research-article

MOEA/D with the online agglomerative clustering based self-adaptive mating restriction strategy

Authors:

Shenmin SongAuthors Info & Claims

Volume 339, Issue C

Pages 77 - 93

https://doi.org/10.1016/j.neucom.2019.02.002

Published: 28 April 2019 Publication History

Abstract

In MOEA/D-DE, the appropriate value of the mating restriction probability varies with the evolutionary process. Furthermore, different subproblems have been solved in different degree during the evolution, so different subproblems have distinct requirements for exploitation and exploration. Additionally, MOEA/D-DE defines the neighborhood according to the distance between the weight vectors. However, the individuals corresponding to the neighbor subproblems may distribute far away in the decision space, which will affect the performance of exploitation. Accordingly, this paper proposes a MOEA/D with the online agglomerative clustering based self-adaptive mating restriction strategy (MOEA/D-OMR). MOEA/D-OMR utilizes the online agglomerative clustering algorithm to extract the neighborhood information in the decision space. The mating pool is then constructed by the neighbor population or the whole population based on the mating restriction probability. What is more, a separate mating restriction probability is assigned to each subproblem. The mating restriction probability is updated at each generation by the survival length, which is the number of generations that the solution has survived over the last certain period of time. Experimental results show that MOEA/D-OMR has a better performance than the comparison algorithms.

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Cited By

Zheng WSun J(2022)Two-stage hybrid learning-based multi-objective evolutionary algorithm based on objective space decompositionInformation Sciences: an International Journal10.1016/j.ins.2022.08.030610:C(1163-1186)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.ins.2022.08.030
Zhang WWang SZhou AZhang H(2022)A practical regularity model based evolutionary algorithm for multiobjective optimizationApplied Soft Computing10.1016/j.asoc.2022.109614129:COnline publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.asoc.2022.109614
Liu TTan LLi XSong S(2022)Incremental learning-inspired mating restriction strategy for Evolutionary Multiobjective OptimizationApplied Soft Computing10.1016/j.asoc.2022.109430127:COnline publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.asoc.2022.109430
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Index Terms

MOEA/D with the online agglomerative clustering based self-adaptive mating restriction strategy
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
  2. Machine learning
    1. Learning paradigms
2. Theory of computation
  1. Design and analysis of algorithms

Index terms have been assigned to the content through auto-classification.

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cover image Neurocomputing

Neurocomputing Volume 339, Issue C

Apr 2019

304 pages

ISSN:0925-2312

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Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

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Published: 28 April 2019

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Zheng WSun J(2022)Two-stage hybrid learning-based multi-objective evolutionary algorithm based on objective space decompositionInformation Sciences: an International Journal10.1016/j.ins.2022.08.030610:C(1163-1186)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.ins.2022.08.030
Zhang WWang SZhou AZhang H(2022)A practical regularity model based evolutionary algorithm for multiobjective optimizationApplied Soft Computing10.1016/j.asoc.2022.109614129:COnline publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.asoc.2022.109614
Liu TTan LLi XSong S(2022)Incremental learning-inspired mating restriction strategy for Evolutionary Multiobjective OptimizationApplied Soft Computing10.1016/j.asoc.2022.109430127:COnline publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.asoc.2022.109430
Liu TSong SLi XTan L(2021)Approximating Pareto Optimal Set by An Incremental Learning Model2021 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC45853.2021.9504996(169-176)Online publication date: 28-Jun-2021
https://dl.acm.org/doi/10.1109/CEC45853.2021.9504996

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