A dual-population based bidirectional coevolution algorithm for constrained multi-objective optimization problems
The balance between multiple objectives and various constraints is the key to solving constrained multi-objective optimization problems (CMOPs). When dealing with CMOPs with complex feasible regions, some evolutionary algorithms suffer from great challenges in converging to the constrained Pareto fr...
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| Veröffentlicht in: | Expert systems with applications Jg. 215; S. 119258 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.04.2023
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | The balance between multiple objectives and various constraints is the key to solving constrained multi-objective optimization problems (CMOPs). When dealing with CMOPs with complex feasible regions, some evolutionary algorithms suffer from great challenges in converging to the constrained Pareto front (CPF) with well-distributed feasible solutions. To address this issue, this paper proposes a dual-population based bidirectional coevolution algorithm, called DBC-CMOEA, which aims to converge to the CPF using promising solutions explored from both feasible and infeasible regions. To do so, DBC-CMOEA maintains two populations and an archive, where the dual-population is complementary in the search process and the archive is used to retain promising feasible and infeasible solutions, thus facilitating information exchange between these two populations. For updating the archive, a nondominated sorting procedure and an angle-based selected scheme are conducted to store infeasible and feasible solutions, as they can help to maintain the diversity of the search and find more feasible regions. To evolve the CPF from the bidirectional side of the feasible region, a novel mating selection strategy is used to choose appropriate mating parents. In comparison with some related constraint multi-objective optimization algorithms on a number of benchmark problems, experimental results show that the proposed algorithm performs better than the state-of-the-art constrained multi-objective evolutionary optimizers.
•A cooperative dual-population algorithm is proposed for solving CMOPs.•Constraints are handled in three different ways to explore the search space.•A brand-new selection strategy is applied to select the mating parents.•A novel archive update strategy is designed to retain the promising information. |
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| AbstractList | The balance between multiple objectives and various constraints is the key to solving constrained multi-objective optimization problems (CMOPs). When dealing with CMOPs with complex feasible regions, some evolutionary algorithms suffer from great challenges in converging to the constrained Pareto front (CPF) with well-distributed feasible solutions. To address this issue, this paper proposes a dual-population based bidirectional coevolution algorithm, called DBC-CMOEA, which aims to converge to the CPF using promising solutions explored from both feasible and infeasible regions. To do so, DBC-CMOEA maintains two populations and an archive, where the dual-population is complementary in the search process and the archive is used to retain promising feasible and infeasible solutions, thus facilitating information exchange between these two populations. For updating the archive, a nondominated sorting procedure and an angle-based selected scheme are conducted to store infeasible and feasible solutions, as they can help to maintain the diversity of the search and find more feasible regions. To evolve the CPF from the bidirectional side of the feasible region, a novel mating selection strategy is used to choose appropriate mating parents. In comparison with some related constraint multi-objective optimization algorithms on a number of benchmark problems, experimental results show that the proposed algorithm performs better than the state-of-the-art constrained multi-objective evolutionary optimizers.
•A cooperative dual-population algorithm is proposed for solving CMOPs.•Constraints are handled in three different ways to explore the search space.•A brand-new selection strategy is applied to select the mating parents.•A novel archive update strategy is designed to retain the promising information. |
| ArticleNumber | 119258 |
| Author | Song, Zhiming Bao, Qian Li, Shuijia Dai, Guangming Wang, Maocai Chen, Xiaoyu |
| Author_xml | – sequence: 1 givenname: Qian orcidid: 0000-0002-9084-2011 surname: Bao fullname: Bao, Qian email: qian.bao@cug.edu.cn organization: School of Computer Science, China University of Geosciences, Wuhan 430074, China – sequence: 2 givenname: Maocai surname: Wang fullname: Wang, Maocai email: cugwangmc@126.com organization: School of Computer Science, China University of Geosciences, Wuhan 430074, China – sequence: 3 givenname: Guangming surname: Dai fullname: Dai, Guangming email: cugdgm@126.com organization: School of Computer Science, China University of Geosciences, Wuhan 430074, China – sequence: 4 givenname: Xiaoyu orcidid: 0000-0002-4588-8475 surname: Chen fullname: Chen, Xiaoyu email: xiaoyu.chen@cug.edu.cn organization: School of Computer Science, China University of Geosciences, Wuhan 430074, China – sequence: 5 givenname: Zhiming surname: Song fullname: Song, Zhiming email: songzm@cug.edu.cn organization: School of Computer Science, China University of Geosciences, Wuhan 430074, China – sequence: 6 givenname: Shuijia orcidid: 0000-0003-3838-0072 surname: Li fullname: Li, Shuijia email: shuijiali@cug.edu.cn organization: School of Computer Science, China University of Geosciences, Wuhan 430074, China |
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| Cites_doi | 10.1007/s00500-019-03794-x 10.1109/CEC.2014.6900645 10.1016/j.eswa.2018.02.040 10.1109/TCYB.2020.3021138 10.1007/s00500-008-0323-y 10.1109/3468.650319 10.1109/4235.797969 10.1109/TCYB.2018.2819208 10.1016/j.swevo.2021.100983 10.1109/TEVC.2008.2009032 10.1016/j.swevo.2021.100952 10.1016/j.swevo.2022.101161 10.1080/03052159908941377 10.1162/evco_a_00226 10.1016/j.eswa.2015.05.050 10.1109/4235.996017 10.1016/j.swevo.2018.08.017 10.1109/TEVC.2021.3110130 10.1109/TEVC.2017.2767023 10.1016/j.asoc.2013.10.008 10.1016/j.asoc.2017.06.053 10.1109/TSMC.2020.3034180 10.1109/TCYB.2019.2899225 10.1109/TCYB.2015.2493239 10.1016/j.asoc.2012.07.027 10.1109/TEVC.2013.2281534 10.1109/TCYB.2021.3056176 10.1109/TEVC.2022.3145582 10.1109/TEVC.2013.2281535 10.1016/j.swevo.2021.100961 10.1109/TEVC.2018.2855411 10.1109/TEVC.2007.892759 10.1016/j.swevo.2019.100619 10.1109/TEVC.2006.872344 10.1016/j.asoc.2018.10.027 10.1109/TEVC.2003.810761 10.1109/MCI.2017.2742868 10.1109/TEVC.2020.3004012 10.1016/j.ins.2018.07.012 10.1109/TEVC.2019.2896967 10.1109/4235.585893 10.1162/evco_a_00259 10.1016/j.ins.2021.07.078 10.1109/TEVC.2019.2894743 10.1109/TEVC.2021.3066301 10.1109/TEVC.2016.2587749 10.1109/CEC.2010.5586545 |
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| Keywords | Constrained multi-objective Cooperative dual-population Constrained-handling technique Bidirectional coevolution |
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| References | Cuate, Uribe, Lara, Schütze (b9) 2020; 52 Peng, Liu, Gu (b37) 2017; 60 Agrawal, Deb, Agrawal (b1) 1994; 9 Wang, Liang, Zhang (b47) 2018; 49 Liu, Wang (b31) 2019; 23 Tharwat, Schenck (b43) 2021; 67 Wolpert, Macready (b50) 1997; 1 Bao, Wang, Dai, Chen, Song, Li (b3) 2022; 75 Deb, Thiele, Laumanns, Zitzler (b15) 2005 Tian, Zhang, Xiao, Zhang, Jin (b46) 2021; 25 Zhang, Lin, Gao, Li (b53) 2015; 42 Bosman, Thierens (b4) 2003; 7 Qiao, Yu, Qu, Liang, Song, Yue (b38) 2022; 26 Ishibuchi, Imada, Setoguchi, Nojima (b24) 2018; 26 Ma, Wang (b33) 2019; 23 Zhang, Hui (b52) 2007; 11 Jiao, Luo, Shang, Liu (b28) 2014; 14 Qiao, Yu, Qu, Liang, Song, Yue (b39) 2022 (pp. 1–8). Chen, Wu, Pedrycz, Suganthan, Xing, Zhu (b7) 2021; 51 Jain, Deb (b26) 2014; 18 Deb (b11) 2011 Zou, Sun, Yang, Zheng (b57) 2021; 579 Fan, Fang, Li, Cai, Wei, Goodman (b16) 2019; 74 García, Monroy, Hernández, Coello (b22) 2021; 67 Ishibuchi, Setoguchi, Masuda, Nojima (b25) 2017; 21 Cai, Wang (b5) 2006; 10 Kumar, Wu, Ali, Luo, Mallipeddi, Suganthan (b29) 2021; 67 Zitzler, Thiele (b56) 1999; 3 Woldesenbet, Yen, Tessema (b49) 2009; 13 Tian, Cheng, Zhang, Jin (b44) 2017; 12 Deb, Jain (b13) 2014; 18 Fan, Li, Cai, Li, Wei, Zhang (b19) 2019; 44 Wang, Wang, Li, Yen (b48) 2016; 46 Qiu, Zhu, Wu, Chen, Pedrycz, Suganthan (b40) 2022; 52 Deb, Pratap, Agarwal, Meyarivan (b14) 2002; 6 Coello, Christiansen (b8) 1999; 31 Wu, Wen, Wang, Pedrycz, Suganthan (b51) 2022; 26 Fonseca, Fleming (b21) 1998; 28 Jan, Khanum (b27) 2013; 13 Ming, Trivedi, Wang, Srinivasan, Zhang (b36) 2021; 25 Chen, Tian, Pedrycz, Wu, Wang, Wang (b6) 2020; 50 Alcalá-Fdez, Sánchez, Garcia, del Jesus, Ventura, Garrell (b2) 2009; 13 Deb, Goyal (b12) 1999; 26 Liu, Wang, Tang (b32) 2022; 52 Martinez, S. Z., & Coello, C. A. C. (2014). A multiobjective evolutionary algorithm based on decomposition for constrained multiobjective optimization. In Tian, Zhang, Su, Zhang, Tan, Jin (b45) 2022; 52 Miguel Antonio, Coello Coello (b35) 2018; 22 Cuate, Uribe, Ponsich, Lara, Beltran, Sánchez (b10) 2019 Fan, Li, Cai, Li, Wei, Zhang (b20) 2020; 28 Zhou, Zhu, Wang, Zhang, Xiang, Zhang (b55) 2020; 50 Fan, Li, Cai, Hu, Lin, Li (b17) 2016 Fan, Li, Cai, Huang, Fang, Yugen (b18) 2019; 23 Ho-Huu, Duong-Gia, Vo-Duy, Le-Duc, Nguyen-Thoi (b23) 2018; 102 Li, Chen, Fu, Yao (b30) 2019; 23 (pp. 429–436). Takahama, T., & Sakai, S. (2010). Efficient constrained optimization by the constrained adaptive differential evolution. In Zhou, Dai, Zhang, Li, Ma (b54) 2018; 465 Ray, Singh, Isaacs, Smith (b41) 2009 Fan (10.1016/j.eswa.2022.119258_b16) 2019; 74 Coello (10.1016/j.eswa.2022.119258_b8) 1999; 31 Deb (10.1016/j.eswa.2022.119258_b12) 1999; 26 Deb (10.1016/j.eswa.2022.119258_b14) 2002; 6 Deb (10.1016/j.eswa.2022.119258_b13) 2014; 18 Wolpert (10.1016/j.eswa.2022.119258_b50) 1997; 1 Jiao (10.1016/j.eswa.2022.119258_b28) 2014; 14 Ishibuchi (10.1016/j.eswa.2022.119258_b24) 2018; 26 Fonseca (10.1016/j.eswa.2022.119258_b21) 1998; 28 Alcalá-Fdez (10.1016/j.eswa.2022.119258_b2) 2009; 13 Ishibuchi (10.1016/j.eswa.2022.119258_b25) 2017; 21 Wang (10.1016/j.eswa.2022.119258_b48) 2016; 46 Bao (10.1016/j.eswa.2022.119258_b3) 2022; 75 Cuate (10.1016/j.eswa.2022.119258_b9) 2020; 52 Agrawal (10.1016/j.eswa.2022.119258_b1) 1994; 9 García (10.1016/j.eswa.2022.119258_b22) 2021; 67 Fan (10.1016/j.eswa.2022.119258_b18) 2019; 23 Wang (10.1016/j.eswa.2022.119258_b47) 2018; 49 Peng (10.1016/j.eswa.2022.119258_b37) 2017; 60 Zou (10.1016/j.eswa.2022.119258_b57) 2021; 579 Li (10.1016/j.eswa.2022.119258_b30) 2019; 23 Deb (10.1016/j.eswa.2022.119258_b11) 2011 Qiao (10.1016/j.eswa.2022.119258_b38) 2022; 26 Jan (10.1016/j.eswa.2022.119258_b27) 2013; 13 10.1016/j.eswa.2022.119258_b42 Tian (10.1016/j.eswa.2022.119258_b46) 2021; 25 Qiao (10.1016/j.eswa.2022.119258_b39) 2022 Jain (10.1016/j.eswa.2022.119258_b26) 2014; 18 Zhang (10.1016/j.eswa.2022.119258_b53) 2015; 42 Zitzler (10.1016/j.eswa.2022.119258_b56) 1999; 3 Fan (10.1016/j.eswa.2022.119258_b19) 2019; 44 Tian (10.1016/j.eswa.2022.119258_b44) 2017; 12 Cai (10.1016/j.eswa.2022.119258_b5) 2006; 10 Kumar (10.1016/j.eswa.2022.119258_b29) 2021; 67 Ray (10.1016/j.eswa.2022.119258_b41) 2009 Miguel Antonio (10.1016/j.eswa.2022.119258_b35) 2018; 22 Ming (10.1016/j.eswa.2022.119258_b36) 2021; 25 Bosman (10.1016/j.eswa.2022.119258_b4) 2003; 7 Liu (10.1016/j.eswa.2022.119258_b32) 2022; 52 Chen (10.1016/j.eswa.2022.119258_b7) 2021; 51 Zhou (10.1016/j.eswa.2022.119258_b54) 2018; 465 10.1016/j.eswa.2022.119258_b34 Wu (10.1016/j.eswa.2022.119258_b51) 2022; 26 Tian (10.1016/j.eswa.2022.119258_b45) 2022; 52 Liu (10.1016/j.eswa.2022.119258_b31) 2019; 23 Deb (10.1016/j.eswa.2022.119258_b15) 2005 Fan (10.1016/j.eswa.2022.119258_b20) 2020; 28 Fan (10.1016/j.eswa.2022.119258_b17) 2016 Zhang (10.1016/j.eswa.2022.119258_b52) 2007; 11 Zhou (10.1016/j.eswa.2022.119258_b55) 2020; 50 Cuate (10.1016/j.eswa.2022.119258_b10) 2019 Qiu (10.1016/j.eswa.2022.119258_b40) 2022; 52 Ho-Huu (10.1016/j.eswa.2022.119258_b23) 2018; 102 Tharwat (10.1016/j.eswa.2022.119258_b43) 2021; 67 Chen (10.1016/j.eswa.2022.119258_b6) 2020; 50 Ma (10.1016/j.eswa.2022.119258_b33) 2019; 23 Woldesenbet (10.1016/j.eswa.2022.119258_b49) 2009; 13 |
| References_xml | – volume: 28 start-page: 339 year: 2020 end-page: 378 ident: b20 article-title: Difficulty adjustable and scalable constrained multiobjective test problem toolkit publication-title: Evolutionary Computation – volume: 50 start-page: 3086 year: 2020 end-page: 3099 ident: b55 article-title: Tri-goal evolution framework for constrained many-objective optimization publication-title: IEEE Transactions on Systems, Man, and Cybernetics: Systems – volume: 67 year: 2021 ident: b29 article-title: A benchmark-suite of real-world constrained multi-objective optimization problems and some baseline results publication-title: Swarm and Evolutionary Computation – volume: 23 start-page: 12491 year: 2019 end-page: 12510 ident: b18 article-title: An improved epsilon constraint-handling method in moea/d for cmops with large infeasible regions publication-title: Soft Computing – volume: 10 start-page: 658 year: 2006 end-page: 675 ident: b5 article-title: A multiobjective optimization-based evolutionary algorithm for constrained optimization publication-title: IEEE Transactions on Evolutionary Computation – volume: 25 start-page: 102 year: 2021 end-page: 116 ident: b46 article-title: A coevolutionary framework for constrained multiobjective optimization problems publication-title: IEEE Transactions on Evolutionary Computation – volume: 465 start-page: 232 year: 2018 end-page: 247 ident: b54 article-title: Entropy based evolutionary algorithm with adaptive reference points for many-objective optimization problems publication-title: Information Sciences – volume: 13 start-page: 307 year: 2009 end-page: 318 ident: b2 article-title: Keel: a software tool to assess evolutionary algorithms for data mining problems publication-title: Soft Computing – start-page: 460 year: 2016 end-page: 467 ident: b17 article-title: Angle-based constrained dominance principle in moea/d for constrained multi-objective optimization problems publication-title: 2016 IEEE congress on evolutionary computation – volume: 26 start-page: 30 year: 1999 end-page: 45 ident: b12 article-title: A combined genetic adaptive search (geneas) for engineering design publication-title: Computer Science and Informatics – volume: 26 start-page: 646 year: 2022 end-page: 660 ident: b51 article-title: A voting-mechanism based ensemble framework for constraint handling techniques publication-title: IEEE Transactions on Evolutionary Computation – volume: 23 start-page: 972 year: 2019 end-page: 986 ident: b33 article-title: Evolutionary constrained multiobjective optimization: Test suite construction and performance comparisons publication-title: IEEE Transactions on Evolutionary Computation – volume: 46 start-page: 2938 year: 2016 end-page: 2952 ident: b48 article-title: Incorporating objective function information into the feasibility rule for constrained evolutionary optimization publication-title: IEEE Transactions on Cybernetics – reference: Takahama, T., & Sakai, S. (2010). Efficient constrained optimization by the constrained adaptive differential evolution. In – reference: (pp. 1–8). – volume: 13 start-page: 514 year: 2009 end-page: 525 ident: b49 article-title: Constraint handling in multiobjective evolutionary optimization publication-title: IEEE Transactions on Evolutionary Computation – start-page: 3 year: 2011 end-page: 34 ident: b11 article-title: Multi-objective optimisation using evolutionary algorithms: An introduction publication-title: Multi-objective evolutionary optimisation for product design and manufacturing – volume: 102 start-page: 262 year: 2018 end-page: 272 ident: b23 article-title: An efficient combination of multi-objective evolutionary optimization and reliability analysis for reliability-based design optimization of truss structures publication-title: Expert Systems with Applications – volume: 67 year: 2021 ident: b22 article-title: Coarseemoa: An indicatorbased evolutionary algorithm for solving equality constrained multi-objective optimization problems publication-title: Swarm and Evolutionary Computation – volume: 60 start-page: 613 year: 2017 end-page: 622 ident: b37 article-title: An evolutionary algorithm with directed weights for constrained multi-objective optimization publication-title: Applied Soft Computing – volume: 28 start-page: 26 year: 1998 end-page: 37 ident: b21 article-title: Multiobjective optimization and multiple constraint handling with evolutionary algorithms. i. a unified formulation publication-title: IEEE Transactions on Systems, Man & Cybernetics, Part A (Systems & Humans) – volume: 12 start-page: 73 year: 2017 end-page: 87 ident: b44 article-title: Platemo: A matlab platform for evolutionary multi-objective optimization publication-title: IEEE Computational Intelligence Magazine – volume: 52 start-page: 1716 year: 2022 end-page: 1730 ident: b40 article-title: Ensemble many-objective optimization algorithm based on voting mechanism publication-title: IEEE Transactions on Systems, Man, and Cybernetics: Systems – start-page: 145 year: 2009 end-page: 165 ident: b41 article-title: Infeasibility driven evolutionary algorithm for constrained optimization publication-title: Constraint-handling in evolutionary optimization – volume: 52 start-page: 9559 year: 2022 end-page: 9572 ident: b45 article-title: Balancing objective optimization and constraint satisfaction in constrained evolutionary multiobjective optimization publication-title: IEEE Transactions on Cybernetics – volume: 7 start-page: 174 year: 2003 end-page: 188 ident: b4 article-title: The balance between proximity and diversity in multiobjective evolutionary algorithms publication-title: IEEE Transactions on Evolutionary Computation – reference: (pp. 429–436). – volume: 579 start-page: 89 year: 2021 end-page: 102 ident: b57 article-title: A dual-population algorithm based on alternative evolution and degeneration for solving constrained multi-objective optimization problems publication-title: Information Sciences – start-page: 105 year: 2005 end-page: 145 ident: b15 article-title: Scalable test problems for evolutionary multiobjective optimization publication-title: Evolutionary multiobjective optimization: theoretical advances and applications – volume: 13 start-page: 128 year: 2013 end-page: 148 ident: b27 article-title: A study of two penalty-parameterless constraint handling techniques in the framework of moea/d publication-title: Applied Soft Computing – reference: Martinez, S. Z., & Coello, C. A. C. (2014). A multiobjective evolutionary algorithm based on decomposition for constrained multiobjective optimization. In – volume: 44 start-page: 665 year: 2019 end-page: 679 ident: b19 article-title: Push and pull search for solving constrained multi-objective optimization problems publication-title: Swarm and Evolutionary Computation – volume: 18 start-page: 602 year: 2014 end-page: 622 ident: b26 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 Transactions on Evolutionary Computation – volume: 14 start-page: 363 year: 2014 end-page: 380 ident: b28 article-title: A modified objective function method with feasibleguiding strategy to solve constrained multiobjective optimization problems publication-title: Applied Soft Computing – volume: 31 start-page: 337 year: 1999 end-page: 368 ident: b8 article-title: Moses: A multiobjective tool for engineering design publication-title: Engineering Optimization – volume: 21 start-page: 169 year: 2017 end-page: 190 ident: b25 article-title: Performance of decomposition-based many-objective algorithms strongly depends on pareto front shapes publication-title: IEEE Transactions on Evolutionary Computation – volume: 22 start-page: 851 year: 2018 end-page: 865 ident: b35 article-title: Coevolutionary multiobjective evolutionary algorithms: Survey of the state-of-the-art publication-title: IEEE Transactions on Evolutionary Computation – volume: 51 start-page: 1507 year: 2021 end-page: 1522 ident: b7 article-title: An adaptive resource allocation strategy for objective space partition-based multiobjective optimization publication-title: IEEE Transactions on Systems, Man, and Cybernetics: Systems – volume: 26 start-page: 263 year: 2022 end-page: 277 ident: b38 article-title: An evolutionary multitasking optimization framework for constrained multiobjective optimization problems publication-title: IEEE Transactions on Evolutionary Computation – volume: 52 year: 2020 ident: b9 article-title: A benchmark for equality constrained multiobjective optimization publication-title: Swarm and Evolutionary Computation – volume: 3 start-page: 257 year: 1999 end-page: 271 ident: b56 article-title: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach publication-title: IEEE Transactions on Evolutionary Computation – volume: 67 year: 2021 ident: b43 article-title: Population initialization techniques for evolutionary algorithms for single-objective constrained optimization problems: Deterministic vs. stochastic techniques publication-title: Swarm and Evolutionary Computation – volume: 6 start-page: 182 year: 2002 end-page: 197 ident: b14 article-title: A fast and elitist multiobjective genetic algorithm: Nsga-ii publication-title: IEEE Transactions on Evolutionary Computation – volume: 11 start-page: 712 year: 2007 end-page: 731 ident: b52 article-title: Moead: A multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Transactions on Evolutionary Computation – volume: 42 start-page: 7831 year: 2015 end-page: 7845 ident: b53 article-title: Backtracking search algorithm with three constraint handling methods for constrained optimization problems publication-title: Expert Systems with Applications – volume: 9 start-page: 115 year: 1994 end-page: 148 ident: b1 article-title: Simulated binary crossover for continuous search space publication-title: Complex Systems – volume: 52 start-page: 10163 year: 2022 end-page: 10176 ident: b32 article-title: Handling constrained multiobjective optimization problems via bidirectional coevolution publication-title: IEEE Transactions on Cybernetics – start-page: 53 year: 2019 end-page: 65 ident: b10 article-title: A new hybrid metaheuristic for equality constrained bi-objective optimization problems publication-title: Evolutionary multi-criterion optimization – volume: 18 start-page: 577 year: 2014 end-page: 601 ident: b13 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 Transactions on Evolutionary Computation – start-page: 1 year: 2022 ident: b39 article-title: Dynamic auxiliary task-based evolutionary multitasking for constrained multi-objective optimization publication-title: IEEE Transactions on Evolutionary Computation – volume: 26 start-page: 411 year: 2018 end-page: 440 ident: b24 article-title: How to specify a reference point in hypervolume calculation for fair performance comparison publication-title: Evolutionary Computation – volume: 25 start-page: 739 year: 2021 end-page: 753 ident: b36 article-title: A dual-population-based evolutionary algorithm for constrained multiobjective optimization publication-title: IEEE Transactions on Evolutionary Computation – volume: 75 year: 2022 ident: b3 article-title: An archive-based two-stage evolutionary algorithm for constrained multi-objective optimization problems publication-title: Swarm and Evolutionary Computation – volume: 50 start-page: 3367 year: 2020 end-page: 3380 ident: b6 article-title: Hyperplane assisted evolutionary algorithm for many-objective optimization problems publication-title: IEEE Transactions on Cybernetics – volume: 23 start-page: 870 year: 2019 end-page: 884 ident: b31 article-title: Handling constrained multiobjective optimization problems with constraints in both the decision and objective spaces publication-title: IEEE Transactions on Evolutionary Computation – volume: 23 start-page: 303 year: 2019 end-page: 315 ident: b30 article-title: Two-archive evolutionary algorithm for constrained multiobjective optimization publication-title: IEEE Transactions on Evolutionary Computation – volume: 74 start-page: 621 year: 2019 end-page: 633 ident: b16 article-title: Moea/d with angle-based constrained dominance principle for constrained multi-objective optimization problems publication-title: Applied Soft Computing – volume: 49 start-page: 2060 year: 2018 end-page: 2072 ident: b47 article-title: Cooperative differential evolution framework for constrained multiobjective optimization publication-title: IEEE Transactions on Cybernetics – volume: 1 start-page: 67 year: 1997 end-page: 82 ident: b50 article-title: No free lunch theorems for optimization publication-title: IEEE Transactions on Evolutionary Computation – volume: 23 start-page: 12491 year: 2019 ident: 10.1016/j.eswa.2022.119258_b18 article-title: An improved epsilon constraint-handling method in moea/d for cmops with large infeasible regions publication-title: Soft Computing doi: 10.1007/s00500-019-03794-x – start-page: 105 year: 2005 ident: 10.1016/j.eswa.2022.119258_b15 article-title: Scalable test problems for evolutionary multiobjective optimization – ident: 10.1016/j.eswa.2022.119258_b34 doi: 10.1109/CEC.2014.6900645 – volume: 102 start-page: 262 year: 2018 ident: 10.1016/j.eswa.2022.119258_b23 article-title: An efficient combination of multi-objective evolutionary optimization and reliability analysis for reliability-based design optimization of truss structures publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2018.02.040 – volume: 52 start-page: 9559 year: 2022 ident: 10.1016/j.eswa.2022.119258_b45 article-title: Balancing objective optimization and constraint satisfaction in constrained evolutionary multiobjective optimization publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2020.3021138 – volume: 13 start-page: 307 year: 2009 ident: 10.1016/j.eswa.2022.119258_b2 article-title: Keel: a software tool to assess evolutionary algorithms for data mining problems publication-title: Soft Computing doi: 10.1007/s00500-008-0323-y – start-page: 53 year: 2019 ident: 10.1016/j.eswa.2022.119258_b10 article-title: A new hybrid metaheuristic for equality constrained bi-objective optimization problems – volume: 28 start-page: 26 year: 1998 ident: 10.1016/j.eswa.2022.119258_b21 article-title: Multiobjective optimization and multiple constraint handling with evolutionary algorithms. i. a unified formulation publication-title: IEEE Transactions on Systems, Man & Cybernetics, Part A (Systems & Humans) doi: 10.1109/3468.650319 – volume: 3 start-page: 257 year: 1999 ident: 10.1016/j.eswa.2022.119258_b56 article-title: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.797969 – start-page: 460 year: 2016 ident: 10.1016/j.eswa.2022.119258_b17 article-title: Angle-based constrained dominance principle in moea/d for constrained multi-objective optimization problems – volume: 49 start-page: 2060 year: 2018 ident: 10.1016/j.eswa.2022.119258_b47 article-title: Cooperative differential evolution framework for constrained multiobjective optimization publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2018.2819208 – start-page: 145 year: 2009 ident: 10.1016/j.eswa.2022.119258_b41 article-title: Infeasibility driven evolutionary algorithm for constrained optimization – volume: 67 year: 2021 ident: 10.1016/j.eswa.2022.119258_b22 article-title: Coarseemoa: An indicatorbased evolutionary algorithm for solving equality constrained multi-objective optimization problems publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2021.100983 – volume: 13 start-page: 514 year: 2009 ident: 10.1016/j.eswa.2022.119258_b49 article-title: Constraint handling in multiobjective evolutionary optimization publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2008.2009032 – volume: 67 year: 2021 ident: 10.1016/j.eswa.2022.119258_b43 article-title: Population initialization techniques for evolutionary algorithms for single-objective constrained optimization problems: Deterministic vs. stochastic techniques publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2021.100952 – volume: 75 year: 2022 ident: 10.1016/j.eswa.2022.119258_b3 article-title: An archive-based two-stage evolutionary algorithm for constrained multi-objective optimization problems publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2022.101161 – volume: 31 start-page: 337 year: 1999 ident: 10.1016/j.eswa.2022.119258_b8 article-title: Moses: A multiobjective tool for engineering design publication-title: Engineering Optimization doi: 10.1080/03052159908941377 – volume: 26 start-page: 411 year: 2018 ident: 10.1016/j.eswa.2022.119258_b24 article-title: How to specify a reference point in hypervolume calculation for fair performance comparison publication-title: Evolutionary Computation doi: 10.1162/evco_a_00226 – volume: 42 start-page: 7831 year: 2015 ident: 10.1016/j.eswa.2022.119258_b53 article-title: Backtracking search algorithm with three constraint handling methods for constrained optimization problems publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2015.05.050 – volume: 6 start-page: 182 year: 2002 ident: 10.1016/j.eswa.2022.119258_b14 article-title: A fast and elitist multiobjective genetic algorithm: Nsga-ii publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.996017 – volume: 44 start-page: 665 year: 2019 ident: 10.1016/j.eswa.2022.119258_b19 article-title: Push and pull search for solving constrained multi-objective optimization problems publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2018.08.017 – volume: 26 start-page: 646 year: 2022 ident: 10.1016/j.eswa.2022.119258_b51 article-title: A voting-mechanism based ensemble framework for constraint handling techniques publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2021.3110130 – volume: 26 start-page: 30 year: 1999 ident: 10.1016/j.eswa.2022.119258_b12 article-title: A combined genetic adaptive search (geneas) for engineering design publication-title: Computer Science and Informatics – volume: 22 start-page: 851 year: 2018 ident: 10.1016/j.eswa.2022.119258_b35 article-title: Coevolutionary multiobjective evolutionary algorithms: Survey of the state-of-the-art publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2017.2767023 – start-page: 3 year: 2011 ident: 10.1016/j.eswa.2022.119258_b11 article-title: Multi-objective optimisation using evolutionary algorithms: An introduction – volume: 50 start-page: 3086 year: 2020 ident: 10.1016/j.eswa.2022.119258_b55 article-title: Tri-goal evolution framework for constrained many-objective optimization publication-title: IEEE Transactions on Systems, Man, and Cybernetics: Systems – volume: 14 start-page: 363 year: 2014 ident: 10.1016/j.eswa.2022.119258_b28 article-title: A modified objective function method with feasibleguiding strategy to solve constrained multiobjective optimization problems publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2013.10.008 – volume: 9 start-page: 115 year: 1994 ident: 10.1016/j.eswa.2022.119258_b1 article-title: Simulated binary crossover for continuous search space publication-title: Complex Systems – volume: 60 start-page: 613 year: 2017 ident: 10.1016/j.eswa.2022.119258_b37 article-title: An evolutionary algorithm with directed weights for constrained multi-objective optimization publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2017.06.053 – volume: 52 start-page: 1716 year: 2022 ident: 10.1016/j.eswa.2022.119258_b40 article-title: Ensemble many-objective optimization algorithm based on voting mechanism publication-title: IEEE Transactions on Systems, Man, and Cybernetics: Systems doi: 10.1109/TSMC.2020.3034180 – volume: 50 start-page: 3367 year: 2020 ident: 10.1016/j.eswa.2022.119258_b6 article-title: Hyperplane assisted evolutionary algorithm for many-objective optimization problems publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2019.2899225 – volume: 46 start-page: 2938 year: 2016 ident: 10.1016/j.eswa.2022.119258_b48 article-title: Incorporating objective function information into the feasibility rule for constrained evolutionary optimization publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2015.2493239 – volume: 13 start-page: 128 year: 2013 ident: 10.1016/j.eswa.2022.119258_b27 article-title: A study of two penalty-parameterless constraint handling techniques in the framework of moea/d publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2012.07.027 – volume: 18 start-page: 602 year: 2014 ident: 10.1016/j.eswa.2022.119258_b26 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 Transactions on Evolutionary Computation doi: 10.1109/TEVC.2013.2281534 – volume: 52 start-page: 10163 year: 2022 ident: 10.1016/j.eswa.2022.119258_b32 article-title: Handling constrained multiobjective optimization problems via bidirectional coevolution publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2021.3056176 – volume: 26 start-page: 263 year: 2022 ident: 10.1016/j.eswa.2022.119258_b38 article-title: An evolutionary multitasking optimization framework for constrained multiobjective optimization problems publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2022.3145582 – volume: 18 start-page: 577 year: 2014 ident: 10.1016/j.eswa.2022.119258_b13 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 Transactions on Evolutionary Computation doi: 10.1109/TEVC.2013.2281535 – volume: 67 year: 2021 ident: 10.1016/j.eswa.2022.119258_b29 article-title: A benchmark-suite of real-world constrained multi-objective optimization problems and some baseline results publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2021.100961 – volume: 23 start-page: 303 year: 2019 ident: 10.1016/j.eswa.2022.119258_b30 article-title: Two-archive evolutionary algorithm for constrained multiobjective optimization publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2018.2855411 – volume: 11 start-page: 712 year: 2007 ident: 10.1016/j.eswa.2022.119258_b52 article-title: Moead: A multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2007.892759 – volume: 52 year: 2020 ident: 10.1016/j.eswa.2022.119258_b9 article-title: A benchmark for equality constrained multiobjective optimization publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2019.100619 – volume: 10 start-page: 658 year: 2006 ident: 10.1016/j.eswa.2022.119258_b5 article-title: A multiobjective optimization-based evolutionary algorithm for constrained optimization publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2006.872344 – volume: 74 start-page: 621 year: 2019 ident: 10.1016/j.eswa.2022.119258_b16 article-title: Moea/d with angle-based constrained dominance principle for constrained multi-objective optimization problems publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2018.10.027 – volume: 7 start-page: 174 year: 2003 ident: 10.1016/j.eswa.2022.119258_b4 article-title: The balance between proximity and diversity in multiobjective evolutionary algorithms publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2003.810761 – volume: 12 start-page: 73 year: 2017 ident: 10.1016/j.eswa.2022.119258_b44 article-title: Platemo: A matlab platform for evolutionary multi-objective optimization publication-title: IEEE Computational Intelligence Magazine doi: 10.1109/MCI.2017.2742868 – volume: 25 start-page: 102 year: 2021 ident: 10.1016/j.eswa.2022.119258_b46 article-title: A coevolutionary framework for constrained multiobjective optimization problems publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2020.3004012 – volume: 465 start-page: 232 year: 2018 ident: 10.1016/j.eswa.2022.119258_b54 article-title: Entropy based evolutionary algorithm with adaptive reference points for many-objective optimization problems publication-title: Information Sciences doi: 10.1016/j.ins.2018.07.012 – start-page: 1 year: 2022 ident: 10.1016/j.eswa.2022.119258_b39 article-title: Dynamic auxiliary task-based evolutionary multitasking for constrained multi-objective optimization publication-title: IEEE Transactions on Evolutionary Computation – volume: 23 start-page: 972 year: 2019 ident: 10.1016/j.eswa.2022.119258_b33 article-title: Evolutionary constrained multiobjective optimization: Test suite construction and performance comparisons publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2019.2896967 – volume: 1 start-page: 67 year: 1997 ident: 10.1016/j.eswa.2022.119258_b50 article-title: No free lunch theorems for optimization publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.585893 – volume: 28 start-page: 339 year: 2020 ident: 10.1016/j.eswa.2022.119258_b20 article-title: Difficulty adjustable and scalable constrained multiobjective test problem toolkit publication-title: Evolutionary Computation doi: 10.1162/evco_a_00259 – volume: 579 start-page: 89 year: 2021 ident: 10.1016/j.eswa.2022.119258_b57 article-title: A dual-population algorithm based on alternative evolution and degeneration for solving constrained multi-objective optimization problems publication-title: Information Sciences doi: 10.1016/j.ins.2021.07.078 – volume: 51 start-page: 1507 year: 2021 ident: 10.1016/j.eswa.2022.119258_b7 article-title: An adaptive resource allocation strategy for objective space partition-based multiobjective optimization publication-title: IEEE Transactions on Systems, Man, and Cybernetics: Systems – volume: 23 start-page: 870 year: 2019 ident: 10.1016/j.eswa.2022.119258_b31 article-title: Handling constrained multiobjective optimization problems with constraints in both the decision and objective spaces publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2019.2894743 – volume: 25 start-page: 739 year: 2021 ident: 10.1016/j.eswa.2022.119258_b36 article-title: A dual-population-based evolutionary algorithm for constrained multiobjective optimization publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2021.3066301 – volume: 21 start-page: 169 year: 2017 ident: 10.1016/j.eswa.2022.119258_b25 article-title: Performance of decomposition-based many-objective algorithms strongly depends on pareto front shapes publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2016.2587749 – ident: 10.1016/j.eswa.2022.119258_b42 doi: 10.1109/CEC.2010.5586545 |
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