Adaptive decomposition-based evolutionary algorithm for many-objective optimization with two-stage dual-density judgment

In order to better balance the convergence and diversity of MOEA/D for many objective optimization problems (MaOPs) with various Pareto fronts (PFs), an adaptive decomposition-based evolutionary algorithm for MaOPs with two-stage dual-density judgment is proposed. To solve the problem that weighted...

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Veröffentlicht in:Applied soft computing Jg. 167; S. 112434
Hauptverfasser: Sun, Yongjun, Liu, Jiaqi, Liu, Zujun
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Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.12.2024
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Abstract In order to better balance the convergence and diversity of MOEA/D for many objective optimization problems (MaOPs) with various Pareto fronts (PFs), an adaptive decomposition-based evolutionary algorithm for MaOPs with two-stage dual-density judgment is proposed. To solve the problem that weighted Tchebycheff decomposition may produce weakly Pareto optimal solutions when the solution is not unique or the uniqueness is difficult to guarantee, an augmented weighted Tchebycheff decomposition is adopted. To balance the convergence and diversity of non-dominated solutions in the external archive, different sparsity-level evaluations using vector angles or Euclidean distances are used to measure the distribution of solutions at different stages. To improve the diversity of solution sets obtained by MOEA/D for various PFs, an adaptive weight vector adjustment method based on two-stage dual-density judgment is presented. For weight vector addition, the potential search area is found according to the two-stage density judgment, and then a two-stage sparsity level judgment on the solutions of this area is performed for a second density judgment. For weight vector deletion, the degree of crowding is used to delete the weight vectors with a high crowding degree. Compared with nine advanced multi-objective optimization algorithms on DTLZ and WFG problems, the results demonstrate that the performance of the proposed algorithm is significantly better than other algorithms. •An augmented weighted Tchebycheff decomposition is adopted to improve convergence.•A two-stage sparsity level judgment is used to maintain the diversity and convergence of non-dominated solutions.•A two-stage dual-density judgment adaptive weight vector adjustment method is proposed.•Compare with 9 latest algorithms to prove the effectiveness of the proposed algorithm.
AbstractList In order to better balance the convergence and diversity of MOEA/D for many objective optimization problems (MaOPs) with various Pareto fronts (PFs), an adaptive decomposition-based evolutionary algorithm for MaOPs with two-stage dual-density judgment is proposed. To solve the problem that weighted Tchebycheff decomposition may produce weakly Pareto optimal solutions when the solution is not unique or the uniqueness is difficult to guarantee, an augmented weighted Tchebycheff decomposition is adopted. To balance the convergence and diversity of non-dominated solutions in the external archive, different sparsity-level evaluations using vector angles or Euclidean distances are used to measure the distribution of solutions at different stages. To improve the diversity of solution sets obtained by MOEA/D for various PFs, an adaptive weight vector adjustment method based on two-stage dual-density judgment is presented. For weight vector addition, the potential search area is found according to the two-stage density judgment, and then a two-stage sparsity level judgment on the solutions of this area is performed for a second density judgment. For weight vector deletion, the degree of crowding is used to delete the weight vectors with a high crowding degree. Compared with nine advanced multi-objective optimization algorithms on DTLZ and WFG problems, the results demonstrate that the performance of the proposed algorithm is significantly better than other algorithms. •An augmented weighted Tchebycheff decomposition is adopted to improve convergence.•A two-stage sparsity level judgment is used to maintain the diversity and convergence of non-dominated solutions.•A two-stage dual-density judgment adaptive weight vector adjustment method is proposed.•Compare with 9 latest algorithms to prove the effectiveness of the proposed algorithm.
ArticleNumber 112434
Author Sun, Yongjun
Liu, Zujun
Liu, Jiaqi
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Cites_doi 10.1109/TEVC.2017.2737781
10.1109/TCYB.2016.2638902
10.1007/s00500-017-2609-4
10.1162/EVCO_a_00109
10.1109/TEVC.2002.802873
10.1109/TEVC.2017.2749619
10.1109/TCYB.2017.2737554
10.1007/BF02591870
10.1145/2576768.2598297
10.1109/MCI.2017.2742868
10.1016/j.asoc.2023.111006
10.1109/TCYB.2014.2365354
10.1007/978-3-030-53956-6_41
10.1016/j.swevo.2017.01.002
10.1109/TEVC.2016.2587808
10.1007/s10732-015-9301-6
10.1109/TCYB.2017.2779450
10.1109/TEVC.2016.2521175
10.1007/s00500-015-1736-z
10.1016/j.ejor.2014.05.019
10.1109/TEVC.2005.861417
10.1016/j.asoc.2022.109412
10.1109/TEVC.2014.2373386
10.1007/978-3-319-13563-2_24
10.1007/978-3-030-12598-1_18
10.1109/TEVC.2015.2424251
10.1109/TEVC.2015.2504730
10.1162/EVCO_a_00009
10.1109/TEVC.2013.2293776
10.1109/TEVC.2007.892759
10.1162/evco_a_00269
10.1109/TEVC.2013.2262178
10.1109/TEVC.2015.2457616
10.1016/j.asoc.2023.110095
10.1007/s10489-017-0927-y
10.1016/j.ins.2019.05.083
10.1109/TEVC.2018.2874465
10.1109/TEVC.2013.2281535
10.1109/TEVC.2018.2865590
10.1109/4235.996017
10.1109/TEVC.2016.2598687
10.1007/s10898-014-0214-y
10.1109/TEVC.2013.2281534
10.1109/TCYB.2020.3020630
10.1109/TEVC.2016.2519378
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ISSN 1568-4946
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Tue Nov 18 21:24:00 EST 2025
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Keywords Dual-density judgment
Adaptive weight vector adjustment
Many-objective optimization
Two-stage strategy
Language English
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References Sato (bib10) 2014
Jain, Deb (bib33) 2014; 18
Liu, Ishibuchi, Masuyama, Nojima (bib67) 2020; 24
Farias, Araujol (bib41) 2019
Peng, Wolter (bib59) 2020
Liu, Jin, Heiderich, Rodemann, Yu (bib68) 2022; 52
Wang, Tao, Deng (bib17) 2020
H. Sato, 2014, Inverted PBI in MOEA/D and its impact on the search performance on multi and many-objective optimization, in Proc.Conf. Genet. Evol. Comput. (GECCO), V ancouver, BC, Canada, 2014,pp. 645–652, https://doi.org/10.1145/2576768.2598297.
Xiang, Zhou, Li, Chen (bib55) 2017; 21
Chen, Shi, Zhou (bib19) 2019
Chen, Yin, Yu, Fan (bib37) 2022; 128
Zhang, Tan, Lee, Gao (bib47) 2018; 22
Wang, Li, Li, Zhang (bib39) 2023; 149
Zitzler, Knowles, Thiele (bib70) 2008
Wang, Li, Tao, Gu (bib15) 2020
Zou, Liu, Yang, Zheng (bib66) 2021; 60
Zhang, Li (bib9) 2007; 11
Liu, Jin, Heiderich (bib36) 2019
He, Yen (bib61) 2017; 21
Kalyanmoy (bib1) 2001
Jaszkiewicz (bib43) 2002; 6
Zhou, Yao (bib22) 2019; 501
Sato (bib13) 2015; 21
Ge, Zhao (bib48) 2019; 23
Deb, Thiele, Laumanns, Zitzler (bib62) 2002
Tian, Cheng, Zhang, Cheng, Jin (bib5) 2018; 22
Li, Kwong, Zhang, Deb (bib6) 2015; 45
Saborido, Ruiz, Luque (bib30) 2016; 8
Steuer, Choo (bib58) 1983; 26
Li, Deb, Zhang, Kwong (bib64) 2015; 19
Li, Deb, Yao (bib50) 2018; 22
Zhou, Yao (bib54) 2017; 47
Wang, Purshouse, Fleming (bib44) 2015; 243
Wang, Zhang, Zhou (bib27) 2016; 20
Li, Ding, Deng, Zhang (bib45) 2015
Huband, Hingston, Barone, While (bib63) 2006; 10
Zhou, Zhang (bib20) 2016; 20
Deb, Jain (bib2) 2014; 18
Cai, Mei, Fan (bib23) 2018; 48
Yu, Zheng, Shen, Li (bib52) 2016; 20
Liu, Zhu, Li (bib60) 2019; 509
Gu, Cheung, Liu, Lin (bib46) 2018
Jiang, Yang (bib11) 2016; 46
Trivedi, Srinivasan, Sanyal, Ghosh (bib7) 2017; 21
Wang, Zhang, Zhang (bib31) 2016; 20
Xu, Zeng, Zhang, Zeng (bib49) 2019; 49
ang, Jiang, Jiang (bib14) 2016
Li, Yao (bib42) 2020; 28
Wang, Zhang, Zhang (bib32) 2015
Ma, Yu, Li (bib21) 2020
Li, Yang, Liu (bib56) 2014; 18
ang, Jiang, Jiang (bib24) 2016
Cheng, Jin, Olhofer (bib35) 2016; 20
Bader, Zitzler (bib4) 2011; 19
Deb, Pratap, Agarwal, Meyarivan (bib3) 2002; 6
He, Zhou, Chen (bib65) 2018; 23
Qi, Ma, Liu, Jiao, Sun, Wu (bib40) 2014; 22
Li, Zhang, Song (bib12) 2019
Ruiz, Saborido, Luque (bib29) 2015; 62
Meneghini, Guimarães (bib51) 2017
Tian, Cheng, Zhang, Jin (bib69) 2017; 12
Xu, Zhang (bib38) 2023; 136
Akhmedova, Stanovov (bib18) 2020
Wang, Zhang, Li, Ishibuchi, Jiao (bib28) 2017; 34
Liu, Gong, Sun, Jin (bib53) 2017; 47
Giagkiozis, Purshouse, Fleming (bib25) 2013
Chen, Li, Wen (bib16) 2019
Camacho, Toscano, Becerra, Ishibuchi (bib34) 2019
Li, Zhang, Kwong, Li, Wang (bib8) 2014; 18
Li, Yang, Liu (bib57) 2016; 20
Li (10.1016/j.asoc.2024.112434_bib8) 2014; 18
Chen (10.1016/j.asoc.2024.112434_bib16) 2019
Zitzler (10.1016/j.asoc.2024.112434_bib70) 2008
Liu (10.1016/j.asoc.2024.112434_bib53) 2017; 47
Zou (10.1016/j.asoc.2024.112434_bib66) 2021; 60
Gu (10.1016/j.asoc.2024.112434_bib46) 2018
Meneghini (10.1016/j.asoc.2024.112434_bib51) 2017
Li (10.1016/j.asoc.2024.112434_bib42) 2020; 28
Deb (10.1016/j.asoc.2024.112434_bib62) 2002
He (10.1016/j.asoc.2024.112434_bib65) 2018; 23
Farias (10.1016/j.asoc.2024.112434_bib41) 2019
Yu (10.1016/j.asoc.2024.112434_bib52) 2016; 20
Huband (10.1016/j.asoc.2024.112434_bib63) 2006; 10
Chen (10.1016/j.asoc.2024.112434_bib37) 2022; 128
Jain (10.1016/j.asoc.2024.112434_bib33) 2014; 18
Saborido (10.1016/j.asoc.2024.112434_bib30) 2016; 8
Steuer (10.1016/j.asoc.2024.112434_bib58) 1983; 26
Trivedi (10.1016/j.asoc.2024.112434_bib7) 2017; 21
Xiang (10.1016/j.asoc.2024.112434_bib55) 2017; 21
Wang (10.1016/j.asoc.2024.112434_bib44) 2015; 243
Zhou (10.1016/j.asoc.2024.112434_bib22) 2019; 501
Akhmedova (10.1016/j.asoc.2024.112434_bib18) 2020
Li (10.1016/j.asoc.2024.112434_bib50) 2018; 22
Wang (10.1016/j.asoc.2024.112434_bib39) 2023; 149
Deb (10.1016/j.asoc.2024.112434_bib3) 2002; 6
Zhang (10.1016/j.asoc.2024.112434_bib9) 2007; 11
Li (10.1016/j.asoc.2024.112434_bib56) 2014; 18
Bader (10.1016/j.asoc.2024.112434_bib4) 2011; 19
Wang (10.1016/j.asoc.2024.112434_bib27) 2016; 20
Li (10.1016/j.asoc.2024.112434_bib6) 2015; 45
Giagkiozis (10.1016/j.asoc.2024.112434_bib25) 2013
Zhou (10.1016/j.asoc.2024.112434_bib20) 2016; 20
Ma (10.1016/j.asoc.2024.112434_bib21) 2020
Li (10.1016/j.asoc.2024.112434_bib12) 2019
Li (10.1016/j.asoc.2024.112434_bib45) 2015
Wang (10.1016/j.asoc.2024.112434_bib17) 2020
Jiang (10.1016/j.asoc.2024.112434_bib11) 2016; 46
Camacho (10.1016/j.asoc.2024.112434_bib34) 2019
Sato (10.1016/j.asoc.2024.112434_bib10) 2014
10.1016/j.asoc.2024.112434_bib26
Xu (10.1016/j.asoc.2024.112434_bib38) 2023; 136
Jaszkiewicz (10.1016/j.asoc.2024.112434_bib43) 2002; 6
ang (10.1016/j.asoc.2024.112434_bib14) 2016
Li (10.1016/j.asoc.2024.112434_bib57) 2016; 20
Peng (10.1016/j.asoc.2024.112434_bib59) 2020
ang (10.1016/j.asoc.2024.112434_bib24) 2016
Ruiz (10.1016/j.asoc.2024.112434_bib29) 2015; 62
Zhou (10.1016/j.asoc.2024.112434_bib54) 2017; 47
Qi (10.1016/j.asoc.2024.112434_bib40) 2014; 22
Tian (10.1016/j.asoc.2024.112434_bib69) 2017; 12
Li (10.1016/j.asoc.2024.112434_bib64) 2015; 19
Liu (10.1016/j.asoc.2024.112434_bib60) 2019; 509
Tian (10.1016/j.asoc.2024.112434_bib5) 2018; 22
Liu (10.1016/j.asoc.2024.112434_bib67) 2020; 24
Cai (10.1016/j.asoc.2024.112434_bib23) 2018; 48
Wang (10.1016/j.asoc.2024.112434_bib28) 2017; 34
Wang (10.1016/j.asoc.2024.112434_bib31) 2016; 20
Wang (10.1016/j.asoc.2024.112434_bib32) 2015
Liu (10.1016/j.asoc.2024.112434_bib36) 2019
Chen (10.1016/j.asoc.2024.112434_bib19) 2019
Cheng (10.1016/j.asoc.2024.112434_bib35) 2016; 20
Ge (10.1016/j.asoc.2024.112434_bib48) 2019; 23
Wang (10.1016/j.asoc.2024.112434_bib15) 2020
Sato (10.1016/j.asoc.2024.112434_bib13) 2015; 21
Xu (10.1016/j.asoc.2024.112434_bib49) 2019; 49
Liu (10.1016/j.asoc.2024.112434_bib68) 2022; 52
Deb (10.1016/j.asoc.2024.112434_bib2) 2014; 18
He (10.1016/j.asoc.2024.112434_bib61) 2017; 21
Kalyanmoy (10.1016/j.asoc.2024.112434_bib1) 2001
Zhang (10.1016/j.asoc.2024.112434_bib47) 2018; 22
References_xml – volume: 501
  start-page: 248
  year: 2019
  end-page: 271
  ident: bib22
  article-title: A decomposition based evolutionary algorithm with direction vector adaption and selection enhancement
  publication-title: Inf. Sci.
– start-page: 615
  year: 2013
  end-page: 620
  ident: bib25
  article-title: Towards understanding the cost of adaptation in decomposition-based optimization algorithms
  publication-title: Systems, Man, and Cybernetics (SMC),2013 IEEE International Conference on
– volume: 19
  start-page: 694
  year: 2015
  end-page: 716
  ident: bib64
  article-title: An evolutionary many-objective optimization algorithm based on dominance and decomposition
  publication-title: IEEE Trans. Evol. Comput.
– volume: 23
  year: 2018
  ident: bib65
  article-title: Evolutionary many-objective optimization based on dynamical decomposition
  publication-title: IEEE Trans. Evolut. Comput.
– volume: 21
  start-page: 220
  year: 2017
  end-page: 233
  ident: bib61
  article-title: Many-objective evolutionary algorithms based on coordinated selection strategy
  publication-title: IEEE Trans. Evolut. Comput.
– start-page: 216
  year: 2019
  end-page: 228
  ident: bib34
  article-title: Indicator based weight adaptation for solving many-objective optimization problems
  publication-title: Proc. Evol. Multi Crit. Optim. (EMO)
– volume: 128
  year: 2022
  ident: bib37
  article-title: A decomposition-based many-objective evolutionary algorithm with adaptive weight vector strategy
  publication-title: Appl. Soft Comput.
– volume: 12
  start-page: 73
  year: 2017
  end-page: 87
  ident: bib69
  article-title: Platemo: a matlab platform for evolutionary multi-objective optimization [educational forum]
  publication-title: IEEE Comput. Intell. Mag.
– volume: 62
  start-page: 101
  year: 2015
  end-page: 129
  ident: bib29
  article-title: A preference-based evolutionary algorithm for multiobjective optimization: The weighting achievement scalarizing function genetic algorithm
  publication-title: J. Glob. Optim.
– start-page: 274
  year: 2014
  end-page: 286
  ident: bib10
  article-title: Adaptive update range of solutions in MOEA/D for multi and many-objective optimization
  publication-title: Simula Evol. Learn.
– start-page: 1
  year: 2016
  end-page: 15
  ident: bib14
  article-title: Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes
  publication-title: Soft Comput.
– volume: 11
  start-page: 712
  year: 2007
  end-page: 731
  ident: bib9
  article-title: Moea/d: A multiobjective evolutionary algorithm based on decomposition
  publication-title: IEEE Trans. Evolut. Comput.
– volume: 20
  start-page: 773
  year: 2016
  end-page: 791
  ident: bib35
  article-title: A reference vector guided evolutionary algorithm for many-objective optimization
  publication-title: IEEE Trans. Evolut. Comput.
– start-page: 1726
  year: 2019
  end-page: 1733
  ident: bib36
  article-title: Adaptation of reference vectors for evolutionary many-objective optimization of problems with irregular pareto fronts
  publication-title: Congr. Evolut. Comput.
– start-page: 978
  year: 2015
  end-page: 985
  ident: bib45
  article-title: On the use of random weights in MOEA/D
  publication-title: Proc. IEEE Congr. Evol. Comput. (CEC)
– year: 2019
  ident: bib41
  article-title: Many-Objective Evolutionary Algorithm Based On Decomposition With Random And Adaptive Weights
  publication-title: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)
– volume: 28
  start-page: 227
  year: 2020
  end-page: 253
  ident: bib42
  article-title: What weights work for you? adapting weights for any pareto front shape in decomposition-based evolutionary multi-objective optimisation
  publication-title: Evolut. Comput.
– volume: 21
  start-page: 440
  year: 2017
  end-page: 462
  ident: bib7
  article-title: A survey of multiobjective evolutionary algorithms based on decomposition
  publication-title: IEEE Trans. Evolut. Comput.
– volume: 18
  start-page: 909
  year: 2014
  end-page: 923
  ident: bib8
  article-title: Stable matching-based selection in evolutionary multiobjective optimization
  publication-title: IEEE Trans. Evol. Comput.
– volume: 6
  start-page: 402
  year: 2002
  end-page: 412
  ident: bib43
  article-title: On the performance of multiple-objective genetic local search on the 0/1 knapsack problem—A comparative experiment
  publication-title: IEEE Trans. Evol. Comput.
– year: 2020
  ident: bib59
  article-title: A Novel Archive Maintenance for Adapting Weight Vectors in Decomposition-based Multi-objective Evolutionary Algorithms
  publication-title: 2020 IEEE Congress on Evolutionary Computation (CEC)
– volume: 45
  start-page: 2076
  year: 2015
  end-page: 2088
  ident: bib6
  article-title: Interrelationship-based selection for decomposition multiobjective optimization
  publication-title: IEEE Trans. Cybern.
– volume: 26
  start-page: 326
  year: 1983
  end-page: 344
  ident: bib58
  article-title: An interactive weighted Tchebycheff procedure for multiple objective programming
  publication-title: Math. Program.
– volume: 22
  start-page: 821
  year: 2018
  end-page: 835
  ident: bib50
  article-title: R-metric: evaluating the performance of preference-based evolutionary multi-objective optimization using reference points
  publication-title: IEEE Trans. Evol. Comput.
– volume: 24
  start-page: 439
  year: 2020
  end-page: 453
  ident: bib67
  article-title: Adapting reference vectors and scalarizing functions by growing neural gas to handle irregular Pareto fronts
  publication-title: IEEE Trans. Evolut. Comput.
– start-page: 842
  year: 2018
  end-page: 845
  ident: bib46
  article-title: A parameterless decomposition-based evolutionary multi-objective algorithm
  publication-title: Proc. Int. Conf. Adv. Comput. Intell.
– volume: 149
  year: 2023
  ident: bib39
  article-title: Multi-objective decomposition evolutionary algorithm with objective modification-based dominance and external archive
  publication-title: Appl. Soft Comput.
– volume: 46
  start-page: 421
  year: 2016
  end-page: 437
  ident: bib11
  article-title: An improved multiobjective optimization evolutionary algorithm based on decomposition for complex pareto fronts
  publication-title: IEEE Trans. Syst., Man, Cybern.
– volume: 23
  start-page: 572
  year: 2019
  end-page: 586
  ident: bib48
  article-title: A many-objective evolutionary algorithm with two interacting processes: Cascade clustering and reference point incremental learning
  publication-title: IEEE Trans. Evol. Comput.
– volume: 10
  start-page: 477
  year: 2006
  end-page: 506
  ident: bib63
  article-title: A review of multiobjective test problems and a scalable test problem toolkit
  publication-title: IEEE Trans. Evol. Comput.
– year: 2020
  ident: bib15
  article-title: An Improved MOEA/D algorithm with an adaptive evolutionary strategy
  publication-title: Inf. Sci.
– volume: 22
  start-page: 231
  year: 2014
  end-page: 264
  ident: bib40
  article-title: Moea/d with adaptive weight adjustment
  publication-title: Evolut. Comput.
– volume: 22
  start-page: 609
  year: 2018
  end-page: 622
  ident: bib5
  article-title: An indicator based multi-objective evolutionary algorithm with reference point adaptation for better versatility
  publication-title: IEEE Trans. Evol. Comput.
– start-page: 77
  year: 2019
  end-page: 93
  ident: bib12
  article-title: MOEA/D with the online agglomerative clustering based self-adaptive mating restriction strategy
  publication-title: Neurocomputing
– start-page: 1900
  year: 2017
  end-page: 1907
  ident: bib51
  article-title: Evolutionary method for weight vector generation in multi-objective evolutionary algorithms based on decomposition and aggregation
  publication-title: Proc. IEEE Congr. Evol. Comput. (CEC)
– volume: 48
  start-page: 2335
  year: 2018
  end-page: 2348
  ident: bib23
  article-title: A decomposition-based many-objective evolutionary algorithm with two types of adjustments for direction vectors
  publication-title: IEEE Trans. Cybern.
– volume: 34
  start-page: 89
  year: 2017
  end-page: 102
  ident: bib28
  article-title: On the use of two reference points in decomposition based multiobjective evolutionary algorithms
  publication-title: Swarm Evol. Comput.
– reference: H. Sato, 2014, Inverted PBI in MOEA/D and its impact on the search performance on multi and many-objective optimization, in Proc.Conf. Genet. Evol. Comput. (GECCO), V ancouver, BC, Canada, 2014,pp. 645–652, https://doi.org/10.1145/2576768.2598297.
– volume: 243
  start-page: 423
  year: 2015
  end-page: 441
  ident: bib44
  article-title: Preference-inspired co-evolutionary algorithms using weight vectors
  publication-title: Eur. J. Oper. Res.
– start-page: 373
  year: 2008
  end-page: 404
  ident: bib70
  article-title: Quality assessment of Pareto set approximations
  publication-title: Multiobjective Optimization
– volume: 18
  start-page: 348
  year: 2014
  end-page: 365
  ident: bib56
  article-title: Shift-based density estimation for pareto-based algorithms in many-objective optimization
  publication-title: IEEE Trans. Evol. Comput.
– volume: 20
  start-page: 821
  year: 2016
  end-page: 837
  ident: bib31
  article-title: Decomposition-based algorithms using pareto adaptive scalarizing methods
  publication-title: IEEE Trans. Evol. Comput.
– volume: 52
  start-page: 2698
  year: 2022
  end-page: 2711
  ident: bib68
  article-title: An adaptive reference vector-guided evolutionary algorithm using growing neural gas for many-objective optimization of irregular problems
  publication-title: IEEE Trans. Cybern.
– volume: 21
  start-page: 819
  year: 2015
  end-page: 849
  ident: bib13
  article-title: Analysis of inverted PBI and comparison with other scalarizing functions in decomposition based MOEAs
  publication-title: J. Heuristics
– volume: 20
  start-page: 645
  year: 2016
  end-page: 665
  ident: bib57
  article-title: Pareto or Non-Pareto: bi-criterion evolution in multiobjective optimization
  publication-title: IEEE Trans. Evol. Comput.
– year: 2020
  ident: bib21
  article-title: A survey of weight vector adjustment methods for decomposition based multi-objective evolutionary algorithms
  publication-title: IEEE Trans. Evolut. Comput.
– volume: 509
  year: 2019
  ident: bib60
  article-title: An angle dominance criterion for evolutionary many-objective optimization
  publication-title: Inf. Sci.
– volume: 20
  start-page: 475
  year: 2016
  end-page: 480
  ident: bib27
  article-title: Constrained subproblems in a decomposition-based multiobjective evolutionary algorithm
  publication-title: IEEE Trans. Evolut. Comput.
– volume: 19
  start-page: 45
  year: 2011
  end-page: 76
  ident: bib4
  article-title: Hype: an algorithm for fast hypervolume−based many-objective optimization
  publication-title: Evol. Comput.
– volume: 20
  start-page: 52
  year: 2016
  end-page: 64
  ident: bib20
  article-title: Are all the subproblems equally important? Resource allocation in decomposition-based multiobjective evolutionary algorithms
  publication-title: IEEE Trans. Evolut. Comput.
– volume: 22
  start-page: 3997
  year: 2018
  end-page: 4012
  ident: bib47
  article-title: Adjust weight vectors in MOEA/D for bi-objective optimization problems with discontinuous Pareto fronts
  publication-title: Soft Comput.
– year: 2020
  ident: bib17
  article-title: Research of strategies of maintaining population diversity for MOEA/D algorithm
  publication-title: Artif. Intell. Algorithms Appl.
– volume: 20
  start-page: 4005
  year: 2016
  end-page: 4021
  ident: bib52
  article-title: Decomposing the user preference in multiobjective optimization
  publication-title: Soft Comput.
– start-page: 825
  year: 2002
  end-page: 830
  ident: bib62
  article-title: Scalable multi-objective optimization test problems
  publication-title: Evolut. Comput., 2002. CEC ’02. Proc. 2002 Congr. , 1
– year: 2019
  ident: bib19
  article-title: On balancing neighborhood and global replacement strategies in MOEA/D
  publication-title: IEEE Access
– volume: 47
  start-page: 721
  year: 2017
  end-page: 742
  ident: bib54
  article-title: Multi-objective hybrid artificial bee colony algorithm enhanced with Lévy flight and self-adaption for cloud manufacturing service composition
  publication-title: Appl. Intell.
– year: 2020
  ident: bib18
  article-title: Success-history based parameter adaptation in MOEA/D algorithm
  publication-title: Adv. Swarm Intell.
– start-page: 59
  year: 2019
  end-page: 68
  ident: bib16
  article-title: Using two reproduction operators for balancing convergence and diversity in MOEA/D
  publication-title: Int. Conf. swarm Intell.
– start-page: 1
  year: 2016
  end-page: 15
  ident: bib24
  article-title: Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes
  publication-title: Soft Comput.
– volume: 136
  year: 2023
  ident: bib38
  article-title: A Pareto Front grid guided multi-objective evolutionary algorithm
  publication-title: Appl. Soft Comput.
– volume: 49
  start-page: 517
  year: 2019
  end-page: 526
  ident: bib49
  article-title: MOEA/HD: a multiobjective evolutionary algorithm based on hierarchical decomposition
  publication-title: IEEE Trans. Cybern.
– volume: 18
  start-page: 577
  year: 2014
  end-page: 601
  ident: bib2
  article-title: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints
  publication-title: IEEE Trans. Evol. Comput.
– start-page: 248
  year: 2015
  end-page: 262
  ident: bib32
  article-title: Pareto Adaptive Scalarising Functions for Decomposition Based Algorithms
– volume: 21
  start-page: 131
  year: 2017
  end-page: 152
  ident: bib55
  article-title: A vector angle-based evolutionary algorithm for unconstrained many-objective optimization
  publication-title: IEEE Trans. Evol. Comput.
– volume: 8
  start-page: 1
  year: 2016
  end-page: 41
  ident: bib30
  article-title: Global WASF-GA: an evolutionary algorithm in multiobjective optimization to approximate the whole Pareto optimal front
  publication-title: Evol. Comput.
– year: 2001
  ident: bib1
  article-title: Multi-objective optimization using evolutionary algorithms. 2001
– volume: 47
  start-page: 2689
  year: 2017
  end-page: 2702
  ident: bib53
  article-title: A many-objective evolutionary algorithm using a one -by-one selection strategy
  publication-title: IEEE Trans. Cybern.
– volume: 60
  year: 2021
  ident: bib66
  article-title: A many-objective evolutionary algorithm based on rotation and decomposition
  publication-title: Swarm Evolut. Comput.
– volume: 18
  start-page: 602
  year: 2014
  end-page: 622
  ident: bib33
  article-title: An evolutionary many-objective optimization algorithm using reference point based nondominated sorting approach, part II: Handling constraints and extending to an adaptive approach
  publication-title: IEEE Trans. Evolut. Comput.
– volume: 6
  start-page: 182
  year: 2002
  end-page: 197
  ident: bib3
  article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II
  publication-title: IEEE Trans. Evol. Comput.
– start-page: 1726
  year: 2019
  ident: 10.1016/j.asoc.2024.112434_bib36
  article-title: Adaptation of reference vectors for evolutionary many-objective optimization of problems with irregular pareto fronts
  publication-title: Congr. Evolut. Comput.
– volume: 22
  start-page: 821
  issue: 6
  year: 2018
  ident: 10.1016/j.asoc.2024.112434_bib50
  article-title: R-metric: evaluating the performance of preference-based evolutionary multi-objective optimization using reference points
  publication-title: IEEE Trans. Evol. Comput.
  doi: 10.1109/TEVC.2017.2737781
– start-page: 1900
  year: 2017
  ident: 10.1016/j.asoc.2024.112434_bib51
  article-title: Evolutionary method for weight vector generation in multi-objective evolutionary algorithms based on decomposition and aggregation
  publication-title: Proc. IEEE Congr. Evol. Comput. (CEC)
– volume: 47
  start-page: 2689
  issue: 9
  year: 2017
  ident: 10.1016/j.asoc.2024.112434_bib53
  article-title: A many-objective evolutionary algorithm using a one -by-one selection strategy
  publication-title: IEEE Trans. Cybern.
  doi: 10.1109/TCYB.2016.2638902
– volume: 22
  start-page: 3997
  issue: 12
  year: 2018
  ident: 10.1016/j.asoc.2024.112434_bib47
  article-title: Adjust weight vectors in MOEA/D for bi-objective optimization problems with discontinuous Pareto fronts
  publication-title: Soft Comput.
  doi: 10.1007/s00500-017-2609-4
– year: 2001
  ident: 10.1016/j.asoc.2024.112434_bib1
– year: 2020
  ident: 10.1016/j.asoc.2024.112434_bib59
  article-title: A Novel Archive Maintenance for Adapting Weight Vectors in Decomposition-based Multi-objective Evolutionary Algorithms
– year: 2019
  ident: 10.1016/j.asoc.2024.112434_bib41
  article-title: Many-Objective Evolutionary Algorithm Based On Decomposition With Random And Adaptive Weights
– volume: 22
  start-page: 231
  issue: 2
  year: 2014
  ident: 10.1016/j.asoc.2024.112434_bib40
  article-title: Moea/d with adaptive weight adjustment
  publication-title: Evolut. Comput.
  doi: 10.1162/EVCO_a_00109
– volume: 6
  start-page: 402
  issue: 4
  year: 2002
  ident: 10.1016/j.asoc.2024.112434_bib43
  article-title: On the performance of multiple-objective genetic local search on the 0/1 knapsack problem—A comparative experiment
  publication-title: IEEE Trans. Evol. Comput.
  doi: 10.1109/TEVC.2002.802873
– volume: 22
  start-page: 609
  issue: 4
  year: 2018
  ident: 10.1016/j.asoc.2024.112434_bib5
  article-title: An indicator based multi-objective evolutionary algorithm with reference point adaptation for better versatility
  publication-title: IEEE Trans. Evol. Comput.
  doi: 10.1109/TEVC.2017.2749619
– volume: 48
  start-page: 2335
  year: 2018
  ident: 10.1016/j.asoc.2024.112434_bib23
  article-title: A decomposition-based many-objective evolutionary algorithm with two types of adjustments for direction vectors
  publication-title: IEEE Trans. Cybern.
  doi: 10.1109/TCYB.2017.2737554
– volume: 26
  start-page: 326
  year: 1983
  ident: 10.1016/j.asoc.2024.112434_bib58
  article-title: An interactive weighted Tchebycheff procedure for multiple objective programming
  publication-title: Math. Program.
  doi: 10.1007/BF02591870
– start-page: 373
  year: 2008
  ident: 10.1016/j.asoc.2024.112434_bib70
  article-title: Quality assessment of Pareto set approximations
– ident: 10.1016/j.asoc.2024.112434_bib26
  doi: 10.1145/2576768.2598297
– volume: 12
  start-page: 73
  issue: 4
  year: 2017
  ident: 10.1016/j.asoc.2024.112434_bib69
  article-title: Platemo: a matlab platform for evolutionary multi-objective optimization [educational forum]
  publication-title: IEEE Comput. Intell. Mag.
  doi: 10.1109/MCI.2017.2742868
– volume: 46
  start-page: 421
  issue: 2
  year: 2016
  ident: 10.1016/j.asoc.2024.112434_bib11
  article-title: An improved multiobjective optimization evolutionary algorithm based on decomposition for complex pareto fronts
  publication-title: IEEE Trans. Syst., Man, Cybern.
– volume: 149
  year: 2023
  ident: 10.1016/j.asoc.2024.112434_bib39
  article-title: Multi-objective decomposition evolutionary algorithm with objective modification-based dominance and external archive
  publication-title: Appl. Soft Comput.
  doi: 10.1016/j.asoc.2023.111006
– volume: 45
  start-page: 2076
  issue: 10
  year: 2015
  ident: 10.1016/j.asoc.2024.112434_bib6
  article-title: Interrelationship-based selection for decomposition multiobjective optimization
  publication-title: IEEE Trans. Cybern.
  doi: 10.1109/TCYB.2014.2365354
– year: 2020
  ident: 10.1016/j.asoc.2024.112434_bib18
  article-title: Success-history based parameter adaptation in MOEA/D algorithm
  publication-title: Adv. Swarm Intell.
  doi: 10.1007/978-3-030-53956-6_41
– volume: 34
  start-page: 89
  year: 2017
  ident: 10.1016/j.asoc.2024.112434_bib28
  article-title: On the use of two reference points in decomposition based multiobjective evolutionary algorithms
  publication-title: Swarm Evol. Comput.
  doi: 10.1016/j.swevo.2017.01.002
– start-page: 248
  year: 2015
  ident: 10.1016/j.asoc.2024.112434_bib32
– volume: 21
  start-page: 131
  issue: 1
  year: 2017
  ident: 10.1016/j.asoc.2024.112434_bib55
  article-title: A vector angle-based evolutionary algorithm for unconstrained many-objective optimization
  publication-title: IEEE Trans. Evol. Comput.
  doi: 10.1109/TEVC.2016.2587808
– volume: 21
  start-page: 819
  year: 2015
  ident: 10.1016/j.asoc.2024.112434_bib13
  article-title: Analysis of inverted PBI and comparison with other scalarizing functions in decomposition based MOEAs
  publication-title: J. Heuristics
  doi: 10.1007/s10732-015-9301-6
– volume: 49
  start-page: 517
  issue: 2
  year: 2019
  ident: 10.1016/j.asoc.2024.112434_bib49
  article-title: MOEA/HD: a multiobjective evolutionary algorithm based on hierarchical decomposition
  publication-title: IEEE Trans. Cybern.
  doi: 10.1109/TCYB.2017.2779450
– volume: 20
  start-page: 821
  issue: 6
  year: 2016
  ident: 10.1016/j.asoc.2024.112434_bib31
  article-title: Decomposition-based algorithms using pareto adaptive scalarizing methods
  publication-title: IEEE Trans. Evol. Comput.
  doi: 10.1109/TEVC.2016.2521175
– volume: 20
  start-page: 4005
  issue: 10
  year: 2016
  ident: 10.1016/j.asoc.2024.112434_bib52
  article-title: Decomposing the user preference in multiobjective optimization
  publication-title: Soft Comput.
  doi: 10.1007/s00500-015-1736-z
– volume: 243
  start-page: 423
  issue: 2
  year: 2015
  ident: 10.1016/j.asoc.2024.112434_bib44
  article-title: Preference-inspired co-evolutionary algorithms using weight vectors
  publication-title: Eur. J. Oper. Res.
  doi: 10.1016/j.ejor.2014.05.019
– volume: 10
  start-page: 477
  issue: 5
  year: 2006
  ident: 10.1016/j.asoc.2024.112434_bib63
  article-title: A review of multiobjective test problems and a scalable test problem toolkit
  publication-title: IEEE Trans. Evol. Comput.
  doi: 10.1109/TEVC.2005.861417
– volume: 128
  year: 2022
  ident: 10.1016/j.asoc.2024.112434_bib37
  article-title: A decomposition-based many-objective evolutionary algorithm with adaptive weight vector strategy
  publication-title: Appl. Soft Comput.
  doi: 10.1016/j.asoc.2022.109412
– volume: 19
  start-page: 694
  issue: 5
  year: 2015
  ident: 10.1016/j.asoc.2024.112434_bib64
  article-title: An evolutionary many-objective optimization algorithm based on dominance and decomposition
  publication-title: IEEE Trans. Evol. Comput.
  doi: 10.1109/TEVC.2014.2373386
– start-page: 978
  year: 2015
  ident: 10.1016/j.asoc.2024.112434_bib45
  article-title: On the use of random weights in MOEA/D
  publication-title: Proc. IEEE Congr. Evol. Comput. (CEC)
– start-page: 274
  year: 2014
  ident: 10.1016/j.asoc.2024.112434_bib10
  article-title: Adaptive update range of solutions in MOEA/D for multi and many-objective optimization
  publication-title: Simula Evol. Learn.
  doi: 10.1007/978-3-319-13563-2_24
– start-page: 216
  year: 2019
  ident: 10.1016/j.asoc.2024.112434_bib34
  article-title: Indicator based weight adaptation for solving many-objective optimization problems
  publication-title: Proc. Evol. Multi Crit. Optim. (EMO)
  doi: 10.1007/978-3-030-12598-1_18
– volume: 20
  start-page: 52
  issue: 1
  year: 2016
  ident: 10.1016/j.asoc.2024.112434_bib20
  article-title: Are all the subproblems equally important? Resource allocation in decomposition-based multiobjective evolutionary algorithms
  publication-title: IEEE Trans. Evolut. Comput.
  doi: 10.1109/TEVC.2015.2424251
– volume: 60
  year: 2021
  ident: 10.1016/j.asoc.2024.112434_bib66
  article-title: A many-objective evolutionary algorithm based on rotation and decomposition
  publication-title: Swarm Evolut. Comput.
– year: 2020
  ident: 10.1016/j.asoc.2024.112434_bib17
  article-title: Research of strategies of maintaining population diversity for MOEA/D algorithm
  publication-title: Artif. Intell. Algorithms Appl.
– volume: 20
  start-page: 645
  issue: 5
  year: 2016
  ident: 10.1016/j.asoc.2024.112434_bib57
  article-title: Pareto or Non-Pareto: bi-criterion evolution in multiobjective optimization
  publication-title: IEEE Trans. Evol. Comput.
  doi: 10.1109/TEVC.2015.2504730
– volume: 19
  start-page: 45
  issue: 1
  year: 2011
  ident: 10.1016/j.asoc.2024.112434_bib4
  article-title: Hype: an algorithm for fast hypervolume−based many-objective optimization
  publication-title: Evol. Comput.
  doi: 10.1162/EVCO_a_00009
– volume: 18
  start-page: 909
  issue: 6
  year: 2014
  ident: 10.1016/j.asoc.2024.112434_bib8
  article-title: Stable matching-based selection in evolutionary multiobjective optimization
  publication-title: IEEE Trans. Evol. Comput.
  doi: 10.1109/TEVC.2013.2293776
– volume: 11
  start-page: 712
  issue: 6
  year: 2007
  ident: 10.1016/j.asoc.2024.112434_bib9
  article-title: Moea/d: A multiobjective evolutionary algorithm based on decomposition
  publication-title: IEEE Trans. Evolut. Comput.
  doi: 10.1109/TEVC.2007.892759
– start-page: 825
  year: 2002
  ident: 10.1016/j.asoc.2024.112434_bib62
  article-title: Scalable multi-objective optimization test problems
  publication-title: Evolut. Comput., 2002. CEC ’02. Proc. 2002 Congr. , 1
– volume: 28
  start-page: 227
  issue: 2
  year: 2020
  ident: 10.1016/j.asoc.2024.112434_bib42
  article-title: What weights work for you? adapting weights for any pareto front shape in decomposition-based evolutionary multi-objective optimisation
  publication-title: Evolut. Comput.
  doi: 10.1162/evco_a_00269
– volume: 24
  start-page: 439
  issue: 3
  year: 2020
  ident: 10.1016/j.asoc.2024.112434_bib67
  article-title: Adapting reference vectors and scalarizing functions by growing neural gas to handle irregular Pareto fronts
  publication-title: IEEE Trans. Evolut. Comput.
– start-page: 1
  year: 2016
  ident: 10.1016/j.asoc.2024.112434_bib24
  article-title: Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes
  publication-title: Soft Comput.
– volume: 8
  start-page: 1
  year: 2016
  ident: 10.1016/j.asoc.2024.112434_bib30
  article-title: Global WASF-GA: an evolutionary algorithm in multiobjective optimization to approximate the whole Pareto optimal front
  publication-title: Evol. Comput.
– issue: 99
  year: 2019
  ident: 10.1016/j.asoc.2024.112434_bib19
  article-title: On balancing neighborhood and global replacement strategies in MOEA/D
  publication-title: IEEE Access
– volume: 18
  start-page: 348
  issue: 3
  year: 2014
  ident: 10.1016/j.asoc.2024.112434_bib56
  article-title: Shift-based density estimation for pareto-based algorithms in many-objective optimization
  publication-title: IEEE Trans. Evol. Comput.
  doi: 10.1109/TEVC.2013.2262178
– volume: 20
  start-page: 475
  issue: 3
  year: 2016
  ident: 10.1016/j.asoc.2024.112434_bib27
  article-title: Constrained subproblems in a decomposition-based multiobjective evolutionary algorithm
  publication-title: IEEE Trans. Evolut. Comput.
  doi: 10.1109/TEVC.2015.2457616
– volume: 509
  year: 2019
  ident: 10.1016/j.asoc.2024.112434_bib60
  article-title: An angle dominance criterion for evolutionary many-objective optimization
  publication-title: Inf. Sci.
– volume: 136
  year: 2023
  ident: 10.1016/j.asoc.2024.112434_bib38
  article-title: A Pareto Front grid guided multi-objective evolutionary algorithm
  publication-title: Appl. Soft Comput.
  doi: 10.1016/j.asoc.2023.110095
– volume: 47
  start-page: 721
  issue: 3
  year: 2017
  ident: 10.1016/j.asoc.2024.112434_bib54
  article-title: Multi-objective hybrid artificial bee colony algorithm enhanced with Lévy flight and self-adaption for cloud manufacturing service composition
  publication-title: Appl. Intell.
  doi: 10.1007/s10489-017-0927-y
– start-page: 77
  year: 2019
  ident: 10.1016/j.asoc.2024.112434_bib12
  article-title: MOEA/D with the online agglomerative clustering based self-adaptive mating restriction strategy
  publication-title: Neurocomputing
– start-page: 1
  year: 2016
  ident: 10.1016/j.asoc.2024.112434_bib14
  article-title: Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes
  publication-title: Soft Comput.
– volume: 21
  start-page: 440
  issue: 3
  year: 2017
  ident: 10.1016/j.asoc.2024.112434_bib7
  article-title: A survey of multiobjective evolutionary algorithms based on decomposition
  publication-title: IEEE Trans. Evolut. Comput.
– volume: 501
  start-page: 248
  year: 2019
  ident: 10.1016/j.asoc.2024.112434_bib22
  article-title: A decomposition based evolutionary algorithm with direction vector adaption and selection enhancement
  publication-title: Inf. Sci.
  doi: 10.1016/j.ins.2019.05.083
– start-page: 842
  year: 2018
  ident: 10.1016/j.asoc.2024.112434_bib46
  article-title: A parameterless decomposition-based evolutionary multi-objective algorithm
  publication-title: Proc. Int. Conf. Adv. Comput. Intell.
– year: 2020
  ident: 10.1016/j.asoc.2024.112434_bib15
  article-title: An Improved MOEA/D algorithm with an adaptive evolutionary strategy
  publication-title: Inf. Sci.
– start-page: 59
  year: 2019
  ident: 10.1016/j.asoc.2024.112434_bib16
  article-title: Using two reproduction operators for balancing convergence and diversity in MOEA/D
  publication-title: Int. Conf. swarm Intell.
– volume: 23
  start-page: 572
  issue: 4
  year: 2019
  ident: 10.1016/j.asoc.2024.112434_bib48
  article-title: A many-objective evolutionary algorithm with two interacting processes: Cascade clustering and reference point incremental learning
  publication-title: IEEE Trans. Evol. Comput.
  doi: 10.1109/TEVC.2018.2874465
– volume: 18
  start-page: 577
  issue: 4
  year: 2014
  ident: 10.1016/j.asoc.2024.112434_bib2
  article-title: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints
  publication-title: IEEE Trans. Evol. Comput.
  doi: 10.1109/TEVC.2013.2281535
– volume: 23
  issue: 3
  year: 2018
  ident: 10.1016/j.asoc.2024.112434_bib65
  article-title: Evolutionary many-objective optimization based on dynamical decomposition
  publication-title: IEEE Trans. Evolut. Comput.
  doi: 10.1109/TEVC.2018.2865590
– volume: 6
  start-page: 182
  issue: 2
  year: 2002
  ident: 10.1016/j.asoc.2024.112434_bib3
  article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II
  publication-title: IEEE Trans. Evol. Comput.
  doi: 10.1109/4235.996017
– start-page: 615
  year: 2013
  ident: 10.1016/j.asoc.2024.112434_bib25
  article-title: Towards understanding the cost of adaptation in decomposition-based optimization algorithms
– volume: 21
  start-page: 220
  issue: 2
  year: 2017
  ident: 10.1016/j.asoc.2024.112434_bib61
  article-title: Many-objective evolutionary algorithms based on coordinated selection strategy
  publication-title: IEEE Trans. Evolut. Comput.
  doi: 10.1109/TEVC.2016.2598687
– volume: 62
  start-page: 101
  issue: 1
  year: 2015
  ident: 10.1016/j.asoc.2024.112434_bib29
  article-title: A preference-based evolutionary algorithm for multiobjective optimization: The weighting achievement scalarizing function genetic algorithm
  publication-title: J. Glob. Optim.
  doi: 10.1007/s10898-014-0214-y
– volume: 18
  start-page: 602
  issue: 4
  year: 2014
  ident: 10.1016/j.asoc.2024.112434_bib33
  article-title: An evolutionary many-objective optimization algorithm using reference point based nondominated sorting approach, part II: Handling constraints and extending to an adaptive approach
  publication-title: IEEE Trans. Evolut. Comput.
  doi: 10.1109/TEVC.2013.2281534
– issue: 99
  year: 2020
  ident: 10.1016/j.asoc.2024.112434_bib21
  article-title: A survey of weight vector adjustment methods for decomposition based multi-objective evolutionary algorithms
  publication-title: IEEE Trans. Evolut. Comput.
– volume: 52
  start-page: 2698
  issue: 5
  year: 2022
  ident: 10.1016/j.asoc.2024.112434_bib68
  article-title: An adaptive reference vector-guided evolutionary algorithm using growing neural gas for many-objective optimization of irregular problems
  publication-title: IEEE Trans. Cybern.
  doi: 10.1109/TCYB.2020.3020630
– volume: 20
  start-page: 773
  issue: 5
  year: 2016
  ident: 10.1016/j.asoc.2024.112434_bib35
  article-title: A reference vector guided evolutionary algorithm for many-objective optimization
  publication-title: IEEE Trans. Evolut. Comput.
  doi: 10.1109/TEVC.2016.2519378
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Snippet In order to better balance the convergence and diversity of MOEA/D for many objective optimization problems (MaOPs) with various Pareto fronts (PFs), an...
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StartPage 112434
SubjectTerms Adaptive weight vector adjustment
Dual-density judgment
Many-objective optimization
Two-stage strategy
Title Adaptive decomposition-based evolutionary algorithm for many-objective optimization with two-stage dual-density judgment
URI https://dx.doi.org/10.1016/j.asoc.2024.112434
Volume 167
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