A Group-based Approach to Improve Multifactorial Evolutionary Algorithm

Tang, Jing; Chen, Yingke; Deng, Zixuan; Xiang, Yang; Joy, Colin Paul

doi:10.24963/ijcai.2018/538

Cited by 63 publications

(17 citation statements)

References 13 publications

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“…However, population diversity is necessary when it becomes a bottleneck against the genetic transfer. In [130], Tang et al proposed a new selection criterion keeping a balance between individual fitness and population diversity as follows:…”

Section: Evaluation and Selection Strategymentioning

confidence: 99%

“…where r k i is the rank of individual x i in task T k . Moreover, Tang et al [130] proposed a group-based MFEA by clustering the similar tasks (tasks with near global optima) and dispersing the dissimilar tasks. More importantly, the genetic materials can only be transferred within the same groups so that negative genetic transfers are eliminated.…”

Section: Many-task Optimization Problemmentioning

confidence: 99%

“…None [114], AT-MFEA [116], EBS-CMAES [122], EBSFA-CMAES [123], SREMTO [125], MTO-DRA [127], AMA [129], GMFEA [130], mMTDE [131], MTDE [132], TLTLA [133], MFEA [137], MaTDE [141], None [142], MFEA-VT [143,144], HD-MFEA [145], MFEA-FuR [146,147] Vehicle routing problem (VRP)…”

Section: Applications Of Multi-task Evolutionary Computationmentioning

confidence: 99%

“…MFEA [11], MFEA [18], MFEA-Perm-LBS [53], MTO-DRA [127] Knapsack problem (KP) MFEA [18], AMTEA [43] Sudoku puzzles MFEA [48], GMFEA [130] Travel salesman problem (TSP)…”

Section: Applications Of Multi-task Evolutionary Computationmentioning

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: Evaluation and Selection Strategymentioning

confidence: 99%

Section: Many-task Optimization Problemmentioning

confidence: 99%

Section: Applications Of Multi-task Evolutionary Computationmentioning

confidence: 99%

“…MFEA [11], MFEA [18], MFEA-Perm-LBS [53], MTO-DRA [127] Knapsack problem (KP) MFEA [18], AMTEA [43] Sudoku puzzles MFEA [48], GMFEA [130] Travel salesman problem (TSP)…”

Section: Applications Of Multi-task Evolutionary Computationmentioning

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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“…Chen et al [19] employed the cooperative co-evolutionary mechanism and proposed an EMTO algorithm called EMTSO-CCMA for high-dimensional optimization problems. Tang et al [20] proposed a group-based MFEA that groups tasks of similar types and selectively transfers the genetic information only within the groups. Bali et al [21] proposed a linearized domain adaptation (LDA) strategy that transforms the search space of a simple task to the search space similar to its constitutive complex task.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Task Optimization Algorithm Based on the Brainstorming Process

2020

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Evolutionary multi-task optimization (EMTO) is an emerging research topic in the field of evolutionary computation, which aims to simultaneously optimize several component tasks within a problem and output the best solution for each task. Since EMTO has widespread applications in solving real-world multi-task optimization problems, in recent years, some EMTO algorithms have been proposed. However, most of which are based on the multifactorial evolution framework which has difficulties in independently controlling the optimization of each component task and implementing parallel computing. To tackle this problem and enrich the EMTO algorithms' family, this paper firstly designs a novel EMTO framework inspired by the brainstorming process of human beings when they solve multi-task problems. Under this framework, a novel EMTO algorithm, named as brain storm multi-task optimization (BSMTO), is presented, where the optimization for each component task and the knowledge transfer between different tasks are both implemented by the proposed brainstorming operations. Furthermore, through investigating the knowledge transfer process in the proposed algorithm, an enhanced BSMTO algorithm named as BSMTO-II is further proposed, where the knowledge transfer in each component task can be managed and controlled by our newly designed scheme. Finally, the proposed two algorithms are tested on benchmark problems. Experimental results show that BSMTO-II has a competitive performance compared with both classical and state-of-the-art algorithms. Moreover, the effectiveness of the proposed EMTO framework and the knowledge transfer control scheme is proved through experiments, and the key parameters settings and the algorithmic complexity are also discussed at last.

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