou et al [38] studied the adaptive configuration of the crossover operator for knowledge transfer in MFEA and obtained promising results.…”

Section: Related Workmentioning

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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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%

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

Zhan

Tan

et al. 2022

IEEE Trans. Evol. Computat.

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“…Yi et al [44] discovered mathematically that the proposed interval dominance method has a strict transitive relation to the original method when γ = 0.5 and can be applied when comparing the dominance relationship between interval values.…”

Section: Theoretical Analyses Of Multi-task Evolutionary Computationmentioning

confidence: 99%

“…The MFEA algorithm was extended to solve the interval MTO problem under uncertainty conditions [44]. In the proposed method, an interval crowding distance based on shape evaluation is calculated to evaluate the interval solutions more comprehensively.…”

Section: Multi-task Optimization Under Uncertaintiesmentioning

confidence: 99%

“…Thanks to the effectiveness of MTEC algorithms, they have been successfully applied to tackle other real-world problems in the literature, such as mobile robot path planning [44,107,108], search-based software test data generation [139], the cloud computing service composition problem [74,179], HIV-1 protease cleavage sites prediction [180], and the double-pole balancing problem [61][62][63].…”

Section: Othersmentioning

confidence: 99%

“…, MFEA-AKT[148], MFDE[149], MFEA/DE-OBL[160] Multiobjective optimization problem (MOOP) EMT/ET[19], None[21], MFEA-GHS[22], AdaMOMFDE[23], MO-MFEA[38], AMTEA[43], IMFEA[44], MO-MFEA-II [79], GDE-MO-MFEA [81], MM-DE [84], MTO-FWA [96], EMTIL [106], TEMO-MPS [109], MOMFEA-SADE [115], EMT-LTR [117], TMO-MFEA [120], RPB-MO-MFEA[124], MFEA/D-DRA[128], MaTDE[141], MFEA-AKT[148], NSGAII+M[158], MO-MFEA/HELP TASK[159], MFEA/D[161], MFEA/D-M2M-SVM[162] …”

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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 Investigation of Multitask Linear Genetic Programming for Dynamic Job Shop Scheduling

Huang

Zhang

Mei

et al. 2022

Lecture Notes in Computer Science

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A Multifactorial Evolutionary Algorithm for Multitasking Under Interval Uncertainties

Cited by 56 publications

References 37 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 Investigation of Multitask Linear Genetic Programming for Dynamic Job Shop Scheduling

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