A constrained multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism
Constrained multi-objective optimization problems (CMOPs) are common in real-world engineering application, and are difficult to solve because of the conflicting nature of the objectives and many constraints. Some constrained multi-objective evolutionary algorithms (CMOEAs) have been developed for C...
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| Vydáno v: | Applied soft computing Ročník 89; s. 106104 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.04.2020
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| ISSN: | 1568-4946, 1872-9681 |
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| Abstract | Constrained multi-objective optimization problems (CMOPs) are common in real-world engineering application, and are difficult to solve because of the conflicting nature of the objectives and many constraints. Some constrained multi-objective evolutionary algorithms (CMOEAs) have been developed for CMOPs, but they still suffer from the problems of easily getting trapped into local optimal solutions and low convergence. This paper introduces a multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism (MOEA/D-DCH) to tackle this issue. Firstly, the dynamic constraint-handling mechanism divides the search modes into the unconstrained search mode and the constrained search mode, which are dynamically adjusted by the generation number and the proportion of feasible solutions in the population. This mechanism could lead to a faster convergence than the traditional constraint-handling mechanisms. For the constrained search mode, an improved epsilon constraint-handling method is used to enhance the diversity of the population. Then, an individual update mechanism based on the best feasible solution of each sub-problem is designed to update the feasible individuals for maintaining the convergence of the feasible solutions. Finally, MOEA/D-DCH dynamically regulates the parameters of the differential evolution operator to enhance the local search ability. Experiments on 21 benchmark test functions are conducted to test MOEA/D-DCH and five other typical CMOEAs. Meanwhile, a real-world problem is employed to evaluate the practical performance of MOEA/D-DCH. MOEA/D-DCH achieves significantly better results than the other five algorithms on most of the test problems. The results indicate the effectiveness and competitiveness of MOEA/D-DCH for solving CMOPs.
•A MOEA based on decomposition and dynamic constraint-handling mechanism for CMOPs.•Search modes based on the unconstrained search and the constrained search.•An improved epsilon constraint handling technology to maintain population diversity.•A selection operator based on best feasible solutions to update the individuals.•Dynamical adjustment of DE parameters to enhance the local search ability. |
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| AbstractList | Constrained multi-objective optimization problems (CMOPs) are common in real-world engineering application, and are difficult to solve because of the conflicting nature of the objectives and many constraints. Some constrained multi-objective evolutionary algorithms (CMOEAs) have been developed for CMOPs, but they still suffer from the problems of easily getting trapped into local optimal solutions and low convergence. This paper introduces a multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism (MOEA/D-DCH) to tackle this issue. Firstly, the dynamic constraint-handling mechanism divides the search modes into the unconstrained search mode and the constrained search mode, which are dynamically adjusted by the generation number and the proportion of feasible solutions in the population. This mechanism could lead to a faster convergence than the traditional constraint-handling mechanisms. For the constrained search mode, an improved epsilon constraint-handling method is used to enhance the diversity of the population. Then, an individual update mechanism based on the best feasible solution of each sub-problem is designed to update the feasible individuals for maintaining the convergence of the feasible solutions. Finally, MOEA/D-DCH dynamically regulates the parameters of the differential evolution operator to enhance the local search ability. Experiments on 21 benchmark test functions are conducted to test MOEA/D-DCH and five other typical CMOEAs. Meanwhile, a real-world problem is employed to evaluate the practical performance of MOEA/D-DCH. MOEA/D-DCH achieves significantly better results than the other five algorithms on most of the test problems. The results indicate the effectiveness and competitiveness of MOEA/D-DCH for solving CMOPs.
•A MOEA based on decomposition and dynamic constraint-handling mechanism for CMOPs.•Search modes based on the unconstrained search and the constrained search.•An improved epsilon constraint handling technology to maintain population diversity.•A selection operator based on best feasible solutions to update the individuals.•Dynamical adjustment of DE parameters to enhance the local search ability. |
| ArticleNumber | 106104 |
| Author | Yang, Yongkuan Liu, Jianchang Tan, Shubin |
| Author_xml | – sequence: 1 givenname: Yongkuan surname: Yang fullname: Yang, Yongkuan – sequence: 2 givenname: Jianchang orcidid: 0000-0002-2801-8312 surname: Liu fullname: Liu, Jianchang email: liujianchang@ise.neu.edu.cn – sequence: 3 givenname: Shubin surname: Tan fullname: Tan, Shubin |
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| Cites_doi | 10.1109/TEVC.2014.2350987 10.1080/0305215X.2010.493937 10.1080/0305215X.2016.1271661 10.1162/evco_a_00259 10.1007/s00500-012-0816-6 10.1016/j.swevo.2018.08.017 10.1109/TEVC.2008.925798 10.1109/TEVC.2012.2196800 10.1007/s00500-006-0068-4 10.1016/j.ins.2018.01.014 10.1016/j.asoc.2012.07.027 10.1023/A:1008202821328 10.1016/S0045-7825(99)00389-8 10.1016/j.asoc.2018.02.048 10.1109/4235.996017 10.1109/TCYB.2014.2337117 10.1016/j.asoc.2018.10.027 10.1016/j.swevo.2011.10.001 10.1080/03052150210915 10.1016/j.asoc.2019.02.041 10.1016/j.asoc.2017.06.053 10.1016/j.ifacol.2018.09.294 10.1109/TEVC.2007.892759 10.1109/TEVC.2013.2281534 10.1109/TEVC.2003.810761 10.1109/TEVC.2008.2009032 10.1109/TEVC.2017.2669098 10.1109/TEVC.2010.2093582 |
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| References | Ray, Singh, Isaacs, Smith (b12) 2009 Deb (b16) 2000; 186 Deb, Pratap, Agarwal, Meyarivan (b17) 2002; 6 Zhang, Zhou, Zhao, Suganthan, Liu, Tiwari (b32) 2008 Li, Wang, Yang, Cai (b23) 2016 Jain, Deb (b33) 2014; 18 Ponsich, Jaimes, Coello (b5) 2013; 17 Peng, Liu, Gu (b20) 2017; 60 Mezura-Montes, Coello (b9) 2011; 1 Fan, Fang, Li, Cai, Wei, Goodman (b34) 2019; 74 Liu, Wang (b14) 2019 Ray, Liew (b36) 2002; 34 Yang, Liu, Tan, Wang (b28) 2019; 80 Wang, Jiao, Yao (b1) 2015; 19 Z. Fan, W. Li, X. Cai, H. Li, C. Wei, Q. Zhang, K. Deb, E. Goodman, Difficulty Adjustable and Scalable Constrained Multi-objective Test Problem Toolkit, Evol. Comput., 0 (ja) 0, 1–28, [in press] Posted Online May 23, 2019. Wang, Cai (b29) 2012; 16 Coello Coello (b15) 2012 Li, Zhang (b24) 2009; 13 Woldesenbet, Yen, Tessema (b10) 2009; 13 Castillo, Trujillo, Melin (b6) 2007; 11 Jan, Khanum (b11) 2013; 13 Fan, Li, Wei, Li, Huang, Cai, Cai (b18) 2016 Qian, Xu, Qi, Tianfield (b8) 2012; 16 Bosman, Thierens (b35) 2003; 7 Zhang, Li (b25) 2007; 11 Xu, Chen, Tao (b30) 2018; 435 Takahama, Sakai (b19) 2013 Yang, Liu, Tan, Wang (b4) 2018; 51 Storn, Price (b27) 1997; 11 Fan, Li, Cai, Li, Wei, Zhang, Deb, Goodman (b13) 2019; 44 Ning, Guo, Yan, Wu, Wu, Zhao (b21) 2017; 49 . Lin, Du, Du (b7) 2018 Qu, Suganthan (b22) 2011; 43 Li, Cheng, Liu, Jin (b3) 2018; 67 Wang, Li, Yen, Song (b2) 2015; 45 Wang, Xu, Sun, Yang (b26) 2017; 21 Qian (10.1016/j.asoc.2020.106104_b8) 2012; 16 Zhang (10.1016/j.asoc.2020.106104_b32) 2008 Yang (10.1016/j.asoc.2020.106104_b4) 2018; 51 Jain (10.1016/j.asoc.2020.106104_b33) 2014; 18 Zhang (10.1016/j.asoc.2020.106104_b25) 2007; 11 Yang (10.1016/j.asoc.2020.106104_b28) 2019; 80 Xu (10.1016/j.asoc.2020.106104_b30) 2018; 435 Ponsich (10.1016/j.asoc.2020.106104_b5) 2013; 17 Castillo (10.1016/j.asoc.2020.106104_b6) 2007; 11 Qu (10.1016/j.asoc.2020.106104_b22) 2011; 43 Li (10.1016/j.asoc.2020.106104_b3) 2018; 67 Peng (10.1016/j.asoc.2020.106104_b20) 2017; 60 Wang (10.1016/j.asoc.2020.106104_b29) 2012; 16 Woldesenbet (10.1016/j.asoc.2020.106104_b10) 2009; 13 Ray (10.1016/j.asoc.2020.106104_b36) 2002; 34 Mezura-Montes (10.1016/j.asoc.2020.106104_b9) 2011; 1 Fan (10.1016/j.asoc.2020.106104_b34) 2019; 74 Wang (10.1016/j.asoc.2020.106104_b2) 2015; 45 Lin (10.1016/j.asoc.2020.106104_b7) 2018 Wang (10.1016/j.asoc.2020.106104_b1) 2015; 19 Wang (10.1016/j.asoc.2020.106104_b26) 2017; 21 Takahama (10.1016/j.asoc.2020.106104_b19) 2013 Li (10.1016/j.asoc.2020.106104_b24) 2009; 13 Deb (10.1016/j.asoc.2020.106104_b16) 2000; 186 Fan (10.1016/j.asoc.2020.106104_b18) 2016 Ning (10.1016/j.asoc.2020.106104_b21) 2017; 49 Coello Coello (10.1016/j.asoc.2020.106104_b15) 2012 Storn (10.1016/j.asoc.2020.106104_b27) 1997; 11 Deb (10.1016/j.asoc.2020.106104_b17) 2002; 6 Jan (10.1016/j.asoc.2020.106104_b11) 2013; 13 10.1016/j.asoc.2020.106104_b31 Liu (10.1016/j.asoc.2020.106104_b14) 2019 Li (10.1016/j.asoc.2020.106104_b23) 2016 Bosman (10.1016/j.asoc.2020.106104_b35) 2003; 7 Ray (10.1016/j.asoc.2020.106104_b12) 2009 Fan (10.1016/j.asoc.2020.106104_b13) 2019; 44 |
| References_xml | – volume: 1 start-page: 173 year: 2011 end-page: 194 ident: b9 article-title: Constraint-handling in nature-inspired numerical optimization: Past, present and future publication-title: Swarm Evol. Comput. – volume: 51 start-page: 168 year: 2018 end-page: 173 ident: b4 article-title: Application of constrained multi-objective evolutionary algorithm in multi-source compressed-air pipeline optimization problems publication-title: IFAC-PapersOnLine – start-page: 1 year: 2016 end-page: 8 ident: b18 article-title: An improved epsilon constraint handling method embedded in MOEA/d for constrained multi-objective optimization problems publication-title: 2016 IEEE Symposium Series on Computational Intelligence (SSCI) – volume: 186 start-page: 311 year: 2000 end-page: 338 ident: b16 article-title: An efficient constraint handling method for genetic algorithms publication-title: Comput. Methods Appl. Mech. Eng. – volume: 49 start-page: 1645 year: 2017 end-page: 1664 ident: b21 article-title: Constrained multi-objective optimization using constrained non-dominated sorting combined with an improved hybrid multi-objective evolutionary algorithm publication-title: Eng. Optim. – start-page: 4175 year: 2016 end-page: 4182 ident: b23 article-title: A comparative study of constraint-handling techniques in evolutionary constrained multiobjective optimization publication-title: 2016 IEEE Congress on Evolutionary Computation (CEC) – volume: 13 start-page: 128 year: 2013 end-page: 148 ident: b11 article-title: A study of two penalty-parameterless constraint handling techniques in the framework of MOEA/D publication-title: Appl. Soft Comput. J. – reference: Z. Fan, W. Li, X. Cai, H. Li, C. Wei, Q. Zhang, K. Deb, E. Goodman, Difficulty Adjustable and Scalable Constrained Multi-objective Test Problem Toolkit, Evol. Comput., 0 (ja) 0, 1–28, [in press] Posted Online May 23, 2019. – volume: 435 start-page: 240 year: 2018 end-page: 262 ident: b30 article-title: Differential evolution with adaptive trial vector generation strategy and cluster-replacement-based feasibility rule for constrained optimization publication-title: Inform. Sci. – volume: 21 start-page: 665 year: 2017 end-page: 680 ident: b26 article-title: A two-phase differential evolution for uniform designs in constrained experimental domains publication-title: IEEE Trans. Evol. Comput. – start-page: 1 year: 2019 ident: b14 article-title: Handling constrained multiobjective optimization problems with constraints in both the decision and objective spaces publication-title: IEEE Trans. Evol. Comput. – volume: 17 start-page: 321 year: 2013 end-page: 344 ident: b5 article-title: A survey on multiobjective evolutionary algorithms for the solution of the portfolio optimization problem and other finance and economics applications publication-title: IEEE Trans. Evol. Comput. – volume: 11 start-page: 269 year: 2007 end-page: 279 ident: b6 article-title: Multiple objective genetic algorithms for path-planning optimization in autonomous mobile robots publication-title: Soft Comput. – volume: 7 start-page: 174 year: 2003 end-page: 188 ident: b35 article-title: The balance between proximity and diversity in multiobjective evolutionary algorithms publication-title: IEEE Trans. Evol. Comput. – volume: 18 start-page: 602 year: 2014 end-page: 622 ident: b33 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. Evol. Comput. – volume: 67 start-page: 245 year: 2018 end-page: 260 ident: b3 article-title: A two-stage R2 indicator based evolutionary algorithm for many-objective optimization publication-title: Appl. Soft Comput. – volume: 80 start-page: 42 year: 2019 end-page: 56 ident: b28 article-title: A multi-objective differential evolutionary algorithm for constrained multi-objective optimization problems with low feasible ratio publication-title: Appl. Soft Comput. – volume: 6 start-page: 182 year: 2002 end-page: 197 ident: b17 article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II publication-title: IEEE Trans. Evol. Comput. – start-page: 1 year: 2008 end-page: 20 ident: b32 article-title: Multiobjective Optimization Test Instances for the CEC 2009 Special Session and Competition – volume: 34 start-page: 141 year: 2002 end-page: 153 ident: b36 article-title: A swarm metaphor for multiobjective design optimization publication-title: Eng. Optim. – start-page: 1 year: 2018 end-page: 15 ident: b7 article-title: Multi-objective differential evolution with dynamic hybrid constraint handling mechanism publication-title: Soft Computing – volume: 11 start-page: 341 year: 1997 end-page: 359 ident: b27 article-title: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces publication-title: J. Global Optim. – volume: 16 start-page: 1353 year: 2012 end-page: 1372 ident: b8 article-title: Self-adaptive differential evolution algorithm with publication-title: Soft Comput. – volume: 11 start-page: 712 year: 2007 end-page: 731 ident: b25 article-title: MOEA/D: A multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Trans. Evol. Comput. – reference: . – volume: 45 start-page: 830 year: 2015 end-page: 843 ident: b2 article-title: MOMMOP: Multiobjective optimization for locating multiple optimal solutions of multimodal optimization problems publication-title: IEEE Trans. Cybern. – volume: 60 start-page: 613 year: 2017 end-page: 622 ident: b20 article-title: An evolutionary algorithm with directed weights for constrained multi-objective optimization publication-title: Appl. Soft Comput. – volume: 19 start-page: 524 year: 2015 end-page: 541 ident: b1 article-title: Two arch2: An improved two-archive algorithm for many-objective optimization publication-title: IEEE Trans. Evol. Comput. – volume: 44 start-page: 665 year: 2019 end-page: 679 ident: b13 article-title: Push and pull search for solving constrained multi-objective optimization problems publication-title: Swarm Evol. Comput. – volume: 13 start-page: 284 year: 2009 end-page: 302 ident: b24 article-title: Multiobjective optimization problems with complicated pareto sets, MOEA/D and NSGA-II publication-title: IEEE Trans. Evol. Comput. – start-page: 849 year: 2012 end-page: 872 ident: b15 article-title: Constraint-handling techniques used with evolutionary algorithms publication-title: Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation – volume: 43 start-page: 403 year: 2011 end-page: 416 ident: b22 article-title: Constrained multi-objective optimization algorithm with an ensemble of constraint handling methods publication-title: Eng. Optim. – volume: 74 start-page: 621 year: 2019 end-page: 633 ident: b34 article-title: MOEA/d with angle-based constrained dominance principle for constrained multi-objective optimization problems publication-title: Appl. Soft Comput. – volume: 13 start-page: 514 year: 2009 end-page: 525 ident: b10 article-title: Constraint handling in multiobjective evolutionary optimization publication-title: IEEE Trans. Evol. Comput. – start-page: 1334 year: 2013 end-page: 1341 ident: b19 article-title: Efficient constrained optimization by the publication-title: IEEE Congress on Evolutionary Computation – volume: 16 start-page: 117 year: 2012 end-page: 134 ident: b29 article-title: Combining multiobjective optimization with differential evolution to solve constrained optimization problems publication-title: IEEE Trans. Evol. Comput. – start-page: 145 year: 2009 end-page: 165 ident: b12 article-title: Infeasibility Driven Evolutionary Algorithm for Constrained Optimization – start-page: 1334 year: 2013 ident: 10.1016/j.asoc.2020.106104_b19 article-title: Efficient constrained optimization by the ε constrained differential evolution with rough approximation using kernel regression – volume: 19 start-page: 524 issue: 4 year: 2015 ident: 10.1016/j.asoc.2020.106104_b1 article-title: Two arch2: An improved two-archive algorithm for many-objective optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2014.2350987 – start-page: 145 year: 2009 ident: 10.1016/j.asoc.2020.106104_b12 – volume: 43 start-page: 403 issue: 4 year: 2011 ident: 10.1016/j.asoc.2020.106104_b22 article-title: Constrained multi-objective optimization algorithm with an ensemble of constraint handling methods publication-title: Eng. Optim. doi: 10.1080/0305215X.2010.493937 – volume: 49 start-page: 1645 issue: 10 year: 2017 ident: 10.1016/j.asoc.2020.106104_b21 article-title: Constrained multi-objective optimization using constrained non-dominated sorting combined with an improved hybrid multi-objective evolutionary algorithm publication-title: Eng. Optim. doi: 10.1080/0305215X.2016.1271661 – ident: 10.1016/j.asoc.2020.106104_b31 doi: 10.1162/evco_a_00259 – volume: 16 start-page: 1353 issue: 8 year: 2012 ident: 10.1016/j.asoc.2020.106104_b8 article-title: Self-adaptive differential evolution algorithm with α-constrained-domination principle for constrained multi-objective optimization publication-title: Soft Comput. doi: 10.1007/s00500-012-0816-6 – start-page: 849 year: 2012 ident: 10.1016/j.asoc.2020.106104_b15 article-title: Constraint-handling techniques used with evolutionary algorithms – volume: 44 start-page: 665 year: 2019 ident: 10.1016/j.asoc.2020.106104_b13 article-title: Push and pull search for solving constrained multi-objective optimization problems publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2018.08.017 – start-page: 4175 year: 2016 ident: 10.1016/j.asoc.2020.106104_b23 article-title: A comparative study of constraint-handling techniques in evolutionary constrained multiobjective optimization – volume: 13 start-page: 284 issue: 2 year: 2009 ident: 10.1016/j.asoc.2020.106104_b24 article-title: Multiobjective optimization problems with complicated pareto sets, MOEA/D and NSGA-II publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2008.925798 – volume: 17 start-page: 321 issue: 3 year: 2013 ident: 10.1016/j.asoc.2020.106104_b5 article-title: A survey on multiobjective evolutionary algorithms for the solution of the portfolio optimization problem and other finance and economics applications publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2012.2196800 – volume: 11 start-page: 269 issue: 3 year: 2007 ident: 10.1016/j.asoc.2020.106104_b6 article-title: Multiple objective genetic algorithms for path-planning optimization in autonomous mobile robots publication-title: Soft Comput. doi: 10.1007/s00500-006-0068-4 – volume: 435 start-page: 240 year: 2018 ident: 10.1016/j.asoc.2020.106104_b30 article-title: Differential evolution with adaptive trial vector generation strategy and cluster-replacement-based feasibility rule for constrained optimization publication-title: Inform. Sci. doi: 10.1016/j.ins.2018.01.014 – volume: 13 start-page: 128 issue: 1 year: 2013 ident: 10.1016/j.asoc.2020.106104_b11 article-title: A study of two penalty-parameterless constraint handling techniques in the framework of MOEA/D publication-title: Appl. Soft Comput. J. doi: 10.1016/j.asoc.2012.07.027 – start-page: 1 year: 2016 ident: 10.1016/j.asoc.2020.106104_b18 article-title: An improved epsilon constraint handling method embedded in MOEA/d for constrained multi-objective optimization problems – volume: 11 start-page: 341 issue: 4 year: 1997 ident: 10.1016/j.asoc.2020.106104_b27 article-title: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces publication-title: J. Global Optim. doi: 10.1023/A:1008202821328 – volume: 186 start-page: 311 issue: 2 year: 2000 ident: 10.1016/j.asoc.2020.106104_b16 article-title: An efficient constraint handling method for genetic algorithms publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/S0045-7825(99)00389-8 – volume: 67 start-page: 245 year: 2018 ident: 10.1016/j.asoc.2020.106104_b3 article-title: A two-stage R2 indicator based evolutionary algorithm for many-objective optimization publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2018.02.048 – volume: 6 start-page: 182 issue: 2 year: 2002 ident: 10.1016/j.asoc.2020.106104_b17 article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/4235.996017 – start-page: 1 year: 2008 ident: 10.1016/j.asoc.2020.106104_b32 – volume: 45 start-page: 830 issue: 4 year: 2015 ident: 10.1016/j.asoc.2020.106104_b2 article-title: MOMMOP: Multiobjective optimization for locating multiple optimal solutions of multimodal optimization problems publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2014.2337117 – volume: 74 start-page: 621 year: 2019 ident: 10.1016/j.asoc.2020.106104_b34 article-title: MOEA/d with angle-based constrained dominance principle for constrained multi-objective optimization problems publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2018.10.027 – start-page: 1 year: 2018 ident: 10.1016/j.asoc.2020.106104_b7 article-title: Multi-objective differential evolution with dynamic hybrid constraint handling mechanism publication-title: Soft Computing – volume: 1 start-page: 173 issue: 4 year: 2011 ident: 10.1016/j.asoc.2020.106104_b9 article-title: Constraint-handling in nature-inspired numerical optimization: Past, present and future publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2011.10.001 – volume: 34 start-page: 141 issue: 2 year: 2002 ident: 10.1016/j.asoc.2020.106104_b36 article-title: A swarm metaphor for multiobjective design optimization publication-title: Eng. Optim. doi: 10.1080/03052150210915 – volume: 80 start-page: 42 year: 2019 ident: 10.1016/j.asoc.2020.106104_b28 article-title: A multi-objective differential evolutionary algorithm for constrained multi-objective optimization problems with low feasible ratio publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2019.02.041 – volume: 60 start-page: 613 year: 2017 ident: 10.1016/j.asoc.2020.106104_b20 article-title: An evolutionary algorithm with directed weights for constrained multi-objective optimization publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2017.06.053 – volume: 51 start-page: 168 issue: 18 year: 2018 ident: 10.1016/j.asoc.2020.106104_b4 article-title: Application of constrained multi-objective evolutionary algorithm in multi-source compressed-air pipeline optimization problems publication-title: IFAC-PapersOnLine doi: 10.1016/j.ifacol.2018.09.294 – volume: 11 start-page: 712 issue: 6 year: 2007 ident: 10.1016/j.asoc.2020.106104_b25 article-title: MOEA/D: A multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2007.892759 – start-page: 1 year: 2019 ident: 10.1016/j.asoc.2020.106104_b14 article-title: Handling constrained multiobjective optimization problems with constraints in both the decision and objective spaces publication-title: IEEE Trans. Evol. Comput. – volume: 18 start-page: 602 issue: 4 year: 2014 ident: 10.1016/j.asoc.2020.106104_b33 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. Evol. Comput. doi: 10.1109/TEVC.2013.2281534 – volume: 7 start-page: 174 issue: 2 year: 2003 ident: 10.1016/j.asoc.2020.106104_b35 article-title: The balance between proximity and diversity in multiobjective evolutionary algorithms publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2003.810761 – volume: 13 start-page: 514 issue: 3 year: 2009 ident: 10.1016/j.asoc.2020.106104_b10 article-title: Constraint handling in multiobjective evolutionary optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2008.2009032 – volume: 21 start-page: 665 issue: 5 year: 2017 ident: 10.1016/j.asoc.2020.106104_b26 article-title: A two-phase differential evolution for uniform designs in constrained experimental domains publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2017.2669098 – volume: 16 start-page: 117 issue: 1 year: 2012 ident: 10.1016/j.asoc.2020.106104_b29 article-title: Combining multiobjective optimization with differential evolution to solve constrained optimization problems publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2010.2093582 |
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