Self-adaptive weight vector adjustment strategy for decomposition-based multi-objective differential evolution algorithm

In multi-objective and many-objective optimization, weight vectors are particularly crucial to the performance of decomposition-based optimization algorithms. The uniform weight vectors are not suitable for complex Pareto fronts (PFs), so it is necessary to improve the distribution of weight vectors...

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Veröffentlicht in:	Soft computing (Berlin, Germany) Jg. 24; H. 17; S. 13179 - 13195
Hauptverfasser:	Fan, Rui, Wei, Lixin, Li, Xin, Zhang, Jinlu, Fan, Zheng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2020 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Computational Intelligence Control Convergence Decomposition Engineering Evolutionary algorithms Evolutionary computation Mathematical Logic and Foundations Mechatronics Methodologies and Application Multiple objective analysis Optimization Pareto optimization Robotics Evolutionary computations Multi-objective optimization Decomposition Many-objective optimization Weight vector
ISSN:	1432-7643, 1433-7479
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Abstract	In multi-objective and many-objective optimization, weight vectors are particularly crucial to the performance of decomposition-based optimization algorithms. The uniform weight vectors are not suitable for complex Pareto fronts (PFs), so it is necessary to improve the distribution of weight vectors. Besides, the balance between convergence and diversity is a difficult issue as well in multi-objective optimization, and it becomes increasingly important with the augment of the number of objectives. To address these issues, a self-adaptive weight vector adjustment strategy for decomposition-based multi-objective differential evolution algorithm (AWDMODE) is proposed. In order to ensure that the guidance of weight vectors becomes accurate and effective, the adaptive adjustment strategy is introduced. This strategy distinguishes the shapes and adjusts weight vectors dynamically, which can ensure that the guidance of weight vectors becomes accurate and effective. In addition, a self-learning strategy is adopted to produce more non-dominated solutions and balance the convergence and diversity. The experimental results indicate that AWDMODE outperforms the compared algorithms on WFG suites test instances, and shows a great potential when handling the problems whose PFs are scaled with different ranges in each objective.
AbstractList	In multi-objective and many-objective optimization, weight vectors are particularly crucial to the performance of decomposition-based optimization algorithms. The uniform weight vectors are not suitable for complex Pareto fronts (PFs), so it is necessary to improve the distribution of weight vectors. Besides, the balance between convergence and diversity is a difficult issue as well in multi-objective optimization, and it becomes increasingly important with the augment of the number of objectives. To address these issues, a self-adaptive weight vector adjustment strategy for decomposition-based multi-objective differential evolution algorithm (AWDMODE) is proposed. In order to ensure that the guidance of weight vectors becomes accurate and effective, the adaptive adjustment strategy is introduced. This strategy distinguishes the shapes and adjusts weight vectors dynamically, which can ensure that the guidance of weight vectors becomes accurate and effective. In addition, a self-learning strategy is adopted to produce more non-dominated solutions and balance the convergence and diversity. The experimental results indicate that AWDMODE outperforms the compared algorithms on WFG suites test instances, and shows a great potential when handling the problems whose PFs are scaled with different ranges in each objective.
Author	Fan, Rui Zhang, Jinlu Li, Xin Wei, Lixin Fan, Zheng
Author_xml	– sequence: 1 givenname: Rui surname: Fan fullname: Fan, Rui organization: Institute of Electrical Engineering, Yanshan University – sequence: 2 givenname: Lixin orcidid: 0000-0002-4520-3069 surname: Wei fullname: Wei, Lixin email: wlx2000@ysu.edu.cn organization: Institute of Electrical Engineering, Yanshan University – sequence: 3 givenname: Xin surname: Li fullname: Li, Xin organization: Faculty of Information Technology, Beijing University of Technology – sequence: 4 givenname: Jinlu surname: Zhang fullname: Zhang, Jinlu organization: Institute of Electrical Engineering, Yanshan University – sequence: 5 givenname: Zheng surname: Fan fullname: Fan, Zheng organization: Luoyang Ruize Petrochemical Engineering Co., Ltd
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Cites_doi	10.1007/s00500-017-2840-z 10.1016/j.engappai.2014.01.016 10.1109/4235.797969 10.1109/TEVC.2013.2281535 10.1109/TII.2015.2485520 10.1109/TEVC.2008.925798 10.1007/s00500-015-1830-2 10.3233/JIFS-171291 10.1016/j.ejor.2014.05.019 10.1109/TEVC.2014.2373386 10.1007/s11831-019-09380-6 10.1109/TEVC.2013.2293776 10.1162/EVCO_a_00009 10.1109/4235.996017 10.1016/j.swevo.2017.01.002 10.1109/TCYB.2015.2403131 10.1109/TEVC.2015.2443001 10.1016/j.asoc.2017.03.041 10.1016/j.neucom.2016.10.014 10.1109/TEVC.2016.2587749 10.1109/TCYB.2014.2317693 10.1109/TEVC.2007.892759 10.1109/TEVC.2005.861417 10.1109/ACCESS.2018.2812701 10.1007/s00521-019-04660-5 10.1109/TEVC.2016.2519378 10.1016/j.asoc.2016.11.009 10.1162/EVCO_a_00109 10.1109/TEVC.2007.910138 10.1016/j.asoc.2017.04.002 10.1109/CEC.2015.7257282 10.1109/ICNC.2011.6022367 10.1109/ISME.2010.274 10.1109/WSC.2014.7020200
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Snippet	In multi-objective and many-objective optimization, weight vectors are particularly crucial to the performance of decomposition-based optimization algorithms....
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SubjectTerms	Algorithms Artificial Intelligence Computational Intelligence Control Convergence Decomposition Engineering Evolutionary algorithms Evolutionary computation Mathematical Logic and Foundations Mechatronics Methodologies and Application Multiple objective analysis Optimization Pareto optimization Robotics
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Title	Self-adaptive weight vector adjustment strategy for decomposition-based multi-objective differential evolution algorithm
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