A surrogate-assisted evolution strategy for constrained multi-objective optimization
•New surrogate-assisted ES for constrained multi-objective optimization is developed.•Surrogates are used to identify the most promising among many trial offspring.•A radial basis function (RBF) model is used to implement the method.•Method is tested on benchmark problems and manufacturing and robot...
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| Published in: | Expert systems with applications Vol. 57; pp. 270 - 284 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
15.09.2016
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| Subjects: | |
| ISSN: | 0957-4174, 1873-6793 |
| Online Access: | Get full text |
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| Abstract | •New surrogate-assisted ES for constrained multi-objective optimization is developed.•Surrogates are used to identify the most promising among many trial offspring.•A radial basis function (RBF) model is used to implement the method.•Method is tested on benchmark problems and manufacturing and robotics applications.•Proposed method generally outperforms an ES and NSGA-II on the problems used.
In many real-world optimization problems, several conflicting objectives must be achieved and optimized simultaneously and the solutions are often required to satisfy certain restrictions or constraints. Moreover, in some applications, the numerical values of the objectives and constraints are obtained from computationally expensive simulations. Many multi-objective optimization algorithms for continuous optimization have been proposed in the literature and some have been incorporated or used in conjunction with expert and intelligent systems. However, relatively few of these multi-objective algorithms handle constraints, and even fewer, use surrogates to approximate the objective or constraint functions when these functions are computationally expensive. This paper proposes a surrogate-assisted evolution strategy (ES) that can be used for constrained multi-objective optimization of expensive black-box objective functions subject to expensive black-box inequality constraints. Such an algorithm can be incorporated into an intelligent system that finds approximate Pareto optimal solutions to simulation-based constrained multi-objective optimization problems in various applications including engineering design optimization, production management and manufacturing. The main idea in the proposed algorithm is to generate a large number of trial offspring in each generation and use the surrogates to predict the objective and constraint function values of these trial offspring. Then the algorithm performs an approximate non-dominated sort of the trial offspring based on the predicted objective and constraint function values, and then it selects the most promising offspring (those with the smallest predicted ranks from the non-dominated sort) to become the actual offspring for the current generation that will be evaluated using the expensive objective and constraint functions. The proposed method is implemented using cubic radial basis function (RBF) surrogate models to assist the ES. The resulting RBF-assisted ES is compared with the original ES and to NSGA-II on 20 test problems involving 2–15 decision variables, 2–5 objectives and up to 13 inequality constraints. These problems include well-known benchmark problems and application problems in manufacturing and robotics. The numerical results showed that the RBF-assisted ES generally outperformed the original ES and NSGA-II on the problems used when the computational budget is relatively limited. These results suggest that the proposed surrogate-assisted ES is promising for computationally expensive constrained multi-objective optimization. |
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| AbstractList | In many real-world optimization problems, several conflicting objectives must be achieved and optimized simultaneously and the solutions are often required to satisfy certain restrictions or constraints. Moreover, in some applications, the numerical values of the objectives and constraints are obtained from computationally expensive simulations. Many multi-objective optimization algorithms for continuous optimization have been proposed in the literature and some have been incorporated or used in conjunction with expert and intelligent systems. However, relatively few of these multi-objective algorithms handle constraints, and even fewer, use surrogates to approximate the objective or constraint functions when these functions are computationally expensive. This paper proposes a surrogate-assisted evolution strategy (ES) that can be used for constrained multi-objective optimization of expensive black-box objective functions subject to expensive black-box inequality constraints. Such an algorithm can be incorporated into an intelligent system that finds approximate Pareto optimal solutions to simulation-based constrained multi-objective optimization problems in various applications including engineering design optimization, production management and manufacturing. The main idea in the proposed algorithm is to generate a large number of trial offspring in each generation and use the surrogates to predict the objective and constraint function values of these trial offspring. Then the algorithm performs an approximate non-dominated sort of the trial offspring based on the predicted objective and constraint function values, and then it selects the most promising offspring (those with the smallest predicted ranks from the non-dominated sort) to become the actual offspring for the current generation that will be evaluated using the expensive objective and constraint functions. The proposed method is implemented using cubic radial basis function (RBF) surrogate models to assist the ES. The resulting RBF-assisted ES is compared with the original ES and to NSGA-II on 20 test problems involving 2-15 decision variables, 2-5 objectives and up to 13 inequality constraints. These problems include well-known benchmark problems and application problems in manufacturing and robotics. The numerical results showed that the RBF-assisted ES generally outperformed the original ES and NSGA-II on the problems used when the computational budget is relatively limited. These results suggest that the proposed surrogate-assisted ES is promising for computationally expensive constrained multi-objective optimization. •New surrogate-assisted ES for constrained multi-objective optimization is developed.•Surrogates are used to identify the most promising among many trial offspring.•A radial basis function (RBF) model is used to implement the method.•Method is tested on benchmark problems and manufacturing and robotics applications.•Proposed method generally outperforms an ES and NSGA-II on the problems used. In many real-world optimization problems, several conflicting objectives must be achieved and optimized simultaneously and the solutions are often required to satisfy certain restrictions or constraints. Moreover, in some applications, the numerical values of the objectives and constraints are obtained from computationally expensive simulations. Many multi-objective optimization algorithms for continuous optimization have been proposed in the literature and some have been incorporated or used in conjunction with expert and intelligent systems. However, relatively few of these multi-objective algorithms handle constraints, and even fewer, use surrogates to approximate the objective or constraint functions when these functions are computationally expensive. This paper proposes a surrogate-assisted evolution strategy (ES) that can be used for constrained multi-objective optimization of expensive black-box objective functions subject to expensive black-box inequality constraints. Such an algorithm can be incorporated into an intelligent system that finds approximate Pareto optimal solutions to simulation-based constrained multi-objective optimization problems in various applications including engineering design optimization, production management and manufacturing. The main idea in the proposed algorithm is to generate a large number of trial offspring in each generation and use the surrogates to predict the objective and constraint function values of these trial offspring. Then the algorithm performs an approximate non-dominated sort of the trial offspring based on the predicted objective and constraint function values, and then it selects the most promising offspring (those with the smallest predicted ranks from the non-dominated sort) to become the actual offspring for the current generation that will be evaluated using the expensive objective and constraint functions. The proposed method is implemented using cubic radial basis function (RBF) surrogate models to assist the ES. The resulting RBF-assisted ES is compared with the original ES and to NSGA-II on 20 test problems involving 2–15 decision variables, 2–5 objectives and up to 13 inequality constraints. These problems include well-known benchmark problems and application problems in manufacturing and robotics. The numerical results showed that the RBF-assisted ES generally outperformed the original ES and NSGA-II on the problems used when the computational budget is relatively limited. These results suggest that the proposed surrogate-assisted ES is promising for computationally expensive constrained multi-objective optimization. |
| Author | Datta, Rituparna Regis, Rommel G. |
| Author_xml | – sequence: 1 givenname: Rituparna surname: Datta fullname: Datta, Rituparna email: rdatta@iitk.ac.in organization: Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, Uttar Pradesh, India – sequence: 2 givenname: Rommel G. surname: Regis fullname: Regis, Rommel G. email: rregis@sju.edu organization: Department of Mathematics, Saint Joseph’s University, 5600 City Avenue, Philadelphia, PA 19131, USA |
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| Cites_doi | 10.1109/4235.797969 10.1016/j.amc.2012.04.057 10.1080/03052150600744555 10.1109/TEVC.2004.826067 10.1016/j.asoc.2008.04.001 10.1162/evco.1996.4.1.1 10.1023/A:1008306431147 10.1023/A:1011255519438 10.1016/j.eswa.2014.11.020 10.1007/s10898-015-0270-y 10.1016/S0736-5845(02)00068-6 10.1007/BF01742932 10.1080/03052150903143935 10.1109/TEVC.2004.835247 10.1109/TEVC.2005.851274 10.1016/j.cor.2010.09.013 10.1080/0305215X.2010.502935 10.1080/03052150600882538 10.1007/s10044-013-0345-7 10.1007/s10898-013-0118-2 10.1137/10079731X 10.1016/j.eswa.2013.01.054 10.1137/120864738 10.1016/j.ejor.2009.11.010 10.1109/4235.996017 10.1109/TEVC.2013.2262111 10.1109/TEVC.2005.859463 10.1016/j.engappai.2005.06.007 10.1016/0957-4174(96)00008-5 10.1137/070691814 10.1016/j.eswa.2012.02.197 10.1162/106365600568167 10.1016/j.ijpe.2013.10.016 10.1155/2012/534783 10.1057/jos.2015.5 10.1504/IJPD.2009.026179 10.1109/TEVC.2007.892759 10.1109/TEVC.2009.2033671 10.1002/fld.2282 |
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| References | Acar (bib0001) 2015; 42 Knowles (bib0029) 2006; 10 Emmerich, Giannakoglou, Naujoks (bib0017) 2006; 10 Deb (bib0013) 2001 Zhang, Li (bib0058) 2007; 11 Husain, Lee, Kim (bib0024) 2011; 66 Akhtar, Shoemaker (bib0002) 2016; 64 Fan, Li, Cai, Li, Hu, Yin (bib0018) 2015 Wagner, Emmerich, Deutz, Ponweiser (bib0056) 2010 Zhang, Liu, Tsang, Virginas (bib0059) 2010; 14 Kanyakam, Bureerat (bib0027) 2012; 2012 Lin, S. (2011). NPGM – A NSGA program in matlab, version 1.4. Jones, Schonlau, Welch (bib0026) 1998; 13 Accessed 05.03.14. Redondo, Sedano, Vera, Hernando, Corchado (bib0046) 2013; 18 Kunakote, Bureerat (bib0032) 2011; 43 Osyczka, Kuchta, Czula (bib0040) 1994; 8 Ponweiser, Wagner, Biermann, Vincze (bib0041) 2008 Andrés, Salcedo-Sanz, Monge, Pérez-Bellido (bib0003) 2012; 39 Singh, Couckuyt, Ferranti, Dhaene (bib0054) 2014 Liu, Sun (bib0035) 2013; 40 Couckuyt, Deschrijver, Dhaene (bib0009) 2014; 60 Gansterer, Almeder, Hartl (bib0021) 2014; 151 Regis, Shoemaker (bib0049) 2004; 8 Knowles, Corne (bib0030) 2000; 8 Cus, Balic (bib0010) 2003; 19 Accessed 07.02.15. Bäck (bib0005) 1996 Deb, Agrawal, Pratap, Meyarivan (bib0015) 2002; 6 Coello, Pulido, Lechuga (bib0008) 2004; 8 Saravanan, Ramabalan, Ebenezer, Dharmaraja (bib0052) 2009; 9 Ortiz, G. A. (2012). Multi-objective optimization using evolution strategies (ES) as evolutionary algorithm (EA). Knowles, Nakayama (bib0031) 2008 Bureerat, Srisomporn (bib0007) 2010; 42 Custódio, Madeira, Vaz, Vicente (bib0011) 2011; 21 Deb, Agrawal, Pratap, Meyarivan (bib0014) 2000 Kasperska, Ostwald (bib0028) 2006; 38 Mezura-Montes, Cetina-Dominguez (bib0036) 2012; 218 Powell (bib0042) 1992 Deb, Goel (bib0016) 2001 Lee, Kim (bib0033) 1996; 11 Bhattacharya (bib0006) 2007 Ryu, Kim (bib0051) 2014; 24 Audet, Savard, Zghal (bib0004) 2010; 204 Gutmann (bib0023) 2001; 19 Forrester, Sobester, Keane (bib0020) 2008 Tesch, Schneider, Choset (bib0055) 2013 Zitzler, Thiele (bib0061) 1999; 3 Regis (bib0050) 2013 Regis (bib0048) 2014; 18 Datta, Deb (bib0012) 2011 Wild, Regis, Shoemaker (bib0057) 2008; 30 Fan, Z., Li, W., Cai, X., Lin, H., Xie, S., & Goodman, E. (2015b). A new repair operator for multi-objective evolutionary algorithm in constrained optimization problems . CoRR, abs/1504.00154, 2015. Güller, Uygun, Noche (bib0022) 2015; 9 Quiza Sardiñas, Rivas Santana, Alfonso Brindis (bib0043) 2006; 19 Isaacs, Ray, Smith (bib0025) 2009; 9 Ray, Smith (bib0045) 2006; 38 Osyczka (bib0039) 2002 Regis (bib0047) 2011; 38 Rao (bib0044) 2009 Michalewicz, Schoenauer (bib0037) 1996; 4 Zitzler, Thiele (bib0060) 1998 Shi, Rasheed (bib0053) 2008 Bureerat (10.1016/j.eswa.2016.03.044_bib0007) 2010; 42 Mezura-Montes (10.1016/j.eswa.2016.03.044_bib0036) 2012; 218 Deb (10.1016/j.eswa.2016.03.044_bib0013) 2001 Güller (10.1016/j.eswa.2016.03.044_bib0022) 2015; 9 Gansterer (10.1016/j.eswa.2016.03.044_bib0021) 2014; 151 Wagner (10.1016/j.eswa.2016.03.044_bib0056) 2010 Deb (10.1016/j.eswa.2016.03.044_bib0015) 2002; 6 Lee (10.1016/j.eswa.2016.03.044_bib0033) 1996; 11 Zhang (10.1016/j.eswa.2016.03.044_bib0059) 2010; 14 10.1016/j.eswa.2016.03.044_bib0019 Akhtar (10.1016/j.eswa.2016.03.044_bib0002) 2016; 64 Knowles (10.1016/j.eswa.2016.03.044_bib0029) 2006; 10 Kasperska (10.1016/j.eswa.2016.03.044_bib0028) 2006; 38 Deb (10.1016/j.eswa.2016.03.044_bib0016) 2001 Shi (10.1016/j.eswa.2016.03.044_bib0053) 2008 Redondo (10.1016/j.eswa.2016.03.044_bib0046) 2013; 18 Acar (10.1016/j.eswa.2016.03.044_bib0001) 2015; 42 Knowles (10.1016/j.eswa.2016.03.044_bib0031) 2008 Wild (10.1016/j.eswa.2016.03.044_bib0057) 2008; 30 Zitzler (10.1016/j.eswa.2016.03.044_bib0061) 1999; 3 Regis (10.1016/j.eswa.2016.03.044_bib0047) 2011; 38 Ryu (10.1016/j.eswa.2016.03.044_bib0051) 2014; 24 Powell (10.1016/j.eswa.2016.03.044_bib0042) 1992 Tesch (10.1016/j.eswa.2016.03.044_bib0055) 2013 Gutmann (10.1016/j.eswa.2016.03.044_bib0023) 2001; 19 Coello (10.1016/j.eswa.2016.03.044_bib0008) 2004; 8 Husain (10.1016/j.eswa.2016.03.044_bib0024) 2011; 66 Forrester (10.1016/j.eswa.2016.03.044_bib0020) 2008 Saravanan (10.1016/j.eswa.2016.03.044_bib0052) 2009; 9 Isaacs (10.1016/j.eswa.2016.03.044_bib0025) 2009; 9 Custódio (10.1016/j.eswa.2016.03.044_bib0011) 2011; 21 Kanyakam (10.1016/j.eswa.2016.03.044_bib0027) 2012; 2012 Audet (10.1016/j.eswa.2016.03.044_bib0004) 2010; 204 Osyczka (10.1016/j.eswa.2016.03.044_bib0040) 1994; 8 Zhang (10.1016/j.eswa.2016.03.044_bib0058) 2007; 11 Singh (10.1016/j.eswa.2016.03.044_bib0054) 2014 Bhattacharya (10.1016/j.eswa.2016.03.044_bib0006) 2007 Datta (10.1016/j.eswa.2016.03.044_bib0012) 2011 Ray (10.1016/j.eswa.2016.03.044_bib0045) 2006; 38 10.1016/j.eswa.2016.03.044_bib0038 Cus (10.1016/j.eswa.2016.03.044_bib0010) 2003; 19 Couckuyt (10.1016/j.eswa.2016.03.044_bib0009) 2014; 60 Rao (10.1016/j.eswa.2016.03.044_bib0044) 2009 Bäck (10.1016/j.eswa.2016.03.044_bib0005) 1996 Regis (10.1016/j.eswa.2016.03.044_bib0050) 2013 Osyczka (10.1016/j.eswa.2016.03.044_bib0039) 2002 10.1016/j.eswa.2016.03.044_bib0034 Michalewicz (10.1016/j.eswa.2016.03.044_bib0037) 1996; 4 Fan (10.1016/j.eswa.2016.03.044_bib0018) 2015 Regis (10.1016/j.eswa.2016.03.044_bib0048) 2014; 18 Knowles (10.1016/j.eswa.2016.03.044_bib0030) 2000; 8 Emmerich (10.1016/j.eswa.2016.03.044_bib0017) 2006; 10 Deb (10.1016/j.eswa.2016.03.044_bib0014) 2000 Zitzler (10.1016/j.eswa.2016.03.044_bib0060) 1998 Jones (10.1016/j.eswa.2016.03.044_bib0026) 1998; 13 Andrés (10.1016/j.eswa.2016.03.044_bib0003) 2012; 39 Ponweiser (10.1016/j.eswa.2016.03.044_bib0041) 2008 Liu (10.1016/j.eswa.2016.03.044_bib0035) 2013; 40 Regis (10.1016/j.eswa.2016.03.044_bib0049) 2004; 8 Kunakote (10.1016/j.eswa.2016.03.044_bib0032) 2011; 43 Quiza Sardiñas (10.1016/j.eswa.2016.03.044_bib0043) 2006; 19 |
| References_xml | – volume: 8 start-page: 490 year: 2004 end-page: 505 ident: bib0049 article-title: Local function approximation in evolutionary algorithms for costly black box optimization publication-title: IEEE Transactions on Evolutionary Computation – volume: 8 start-page: 37 year: 1994 end-page: 41 ident: bib0040 article-title: Computer aided multicriterion optimization system for computationally expensive functions publication-title: Structural Optimization – start-page: 67 year: 2001 end-page: 81 ident: bib0016 article-title: Controlled elitist non-dominated sorting genetic algorithms for better convergence publication-title: Evolutionary multi-criterion optimization – volume: 19 start-page: 113 year: 2003 end-page: 121 ident: bib0010 article-title: Optimization of cutting process by GA approach publication-title: Robotics and Computer-Integrated Manufacturing – start-page: 51 year: 2013 end-page: 85 ident: bib0050 article-title: An initialization strategy for high-dimensional surrogate-based expensive black-box optimization publication-title: Selected contributions from the MOPTA 2012 conference series: Springer proceedings in mathematics & statistics, Volume 62 – volume: 64 start-page: 17 year: 2016 end-page: 32 ident: bib0002 article-title: Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection publication-title: Journal of Global Optimization – year: 2009 ident: bib0044 publication-title: Engineering optimization: Theory and practice – volume: 2012 year: 2012 ident: bib0027 article-title: Comparative performance of surrogate-assisted MOEAs for geometrical design of pin-fin heat sinks publication-title: Journal of Applied Mathematics – volume: 21 start-page: 1109 year: 2011 end-page: 1140 ident: bib0011 article-title: Direct multisearch for multiobjective optimization. publication-title: SIAM Journal on Optimization – volume: 9 start-page: 325 year: 2015 end-page: 336 ident: bib0022 article-title: Simulation-based optimization for a capacitated multi-echelon production-inventory system publication-title: Journal of Simulation – volume: 10 start-page: 50 year: 2006 end-page: 66 ident: bib0029 article-title: Parego: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems publication-title: IEEE Transactions on Evolutionary Computation – start-page: 784 year: 2008 end-page: 794 ident: bib0041 article-title: Multiobjective optimization on a limited budget of evaluations using model-assisted publication-title: Proceedings of parallel problem solving from nature – PPSN X, Dortmund, Germany – volume: 218 start-page: 10943 year: 2012 end-page: 10973 ident: bib0036 article-title: Empirical analysis of a modified artificial bee colony for constrained numerical optimization publication-title: Applied Mathematics and Computation – start-page: 718 year: 2010 end-page: 727 ident: bib0056 article-title: On expected-improvement criteria for model-based multi-objective optimization publication-title: Proceedings of parallel problem solving from nature – PPSN XI, Kraków, Poland – volume: 13 start-page: 455 year: 1998 end-page: 492 ident: bib0026 article-title: Efficient global optimization of expensive black-box functions publication-title: Journal of Global Optimization – volume: 19 start-page: 127 year: 2006 end-page: 133 ident: bib0043 article-title: Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes publication-title: Engineering Applications of Artificial Intelligence – start-page: 3847 year: 2007 end-page: 3854 ident: bib0006 article-title: Surrogate based ea for expensive optimization problems publication-title: Proceedings of the 2007 IEEE congress on evolutionary computation (CEC) – volume: 9 start-page: 159 year: 2009 end-page: 172 ident: bib0052 article-title: Evolutionary multi criteria design optimization of robot grippers. publication-title: Applied Soft Computing – volume: 4 start-page: 1 year: 1996 end-page: 32 ident: bib0037 article-title: Evolutionary algorithms for constrained parameter optimization problems publication-title: Evolutionary Computation – reference: Ortiz, G. A. (2012). Multi-objective optimization using evolution strategies (ES) as evolutionary algorithm (EA). – start-page: 1049 year: 2008 end-page: 1056 ident: bib0053 article-title: ASAGA: An adaptive surrogate-assisted genetic algorithm publication-title: Proceedings of the 10th annual conference on genetic and evolutionary computation – start-page: 245 year: 2008 end-page: 284 ident: bib0031 article-title: Meta-modeling in multiobjective optimization publication-title: Multiobjective optimization: Interactive and evolutionary approaches – volume: 3 start-page: 257 year: 1999 end-page: 271 ident: bib0061 article-title: Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach publication-title: IEEE Transactions on Evolutionary Computation – volume: 43 start-page: 541 year: 2011 end-page: 557 ident: bib0032 article-title: Multi-objective topology optimization using evolutionary algorithms publication-title: Engineering Optimization – volume: 18 start-page: 31 year: 2013 end-page: 44 ident: bib0046 article-title: A novel hybrid intelligent system for multi-objective machine parameter optimization publication-title: Pattern Analysis and Applications – year: 1996 ident: bib0005 publication-title: Evolutionary algorithms in theory and practice – volume: 8 start-page: 149 year: 2000 end-page: 172 ident: bib0030 article-title: Approximating the nondominated front using the pareto archived evolution strategy publication-title: Evolutionary computation – start-page: 1843 year: 2011 end-page: 1850 ident: bib0012 article-title: Multi-objective design and analysis of robot gripper configurations using an evolutionary-classical approach publication-title: Proceedings of the 13th annual conference on Genetic and evolutionary computation – volume: 6 start-page: 182 year: 2002 end-page: 197 ident: bib0015 article-title: A fast and elitist multi-objective genetic algorithm: NSGA-II publication-title: IEEE Transactions on Evolutionary Computation – volume: 60 start-page: 575 year: 2014 end-page: 594 ident: bib0009 article-title: Fast calculation of multiobjective probability of improvement and expected improvement criteria for pareto optimization publication-title: Journal of Global Optimization – start-page: 3080 year: 2014 end-page: 3087 ident: bib0054 article-title: A constrained multi-objective surrogate-based optimization algorithm publication-title: Proceedings of 2014 IEEE congress on evolutionary computation (CEC) – year: 2002 ident: bib0039 publication-title: Evolutionary algorithms for single and multicriteria design optimization – volume: 11 start-page: 712 year: 2007 end-page: 731 ident: bib0058 article-title: MOEA/D: A multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Transactions on Evolutionary Computation – start-page: 849 year: 2000 end-page: 858 ident: bib0014 article-title: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II publication-title: Proceedings of the parallel problem solving from nature VI (PPSN-VI) – reference: . Accessed 05.03.14. – year: 2008 ident: bib0020 publication-title: Engineering design via surrogate modelling: A practical guide – reference: Lin, S. (2011). NPGM – A NSGA program in matlab, version 1.4. – volume: 39 start-page: 10700 year: 2012 end-page: 10708 ident: bib0003 article-title: Efficient aerodynamic design through evolutionary programming and support vector regression algorithms publication-title: Expert Systems with Applications – volume: 151 start-page: 206 year: 2014 end-page: 213 ident: bib0021 article-title: Simulation-based optimization methods for setting production planning parameters publication-title: International Journal of Production Economics – volume: 38 start-page: 837 year: 2011 end-page: 853 ident: bib0047 article-title: Stochastic radial basis function algorithms for large-scale optimization involving expensive black-box objective and constraint functions publication-title: Computers and Operations Research – volume: 42 start-page: 305 year: 2010 end-page: 323 ident: bib0007 article-title: Optimum plate-fin heat sinks by using a multi-objective evolutionary algorithm publication-title: Engineering Optimization – volume: 8 start-page: 256 year: 2004 end-page: 279 ident: bib0008 article-title: Handling multiple objectives with particle swarm optimization publication-title: IEEE Transactions on Evolutionary Computation – volume: 38 start-page: 739 year: 2006 end-page: 753 ident: bib0028 article-title: Polyoptimal design of sandwich cylindrical panels with the application of an expert system publication-title: Engineering Optimization – volume: 19 start-page: 201 year: 2001 end-page: 227 ident: bib0023 article-title: A radial basis function method for global optimization publication-title: Journal of Global Optimization – reference: . Accessed 07.02.15. – volume: 66 start-page: 742 year: 2011 end-page: 759 ident: bib0024 article-title: Enhanced multi-objective optimization of a dimpled channel through evolutionary algorithms and multiple surrogate methods publication-title: International Journal for Numerical Methods in Fluids – volume: 40 start-page: 4496 year: 2013 end-page: 4502 ident: bib0035 article-title: Parameter estimation of a pressure swing adsorption model for air separation using multi-objective optimisation and support vector regression model publication-title: Expert Systems with Applications – volume: 18 start-page: 326 year: 2014 end-page: 347 ident: bib0048 article-title: Evolutionary programming for high-dimensional constrained expensive black-box optimization using radial basis functions publication-title: IEEE Transactions on Evolutionary Computation – volume: 24 start-page: 334 year: 2014 end-page: 362 ident: bib0051 article-title: A derivative-free trust-region method for biobjective optimization publication-title: SIAM Journal on Optimization – reference: Fan, Z., Li, W., Cai, X., Lin, H., Xie, S., & Goodman, E. (2015b). A new repair operator for multi-objective evolutionary algorithm in constrained optimization problems . CoRR, abs/1504.00154, 2015. – start-page: 76 year: 2015 end-page: 83 ident: bib0018 article-title: Difficulty controllable and scalable constrained multi-objective test problems publication-title: Proceedings of 2015 international conference on industrial informatics-computing technology, intelligent technology, industrial information integration (ICIICII) – start-page: 973 year: 2013 end-page: 980 ident: bib0055 article-title: Expensive multiobjective optimization for robotics publication-title: Proceedings of the 2013 IEEE international conference on robotics and automation (ICRA) – volume: 30 start-page: 3197 year: 2008 end-page: 3219 ident: bib0057 article-title: ORBIT: Optimization by radial basis function interpolation in trust-regions publication-title: SIAM Journal on Scientific Computing – volume: 10 start-page: 421 year: 2006 end-page: 439 ident: bib0017 article-title: Single- and multiobjective evolutionary optimization assisted by gaussian random field metamodels publication-title: IEEE Transactions on Evolutionary Computation – year: 2001 ident: bib0013 publication-title: Multi-objective optimization using evolutionary algorithms – volume: 14 start-page: 456 year: 2010 end-page: 474 ident: bib0059 article-title: Expensive multiobjective optimization by MOEA/D with gaussian process model publication-title: IEEE Transactions on Evolutionary Computation – volume: 38 start-page: 997 year: 2006 end-page: 1011 ident: bib0045 article-title: A surrogate assisted parallel multiobjective evolutionary algorithm for robust engineering design publication-title: Engineering Optimization – volume: 9 start-page: 188 year: 2009 end-page: 217 ident: bib0025 article-title: Multi-objective design optimisation using multiple adaptive spatially distributed surrogates publication-title: International Journal of Product Development – year: 1998 ident: bib0060 article-title: An evolutionary algorithm for multiobjective optimization: The strength pareto approach publication-title: Technical report 43 – volume: 42 start-page: 2703 year: 2015 end-page: 2709 ident: bib0001 article-title: Effect of error metrics on optimum weight factor selection for ensemble of metamodels publication-title: Expert Systems with Applications – volume: 11 start-page: 79 year: 1996 end-page: 87 ident: bib0033 article-title: A knowledge-based expert system as a pre-post processor in engineering optimization publication-title: Expert Systems with Applications – start-page: 105 year: 1992 end-page: 210 ident: bib0042 article-title: The theory of radial basis function approximation in 1990 publication-title: Advances in numerical analysis, Volume 2: Wavelets, subdivision algorithms and radial basis functions – volume: 204 start-page: 545 year: 2010 end-page: 556 ident: bib0004 article-title: A mesh adaptive direct search algorithm for multiobjective optimization publication-title: European Journal of Operational Research – volume: 3 start-page: 257 issue: 4 year: 1999 ident: 10.1016/j.eswa.2016.03.044_bib0061 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 – ident: 10.1016/j.eswa.2016.03.044_bib0019 – volume: 218 start-page: 10943 issue: 22 year: 2012 ident: 10.1016/j.eswa.2016.03.044_bib0036 article-title: Empirical analysis of a modified artificial bee colony for constrained numerical optimization publication-title: Applied Mathematics and Computation doi: 10.1016/j.amc.2012.04.057 – volume: 38 start-page: 739 issue: 6 year: 2006 ident: 10.1016/j.eswa.2016.03.044_bib0028 article-title: Polyoptimal design of sandwich cylindrical panels with the application of an expert system publication-title: Engineering Optimization doi: 10.1080/03052150600744555 – volume: 8 start-page: 256 issue: 3 year: 2004 ident: 10.1016/j.eswa.2016.03.044_bib0008 article-title: Handling multiple objectives with particle swarm optimization publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2004.826067 – volume: 9 start-page: 159 issue: 1 year: 2009 ident: 10.1016/j.eswa.2016.03.044_bib0052 article-title: Evolutionary multi criteria design optimization of robot grippers. publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2008.04.001 – start-page: 1049 year: 2008 ident: 10.1016/j.eswa.2016.03.044_bib0053 article-title: ASAGA: An adaptive surrogate-assisted genetic algorithm – start-page: 51 year: 2013 ident: 10.1016/j.eswa.2016.03.044_bib0050 article-title: An initialization strategy for high-dimensional surrogate-based expensive black-box optimization – volume: 4 start-page: 1 issue: 1 year: 1996 ident: 10.1016/j.eswa.2016.03.044_bib0037 article-title: Evolutionary algorithms for constrained parameter optimization problems publication-title: Evolutionary Computation doi: 10.1162/evco.1996.4.1.1 – start-page: 67 year: 2001 ident: 10.1016/j.eswa.2016.03.044_bib0016 article-title: Controlled elitist non-dominated sorting genetic algorithms for better convergence – ident: 10.1016/j.eswa.2016.03.044_bib0034 – volume: 13 start-page: 455 issue: 4 year: 1998 ident: 10.1016/j.eswa.2016.03.044_bib0026 article-title: Efficient global optimization of expensive black-box functions publication-title: Journal of Global Optimization doi: 10.1023/A:1008306431147 – year: 2002 ident: 10.1016/j.eswa.2016.03.044_bib0039 – year: 2009 ident: 10.1016/j.eswa.2016.03.044_bib0044 – volume: 19 start-page: 201 issue: 3 year: 2001 ident: 10.1016/j.eswa.2016.03.044_bib0023 article-title: A radial basis function method for global optimization publication-title: Journal of Global Optimization doi: 10.1023/A:1011255519438 – start-page: 849 year: 2000 ident: 10.1016/j.eswa.2016.03.044_bib0014 article-title: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II – ident: 10.1016/j.eswa.2016.03.044_bib0038 – volume: 42 start-page: 2703 issue: 5 year: 2015 ident: 10.1016/j.eswa.2016.03.044_bib0001 article-title: Effect of error metrics on optimum weight factor selection for ensemble of metamodels publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2014.11.020 – volume: 64 start-page: 17 issue: 1 year: 2016 ident: 10.1016/j.eswa.2016.03.044_bib0002 article-title: Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection publication-title: Journal of Global Optimization doi: 10.1007/s10898-015-0270-y – volume: 19 start-page: 113 issue: 1 year: 2003 ident: 10.1016/j.eswa.2016.03.044_bib0010 article-title: Optimization of cutting process by GA approach publication-title: Robotics and Computer-Integrated Manufacturing doi: 10.1016/S0736-5845(02)00068-6 – volume: 8 start-page: 37 year: 1994 ident: 10.1016/j.eswa.2016.03.044_bib0040 article-title: Computer aided multicriterion optimization system for computationally expensive functions publication-title: Structural Optimization doi: 10.1007/BF01742932 – start-page: 973 year: 2013 ident: 10.1016/j.eswa.2016.03.044_bib0055 article-title: Expensive multiobjective optimization for robotics – start-page: 3847 year: 2007 ident: 10.1016/j.eswa.2016.03.044_bib0006 article-title: Surrogate based ea for expensive optimization problems – volume: 42 start-page: 305 issue: 4 year: 2010 ident: 10.1016/j.eswa.2016.03.044_bib0007 article-title: Optimum plate-fin heat sinks by using a multi-objective evolutionary algorithm publication-title: Engineering Optimization doi: 10.1080/03052150903143935 – volume: 8 start-page: 490 issue: 5 year: 2004 ident: 10.1016/j.eswa.2016.03.044_bib0049 article-title: Local function approximation in evolutionary algorithms for costly black box optimization publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2004.835247 – volume: 10 start-page: 50 issue: 1 year: 2006 ident: 10.1016/j.eswa.2016.03.044_bib0029 article-title: Parego: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2005.851274 – volume: 38 start-page: 837 issue: 5 year: 2011 ident: 10.1016/j.eswa.2016.03.044_bib0047 article-title: Stochastic radial basis function algorithms for large-scale optimization involving expensive black-box objective and constraint functions publication-title: Computers and Operations Research doi: 10.1016/j.cor.2010.09.013 – volume: 43 start-page: 541 issue: 5 year: 2011 ident: 10.1016/j.eswa.2016.03.044_bib0032 article-title: Multi-objective topology optimization using evolutionary algorithms publication-title: Engineering Optimization doi: 10.1080/0305215X.2010.502935 – start-page: 3080 year: 2014 ident: 10.1016/j.eswa.2016.03.044_bib0054 article-title: A constrained multi-objective surrogate-based optimization algorithm – volume: 38 start-page: 997 issue: 8 year: 2006 ident: 10.1016/j.eswa.2016.03.044_bib0045 article-title: A surrogate assisted parallel multiobjective evolutionary algorithm for robust engineering design publication-title: Engineering Optimization doi: 10.1080/03052150600882538 – volume: 18 start-page: 31 issue: 1 year: 2013 ident: 10.1016/j.eswa.2016.03.044_bib0046 article-title: A novel hybrid intelligent system for multi-objective machine parameter optimization publication-title: Pattern Analysis and Applications doi: 10.1007/s10044-013-0345-7 – volume: 60 start-page: 575 issue: 3 year: 2014 ident: 10.1016/j.eswa.2016.03.044_bib0009 article-title: Fast calculation of multiobjective probability of improvement and expected improvement criteria for pareto optimization publication-title: Journal of Global Optimization doi: 10.1007/s10898-013-0118-2 – start-page: 1843 year: 2011 ident: 10.1016/j.eswa.2016.03.044_bib0012 article-title: Multi-objective design and analysis of robot gripper configurations using an evolutionary-classical approach – volume: 21 start-page: 1109 issue: 3 year: 2011 ident: 10.1016/j.eswa.2016.03.044_bib0011 article-title: Direct multisearch for multiobjective optimization. publication-title: SIAM Journal on Optimization doi: 10.1137/10079731X – volume: 40 start-page: 4496 issue: 11 year: 2013 ident: 10.1016/j.eswa.2016.03.044_bib0035 article-title: Parameter estimation of a pressure swing adsorption model for air separation using multi-objective optimisation and support vector regression model publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2013.01.054 – year: 1998 ident: 10.1016/j.eswa.2016.03.044_bib0060 article-title: An evolutionary algorithm for multiobjective optimization: The strength pareto approach – volume: 24 start-page: 334 issue: 1 year: 2014 ident: 10.1016/j.eswa.2016.03.044_bib0051 article-title: A derivative-free trust-region method for biobjective optimization publication-title: SIAM Journal on Optimization doi: 10.1137/120864738 – start-page: 76 year: 2015 ident: 10.1016/j.eswa.2016.03.044_bib0018 article-title: Difficulty controllable and scalable constrained multi-objective test problems – volume: 204 start-page: 545 issue: 3 year: 2010 ident: 10.1016/j.eswa.2016.03.044_bib0004 article-title: A mesh adaptive direct search algorithm for multiobjective optimization publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2009.11.010 – volume: 6 start-page: 182 issue: 2 year: 2002 ident: 10.1016/j.eswa.2016.03.044_bib0015 article-title: A fast and elitist multi-objective genetic algorithm: NSGA-II publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.996017 – year: 2001 ident: 10.1016/j.eswa.2016.03.044_bib0013 – volume: 18 start-page: 326 issue: 3 year: 2014 ident: 10.1016/j.eswa.2016.03.044_bib0048 article-title: Evolutionary programming for high-dimensional constrained expensive black-box optimization using radial basis functions publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2013.2262111 – year: 2008 ident: 10.1016/j.eswa.2016.03.044_bib0020 – volume: 10 start-page: 421 issue: 4 year: 2006 ident: 10.1016/j.eswa.2016.03.044_bib0017 article-title: Single- and multiobjective evolutionary optimization assisted by gaussian random field metamodels publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2005.859463 – start-page: 245 year: 2008 ident: 10.1016/j.eswa.2016.03.044_bib0031 article-title: Meta-modeling in multiobjective optimization – start-page: 784 year: 2008 ident: 10.1016/j.eswa.2016.03.044_bib0041 article-title: Multiobjective optimization on a limited budget of evaluations using model-assisted S-metric selection – volume: 19 start-page: 127 issue: 2 year: 2006 ident: 10.1016/j.eswa.2016.03.044_bib0043 article-title: Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2005.06.007 – volume: 11 start-page: 79 issue: 1 year: 1996 ident: 10.1016/j.eswa.2016.03.044_bib0033 article-title: A knowledge-based expert system as a pre-post processor in engineering optimization publication-title: Expert Systems with Applications doi: 10.1016/0957-4174(96)00008-5 – volume: 30 start-page: 3197 issue: 6 year: 2008 ident: 10.1016/j.eswa.2016.03.044_bib0057 article-title: ORBIT: Optimization by radial basis function interpolation in trust-regions publication-title: SIAM Journal on Scientific Computing doi: 10.1137/070691814 – volume: 39 start-page: 10700 issue: 12 year: 2012 ident: 10.1016/j.eswa.2016.03.044_bib0003 article-title: Efficient aerodynamic design through evolutionary programming and support vector regression algorithms publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2012.02.197 – volume: 8 start-page: 149 issue: 2 year: 2000 ident: 10.1016/j.eswa.2016.03.044_bib0030 article-title: Approximating the nondominated front using the pareto archived evolution strategy publication-title: Evolutionary computation doi: 10.1162/106365600568167 – volume: 151 start-page: 206 year: 2014 ident: 10.1016/j.eswa.2016.03.044_bib0021 article-title: Simulation-based optimization methods for setting production planning parameters publication-title: International Journal of Production Economics doi: 10.1016/j.ijpe.2013.10.016 – volume: 2012 year: 2012 ident: 10.1016/j.eswa.2016.03.044_bib0027 article-title: Comparative performance of surrogate-assisted MOEAs for geometrical design of pin-fin heat sinks publication-title: Journal of Applied Mathematics doi: 10.1155/2012/534783 – volume: 9 start-page: 325 issue: 4 year: 2015 ident: 10.1016/j.eswa.2016.03.044_bib0022 article-title: Simulation-based optimization for a capacitated multi-echelon production-inventory system publication-title: Journal of Simulation doi: 10.1057/jos.2015.5 – volume: 9 start-page: 188 issue: 1–3 year: 2009 ident: 10.1016/j.eswa.2016.03.044_bib0025 article-title: Multi-objective design optimisation using multiple adaptive spatially distributed surrogates publication-title: International Journal of Product Development doi: 10.1504/IJPD.2009.026179 – volume: 11 start-page: 712 issue: 6 year: 2007 ident: 10.1016/j.eswa.2016.03.044_bib0058 article-title: MOEA/D: A multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2007.892759 – volume: 14 start-page: 456 issue: 3 year: 2010 ident: 10.1016/j.eswa.2016.03.044_bib0059 article-title: Expensive multiobjective optimization by MOEA/D with gaussian process model publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2009.2033671 – start-page: 105 year: 1992 ident: 10.1016/j.eswa.2016.03.044_bib0042 article-title: The theory of radial basis function approximation in 1990 – year: 1996 ident: 10.1016/j.eswa.2016.03.044_bib0005 – volume: 66 start-page: 742 issue: 6 year: 2011 ident: 10.1016/j.eswa.2016.03.044_bib0024 article-title: Enhanced multi-objective optimization of a dimpled channel through evolutionary algorithms and multiple surrogate methods publication-title: International Journal for Numerical Methods in Fluids doi: 10.1002/fld.2282 – start-page: 718 year: 2010 ident: 10.1016/j.eswa.2016.03.044_bib0056 article-title: On expected-improvement criteria for model-based multi-objective optimization |
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| Snippet | •New surrogate-assisted ES for constrained multi-objective optimization is developed.•Surrogates are used to identify the most promising among many trial... In many real-world optimization problems, several conflicting objectives must be achieved and optimized simultaneously and the solutions are often required to... |
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| SubjectTerms | Algorithms Approximation Computation Computer simulation Constrained optimization Constraints Evolution strategy Mathematical analysis Mathematical models Metamodel Multi-objective optimization Optimization Radial basis function Surrogate |
| Title | A surrogate-assisted evolution strategy for constrained multi-objective optimization |
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