A comparative study on multiobjective metaheuristics for solving constrained in-core fuel management optimisation problems

In this paper, the topic of constrained multiobjective in-core fuel management optimisation (MICFMO) using metaheuristics is considered. Several modern and state-of-the-art metaheuristics from different classes, including evolutionary algorithms, local search algorithms, swarm intelligence algorithm...

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Vydáno v:	Computers & operations research Ročník 75; s. 174 - 190
Hlavní autoři:	Schlünz, E.B., Bokov, P.M., van Vuuren, J.H.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Elsevier Ltd 01.11.2016 Pergamon Press Inc
Témata:	Algorithms Comparative studies Constraint handling Constraints Genetic algorithms Handling Heuristic Heuristic methods In-core fuel management optimisation Mathematical models Metaheuristics Multiobjective optimisation Nuclear reactor Nuclear reactors Optimization Penalty function Problem solving Search algorithms Statistical analysis Constraint handling In-core fuel management optimisation Metaheuristics Multiobjective optimisation Nuclear reactor
ISSN:	0305-0548, 1873-765X, 0305-0548
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Abstract	In this paper, the topic of constrained multiobjective in-core fuel management optimisation (MICFMO) using metaheuristics is considered. Several modern and state-of-the-art metaheuristics from different classes, including evolutionary algorithms, local search algorithms, swarm intelligence algorithms, a probabilistic model-based algorithm and a harmony search algorithm, are compared in order to determine which approach is the most suitable in the context of constrained MICFMO. A test suite of 16 optimisation problem instances, based on the SAFARI-1 nuclear research reactor, has been established for the comparative study. The suite is partitioned into three classes, each consisting of problem instances having a different number of objectives, but subject to the same stringent constraint set. The effectiveness of a multiplicative penalty function constraint handling technique is also compared with the constrained-domination technique from the literature. The different optimisation approaches are compared in a nonparametric statistical analysis. The analysis reveals that multiplicative penalty function constraint handling is a competitive alternative to constrained-domination, and seems to be particularly effective in the context of bi-objective optimisation problems. In terms of the metaheuristic solution comparison, it is found that the nondominated sorting genetic algorithm II (NSGA-II), the Pareto ant colony optimisation (P-ACO) algorithm and the multiobjective optimisation using cross-entropy method (MOOCEM) are generally the best-performing metaheuristics across all three problem classes, along with the multiobjective variable neighbourhood search (MOVNS) in the bi-objective problem class. Furthermore, the practical relevance of the metaheuristic results is demonstrated by comparing the solutions thus obtained to the current SAFARI-1 reload configuration design approach. •Considers the constrained multiobjective ICFMO problem for nuclear research reactor.•Comparative study using eight metaheuristics and two constraint handling techniques.•Nonparametric statistical analyses on results across several problem instances.•New constraint handling technique found to be competitive to the existing technique.•NSGA-II, P-ACO and MOOCEM generally found to be the best-performing metaheuristics.
AbstractList	In this paper, the topic of constrained multiobjective in-core fuel management optimisation (MICFMO) using metaheuristics is considered. Several modern and state-of-the-art metaheuristics from different classes, including evolutionary algorithms, local search algorithms, swarm intelligence algorithms, a probabilistic model-based algorithm and a harmony search algorithm, are compared in order to determine which approach is the most suitable in the context of constrained MICFMO. A test suite of 16 optimisation problem instances, based on the SAFARI-1 nuclear research reactor, has been established for the comparative study. The suite is partitioned into three classes, each consisting of problem instances having a different number of objectives, but subject to the same stringent constraint set. The effectiveness of a multiplicative penalty function constraint handling technique is also compared with the constrained-domination technique from the literature. The different optimisation approaches are compared in a nonparametric statistical analysis. The analysis reveals that multiplicative penalty function constraint handling is a competitive alternative to constrained-domination, and seems to be particularly effective in the context of bi-objective optimisation problems. In terms of the metaheuristic solution comparison, it is found that the nondominated sorting genetic algorithm II (NSGA-II), the Pareto ant colony optimisation (P-ACO) algorithm and the multiobjective optimisation using cross-entropy method (MOOCEM) are generally the best-performing metaheuristics across all three problem classes, along with the multiobjective variable neighbourhood search (MOVNS) in the bi-objective problem class. Furthermore, the practical relevance of the metaheuristic results is demonstrated by comparing the solutions thus obtained to the current SAFARI-1 reload configuration design approach. In this paper, the topic of constrained multiobjective in-core fuel management optimisation (MICFMO) using metaheuristics is considered. Several modern and state-of-the-art metaheuristics from different classes, including evolutionary algorithms, local search algorithms, swarm intelligence algorithms, a probabilistic model-based algorithm and a harmony search algorithm, are compared in order to determine which approach is the most suitable in the context of constrained MICFMO. A test suite of 16 optimisation problem instances, based on the SAFARI-1 nuclear research reactor, has been established for the comparative study. The suite is partitioned into three classes, each consisting of problem instances having a different number of objectives, but subject to the same stringent constraint set. The effectiveness of a multiplicative penalty function constraint handling technique is also compared with the constrained-domination technique from the literature. The different optimisation approaches are compared in a nonparametric statistical analysis. The analysis reveals that multiplicative penalty function constraint handling is a competitive alternative to constrained-domination, and seems to be particularly effective in the context of bi-objective optimisation problems. In terms of the metaheuristic solution comparison, it is found that the nondominated sorting genetic algorithm II (NSGA-II), the Pareto ant colony optimisation (P-ACO) algorithm and the multiobjective optimisation using cross-entropy method (MOOCEM) are generally the best-performing metaheuristics across all three problem classes, along with the multiobjective variable neighbourhood search (MOVNS) in the bi-objective problem class. Furthermore, the practical relevance of the metaheuristic results is demonstrated by comparing the solutions thus obtained to the current SAFARI-1 reload configuration design approach. •Considers the constrained multiobjective ICFMO problem for nuclear research reactor.•Comparative study using eight metaheuristics and two constraint handling techniques.•Nonparametric statistical analyses on results across several problem instances.•New constraint handling technique found to be competitive to the existing technique.•NSGA-II, P-ACO and MOOCEM generally found to be the best-performing metaheuristics.
Author	Schlünz, E.B. van Vuuren, J.H. Bokov, P.M.
Author_xml	– sequence: 1 givenname: E.B. surname: Schlünz fullname: Schlünz, E.B. email: bernard.schlunz@necsa.co.za organization: Radiation and Reactor Theory, The South African Nuclear Energy Corporation SOC Ltd, PO Box 582, Pretoria 0001, South Africa – sequence: 2 givenname: P.M. surname: Bokov fullname: Bokov, P.M. email: pavel.bokov@necsa.co.za organization: Radiation and Reactor Theory, The South African Nuclear Energy Corporation SOC Ltd, PO Box 582, Pretoria 0001, South Africa – sequence: 3 givenname: J.H. surname: van Vuuren fullname: van Vuuren, J.H. email: vuuren@sun.ac.za organization: Stellenbosch Unit for Operations Research in Engineering, Department of Industrial Engineering, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa
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References	Lin, Lin (bib45) 2012; 43 Larrañaga, Kuijpers, Murga, Inza, Dizdarevic (bib37) 1999; 13 Bandyopadhyay, Saha, Maulik, Deb (bib10) 2008; 12 Mirvakili, Faghihi, Khalafi (bib5) 2012; 50 Bekker, Aldrich (bib12) 2011; 211 Stander G, Prinsloo RH, Müller E, Tomašević DI. OSCAR-4 code system application to the SAFARI-1 reactor. In: Proceedings of the international conference on reactor physics (PHYSOR 2008). Interlaken, Switzerland; September 14–19, 2008. Poursalehi, Zolfaghari, Minuchehr (bib21) 2013; 57 Engrand P. A multi-objective optimization approach based on simulated annealing and its application to nuclear fuel management. Technical report 98NB00037. Electricité de France; 1998. Suppapitnarm, Seffen, Parks, Clarkson (bib25) 2000; 33 Reyes Sierra M, Coello Coello CA. Improved PSO-based multi-objective optimization using crowding distance, mutation and Mazrou, Hamadouche (bib4) 2004; 44 Zitzler E, Laumanns M, Thiele L. SPEA2: improving the strength Pareto evolutionary algorithm. Technical report 103. Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH). Zurich; 2001. Derrac, García, Molina, Herrera (bib49) 2011; 1 Geiger MJ. Randomised variable neighbourhood search for multi objective optimisation. In: Proceedings of the EU/ME workshop: design and evaluation of advanced hybrid meta-heuristics. Nottingham, UK; November 4–5, 2004. p. 34–42. . Hu X, Eberhart RC, Shi Y. Swarm intelligence for permutation optimization: a case study of Doerner, Gutjahr, Hartl, Strauss, Stummer (bib9) 2004; 131 The MathWorks Inc. Neural network toolbox release 2014a. Natick, MA. URL Hochberg, Tamhane (bib18) 1987 Hedayat, Davilu, Barfrosh, Sepanloo (bib29) 2009; 239 2014. Foresee FD, Hagan MT. Gauss–Newton approximation to Bayesian learning. In: Proceedings of the international conference on neural networks. Houston, TX: IEEE; 1997. p. 1930–5. queens problem. In: Proceedings of the IEEE swarm intelligence symposium (SIS'03). Indianapolis, IN; April 24–26, 2003. p. 243–6. dominance. In: Proceedings of the international conference on evolutionary multi-criterion optimization (EMO 2005). Guanajuato, Mexico; March 9–11, 2005. p. 505–19. Gong, Wang, Yao (bib19) 2011; 38 Bean (bib40) 1994; 6 Hansen MP, Jaszkiewicz A. Evaluating the quality of approximations to the non-dominated set. Technical report IMM-REP-1998-7. Institute of Mathematical Modelling, Technical University of Denmark; 1998. Liang, Chuang (bib43) 2013; 29 Liu, Cai (bib20) 2012; 50 Keller PM. FORMOSA-P constrained multiobjective simulated methodology. In: Proceedings of the international meeting on mathematical methods for nuclear applications (M& C 2001). Salt Lake City, UT, USA; September 2001. Sivasubramani, Swarup (bib13) 2011; 33 The MathWorks Inc, MATLAB Release 2014a. Natick, MA. URL Barr, Golden, Kelly, Resende, Stewart (bib15) 1995; 1 Li, Du (bib36) 2013; 40 Do, Nguyen (bib27) 2007; 187 Turinsky PJ. Core isotopic depletion and fuel management. In: Cacuci DG, editor. Handbook of nuclear engineering. New York, NY: Springer; 2010. p. 1241–312. Schlünz EB, Bokov, PM, van Vuuren JH. Application of artificial neural networks for predicting core parameters for the SAFARI-1 nuclear research reactor. In: Proceedings of the annual conference of the Operations Research Society of South Africa (ORSSA 2015). Hartbeespoort, South Africa; September 13–16, 2015. p. 12–22. Turinsky (bib2) 2005; 151 Parks (bib23) 1996; 124 Schlünz, Bokov, Prinsloo, van Vuuren (bib30) 2016; 87 Jain, Dubes (bib42) 1988 Deb, Pratap, Agarwal, Meyarivan (bib6) 2002; 6 Miettinen (bib22) 1999 Meneses, Machado, Schirru (bib44) 2009; 51 Branke J, Deb K, Miettinen K, Slowinski R, editors. Multiobjective optimization: interactive and evolutionary approaches. Berlin: Springer; 2008. Durillo JJ, García-Nieto J, Nebro AJ, Coello Coello CA, Luna F, Alba E. Multi-objective particle swarm optimizers: an experimental comparison. In: Proceedings of the international conference on evolutionary multi-criterion optimization (EMO 2009). Nantes, France; April 7–10, 2009. p. 495–509. Coello Coello, Lamont, van Velduizen (bib46) 2007 Zitzler, Thiele (bib47) 1999; 3 Schlünz EB, Bokov PM, van Vuuren JH. Research reactor in-core fuel management optimisation using the multiobjective cross-entropy method. In: Proceedings of the international conference on reactor physics (PHYSOR 2014). Kyoto, Japan; September 28–October 3, 2014. Park, Joo, Kim, Lee (bib28) 2009; 162 Hollander M, Wolfe DA, Chicken E. Nonparametric statistical methods, 3rd ed. Hoboken, NJ: John Wiley & Sons; 2014. Soni, Kumar (bib38) 2014; 5 Meneses AAM, de Lima AMM, Schirru R. Artificial intelligence methods applied to the in-core fuel management optimization. In: Tsvetkov P, editor. Nuclear power. Sciyo; 2010. p. 63–77. Available online. URL Mazrou (10.1016/j.cor.2016.06.001_bib4) 2004; 44 Bandyopadhyay (10.1016/j.cor.2016.06.001_bib10) 2008; 12 Miettinen (10.1016/j.cor.2016.06.001_bib22) 1999 Meneses (10.1016/j.cor.2016.06.001_bib44) 2009; 51 10.1016/j.cor.2016.06.001_bib41 10.1016/j.cor.2016.06.001_bib48 Barr (10.1016/j.cor.2016.06.001_bib15) 1995; 1 Soni (10.1016/j.cor.2016.06.001_bib38) 2014; 5 Poursalehi (10.1016/j.cor.2016.06.001_bib21) 2013; 57 Coello Coello (10.1016/j.cor.2016.06.001_bib46) 2007 Gong (10.1016/j.cor.2016.06.001_bib19) 2011; 38 Parks (10.1016/j.cor.2016.06.001_bib23) 1996; 124 10.1016/j.cor.2016.06.001_bib32 10.1016/j.cor.2016.06.001_bib31 Turinsky (10.1016/j.cor.2016.06.001_bib2) 2005; 151 Derrac (10.1016/j.cor.2016.06.001_bib49) 2011; 1 10.1016/j.cor.2016.06.001_bib39 Suppapitnarm (10.1016/j.cor.2016.06.001_bib25) 2000; 33 10.1016/j.cor.2016.06.001_bib34 10.1016/j.cor.2016.06.001_bib33 Bean (10.1016/j.cor.2016.06.001_bib40) 1994; 6 Park (10.1016/j.cor.2016.06.001_bib28) 2009; 162 10.1016/j.cor.2016.06.001_bib35 10.1016/j.cor.2016.06.001_bib8 Mirvakili (10.1016/j.cor.2016.06.001_bib5) 2012; 50 Sivasubramani (10.1016/j.cor.2016.06.001_bib13) 2011; 33 Zitzler (10.1016/j.cor.2016.06.001_bib47) 1999; 3 Jain (10.1016/j.cor.2016.06.001_bib42) 1988 Liu (10.1016/j.cor.2016.06.001_bib20) 2012; 50 Do (10.1016/j.cor.2016.06.001_bib27) 2007; 187 10.1016/j.cor.2016.06.001_bib1 Deb (10.1016/j.cor.2016.06.001_bib6) 2002; 6 Hochberg (10.1016/j.cor.2016.06.001_bib18) 1987 10.1016/j.cor.2016.06.001_bib26 10.1016/j.cor.2016.06.001_bib3 Lin (10.1016/j.cor.2016.06.001_bib45) 2012; 43 10.1016/j.cor.2016.06.001_bib7 10.1016/j.cor.2016.06.001_bib24 Bekker (10.1016/j.cor.2016.06.001_bib12) 2011; 211 Larrañaga (10.1016/j.cor.2016.06.001_bib37) 1999; 13 Doerner (10.1016/j.cor.2016.06.001_bib9) 2004; 131 Hedayat (10.1016/j.cor.2016.06.001_bib29) 2009; 239 Liang (10.1016/j.cor.2016.06.001_bib43) 2013; 29 10.1016/j.cor.2016.06.001_bib16 Schlünz (10.1016/j.cor.2016.06.001_bib30) 2016; 87 10.1016/j.cor.2016.06.001_bib17 10.1016/j.cor.2016.06.001_bib11 Li (10.1016/j.cor.2016.06.001_bib36) 2013; 40 10.1016/j.cor.2016.06.001_bib14
References_xml	– volume: 50 start-page: 117 year: 2012 end-page: 125 ident: bib20 article-title: Studies of fuel loading pattern optimization for a typical pressurized water reactor (PWR) using improved pivot particle swarm method publication-title: Ann Nucl Energy – volume: 151 start-page: 3 year: 2005 end-page: 8 ident: bib2 article-title: Nuclear fuel management optimization publication-title: Nucl Technol – year: 1988 ident: bib42 article-title: Algorithms for clustering data – reference: Branke J, Deb K, Miettinen K, Slowinski R, editors. Multiobjective optimization: interactive and evolutionary approaches. Berlin: Springer; 2008. – volume: 3 start-page: 257 year: 1999 end-page: 271 ident: bib47 article-title: Multiobjective evolutionary algorithms publication-title: IEEE Trans Evol Comput – volume: 87 start-page: 659 year: 2016 end-page: 670 ident: bib30 article-title: A unified methodology for single- and multiobjective in-core fuel management optimisation based on augmented Chebyshev scalarisation and a harmony search algorithm publication-title: Ann Nucl Energy – volume: 12 start-page: 269 year: 2008 end-page: 283 ident: bib10 article-title: A simulated annealing-based multiobjective optimization algorithm publication-title: IEEE Trans Evol Comput – reference: -dominance. In: Proceedings of the international conference on evolutionary multi-criterion optimization (EMO 2005). Guanajuato, Mexico; March 9–11, 2005. p. 505–19. – reference: The MathWorks Inc, MATLAB Release 2014a. Natick, MA. URL 〈 – volume: 43 start-page: 91 year: 2012 end-page: 98 ident: bib45 article-title: Automatic pressurized water reactor loading pattern design using ant colony algorithms publication-title: Ann Nucl Energy – volume: 1 start-page: 3 year: 2011 end-page: 18 ident: bib49 article-title: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms publication-title: Swarm Evol Comput – reference: Hollander M, Wolfe DA, Chicken E. Nonparametric statistical methods, 3rd ed. Hoboken, NJ: John Wiley & Sons; 2014. – volume: 131 start-page: 79 year: 2004 end-page: 99 ident: bib9 article-title: Pareto ant colony optimization publication-title: Ann Oper Res – volume: 13 start-page: 129 year: 1999 end-page: 170 ident: bib37 article-title: Genetic algorithms for the travelling salesman problem publication-title: Artif Intell Rev – reference: 〉. – reference: Hansen MP, Jaszkiewicz A. Evaluating the quality of approximations to the non-dominated set. Technical report IMM-REP-1998-7. Institute of Mathematical Modelling, Technical University of Denmark; 1998. – volume: 239 start-page: 2786 year: 2009 end-page: 2799 ident: bib29 article-title: Optimization of the core configuration design using a hybrid artificial intelligence algorithm for research reactors publication-title: Nucl Eng Des – volume: 51 start-page: 319 year: 2009 end-page: 326 ident: bib44 article-title: Particle swarm optimization applied to the nuclear reload problem of a pressurized water reactor publication-title: Progr Nucl Energy – reference: Reyes Sierra M, Coello Coello CA. Improved PSO-based multi-objective optimization using crowding distance, mutation and – reference: Durillo JJ, García-Nieto J, Nebro AJ, Coello Coello CA, Luna F, Alba E. Multi-objective particle swarm optimizers: an experimental comparison. In: Proceedings of the international conference on evolutionary multi-criterion optimization (EMO 2009). Nantes, France; April 7–10, 2009. p. 495–509. – reference: The MathWorks Inc. Neural network toolbox release 2014a. Natick, MA. URL 〈 – year: 2007 ident: bib46 article-title: Evolutionary algorithms for solving multi-objective problems – volume: 33 start-page: 745 year: 2011 end-page: 752 ident: bib13 article-title: Multi-objective harmony search algorithm for optimal power flow problem publication-title: Electr Power Energy Syst – reference: Turinsky PJ. Core isotopic depletion and fuel management. In: Cacuci DG, editor. Handbook of nuclear engineering. New York, NY: Springer; 2010. p. 1241–312. – reference: Engrand P. A multi-objective optimization approach based on simulated annealing and its application to nuclear fuel management. Technical report 98NB00037. Electricité de France; 1998. – volume: 33 start-page: 59 year: 2000 end-page: 85 ident: bib25 article-title: A simulated annealing algorithm for multiobjective optimization publication-title: Eng Optim – volume: 29 start-page: 73 year: 2013 end-page: 78 ident: bib43 article-title: Variable neighborhood search for multi-objective resource allocation problems publication-title: Robot Comput-Integr Manuf – volume: 6 start-page: 182 year: 2002 end-page: 197 ident: bib6 article-title: A fast and elitist multiobjective genetic algorithm publication-title: IEEE Trans Evol Comput – reference: Schlünz EB, Bokov, PM, van Vuuren JH. Application of artificial neural networks for predicting core parameters for the SAFARI-1 nuclear research reactor. In: Proceedings of the annual conference of the Operations Research Society of South Africa (ORSSA 2015). Hartbeespoort, South Africa; September 13–16, 2015. p. 12–22. – reference: 〉; 2014. – volume: 50 start-page: 82 year: 2012 end-page: 93 ident: bib5 article-title: Developing a computational tool for predicting physical parameters of a typical VVER-1000 core based on artificial neural networks publication-title: Ann Nucl Energy – reference: -queens problem. In: Proceedings of the IEEE swarm intelligence symposium (SIS'03). Indianapolis, IN; April 24–26, 2003. p. 243–6. – volume: 211 start-page: 112 year: 2011 end-page: 121 ident: bib12 article-title: The cross-entropy method in multi-objective optimisation publication-title: Eur J Oper Res – reference: Foresee FD, Hagan MT. Gauss–Newton approximation to Bayesian learning. In: Proceedings of the international conference on neural networks. Houston, TX: IEEE; 1997. p. 1930–5. – reference: Geiger MJ. Randomised variable neighbourhood search for multi objective optimisation. In: Proceedings of the EU/ME workshop: design and evaluation of advanced hybrid meta-heuristics. Nottingham, UK; November 4–5, 2004. p. 34–42. – volume: 57 start-page: 151 year: 2013 end-page: 163 ident: bib21 article-title: Multi-objective loading pattern enhancement of PWR based on the discrete firefly algorithm publication-title: Ann Nucl Energy – volume: 5 start-page: 4519 year: 2014 end-page: 4521 ident: bib38 article-title: Study of various mutation operators in genetic algorithms publication-title: Int J Comput Sci Inf Technol – year: 1987 ident: bib18 article-title: Multiple comparison procedures – reference: Zitzler E, Laumanns M, Thiele L. SPEA2: improving the strength Pareto evolutionary algorithm. Technical report 103. Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH). Zurich; 2001. – reference: Stander G, Prinsloo RH, Müller E, Tomašević DI. OSCAR-4 code system application to the SAFARI-1 reactor. In: Proceedings of the international conference on reactor physics (PHYSOR 2008). Interlaken, Switzerland; September 14–19, 2008. – year: 1999 ident: bib22 article-title: Nonlinear multiobjective optimization – volume: 6 start-page: 154 year: 1994 end-page: 160 ident: bib40 article-title: Genetic algorithms and random keys for sequencing and optimization publication-title: ORSA J Comput – reference: Keller PM. FORMOSA-P constrained multiobjective simulated methodology. In: Proceedings of the international meeting on mathematical methods for nuclear applications (M& C 2001). Salt Lake City, UT, USA; September 2001. – volume: 40 start-page: 282 year: 2013 end-page: 302 ident: bib36 article-title: BSTBGA publication-title: Comput Oper Res – reference: Hu X, Eberhart RC, Shi Y. Swarm intelligence for permutation optimization: a case study of – volume: 162 start-page: 134 year: 2009 end-page: 147 ident: bib28 article-title: Multiobjective loading pattern optimization by simulated annealing employing discontinuous penalty function and screening technique publication-title: Nucl Sci Eng – volume: 1 start-page: 3 year: 1995 end-page: 32 ident: bib15 article-title: Designing and reporting on computational experiments with heuristic methods publication-title: J Heuristics – reference: Meneses AAM, de Lima AMM, Schirru R. Artificial intelligence methods applied to the in-core fuel management optimization. In: Tsvetkov P, editor. Nuclear power. Sciyo; 2010. p. 63–77. Available online. URL 〈 – volume: 38 start-page: 2787 year: 2011 end-page: 2796 ident: bib19 article-title: An interval bound algorithm of optimizing reactor core loading pattern by using reactivity interval schema publication-title: Ann Nucl Energy – volume: 124 start-page: 178 year: 1996 end-page: 187 ident: bib23 article-title: Multiobjective pressurized water reactor reload core design by nondominated genetic algorithm search publication-title: Nucl Sci Eng – volume: 44 start-page: 263 year: 2004 end-page: 275 ident: bib4 article-title: Application of artificial neural network for safety core parameters prediction in LWRRs publication-title: Progr Nucl Energy – volume: 187 start-page: 977 year: 2007 end-page: 988 ident: bib27 article-title: Application of a genetic algorithm to the fuel reload optimization for a research reactor publication-title: Appl Math Comput – reference: Schlünz EB, Bokov PM, van Vuuren JH. Research reactor in-core fuel management optimisation using the multiobjective cross-entropy method. In: Proceedings of the international conference on reactor physics (PHYSOR 2014). Kyoto, Japan; September 28–October 3, 2014. – ident: 10.1016/j.cor.2016.06.001_bib3 doi: 10.1007/978-0-387-98149-9_10 – year: 2007 ident: 10.1016/j.cor.2016.06.001_bib46 – ident: 10.1016/j.cor.2016.06.001_bib14 – volume: 124 start-page: 178 year: 1996 ident: 10.1016/j.cor.2016.06.001_bib23 article-title: Multiobjective pressurized water reactor reload core design by nondominated genetic algorithm search publication-title: Nucl Sci Eng doi: 10.13182/NSE96-A24233 – volume: 6 start-page: 182 issue: 2 year: 2002 ident: 10.1016/j.cor.2016.06.001_bib6 article-title: A fast and elitist multiobjective genetic algorithm publication-title: IEEE Trans Evol Comput doi: 10.1109/4235.996017 – volume: 33 start-page: 59 year: 2000 ident: 10.1016/j.cor.2016.06.001_bib25 article-title: A simulated annealing algorithm for multiobjective optimization publication-title: Eng Optim doi: 10.1080/03052150008940911 – volume: 1 start-page: 3 year: 1995 ident: 10.1016/j.cor.2016.06.001_bib15 article-title: Designing and reporting on computational experiments with heuristic methods publication-title: J Heuristics doi: 10.1007/BF02430363 – volume: 50 start-page: 117 year: 2012 ident: 10.1016/j.cor.2016.06.001_bib20 article-title: Studies of fuel loading pattern optimization for a typical pressurized water reactor (PWR) using improved pivot particle swarm method publication-title: Ann Nucl Energy doi: 10.1016/j.anucene.2012.08.007 – ident: 10.1016/j.cor.2016.06.001_bib33 – volume: 131 start-page: 79 year: 2004 ident: 10.1016/j.cor.2016.06.001_bib9 article-title: Pareto ant colony optimization publication-title: Ann Oper Res doi: 10.1023/B:ANOR.0000039513.99038.c6 – volume: 12 start-page: 269 issue: 3 year: 2008 ident: 10.1016/j.cor.2016.06.001_bib10 article-title: A simulated annealing-based multiobjective optimization algorithm publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2007.900837 – volume: 87 start-page: 659 year: 2016 ident: 10.1016/j.cor.2016.06.001_bib30 article-title: A unified methodology for single- and multiobjective in-core fuel management optimisation based on augmented Chebyshev scalarisation and a harmony search algorithm publication-title: Ann Nucl Energy doi: 10.1016/j.anucene.2015.09.023 – volume: 211 start-page: 112 year: 2011 ident: 10.1016/j.cor.2016.06.001_bib12 article-title: The cross-entropy method in multi-objective optimisation publication-title: Eur J Oper Res doi: 10.1016/j.ejor.2010.10.028 – volume: 187 start-page: 977 year: 2007 ident: 10.1016/j.cor.2016.06.001_bib27 article-title: Application of a genetic algorithm to the fuel reload optimization for a research reactor publication-title: Appl Math Comput doi: 10.1016/j.amc.2006.09.024 – volume: 38 start-page: 2787 year: 2011 ident: 10.1016/j.cor.2016.06.001_bib19 article-title: An interval bound algorithm of optimizing reactor core loading pattern by using reactivity interval schema publication-title: Ann Nucl Energy doi: 10.1016/j.anucene.2011.08.029 – volume: 50 start-page: 82 year: 2012 ident: 10.1016/j.cor.2016.06.001_bib5 article-title: Developing a computational tool for predicting physical parameters of a typical VVER-1000 core based on artificial neural networks publication-title: Ann Nucl Energy doi: 10.1016/j.anucene.2012.04.022 – ident: 10.1016/j.cor.2016.06.001_bib24 – volume: 40 start-page: 282 year: 2013 ident: 10.1016/j.cor.2016.06.001_bib36 article-title: BSTBGA publication-title: Comput Oper Res doi: 10.1016/j.cor.2012.07.014 – volume: 57 start-page: 151 year: 2013 ident: 10.1016/j.cor.2016.06.001_bib21 article-title: Multi-objective loading pattern enhancement of PWR based on the discrete firefly algorithm publication-title: Ann Nucl Energy doi: 10.1016/j.anucene.2013.01.043 – volume: 3 start-page: 257 issue: 4 year: 1999 ident: 10.1016/j.cor.2016.06.001_bib47 article-title: Multiobjective evolutionary algorithms publication-title: IEEE Trans Evol Comput doi: 10.1109/4235.797969 – volume: 151 start-page: 3 year: 2005 ident: 10.1016/j.cor.2016.06.001_bib2 article-title: Nuclear fuel management optimization publication-title: Nucl Technol doi: 10.13182/NT05-A3626 – volume: 51 start-page: 319 year: 2009 ident: 10.1016/j.cor.2016.06.001_bib44 article-title: Particle swarm optimization applied to the nuclear reload problem of a pressurized water reactor publication-title: Progr Nucl Energy doi: 10.1016/j.pnucene.2008.07.002 – ident: 10.1016/j.cor.2016.06.001_bib17 doi: 10.1002/9781119196037 – ident: 10.1016/j.cor.2016.06.001_bib41 – volume: 29 start-page: 73 year: 2013 ident: 10.1016/j.cor.2016.06.001_bib43 article-title: Variable neighborhood search for multi-objective resource allocation problems publication-title: Robot Comput-Integr Manuf doi: 10.1016/j.rcim.2012.04.015 – ident: 10.1016/j.cor.2016.06.001_bib34 – volume: 239 start-page: 2786 year: 2009 ident: 10.1016/j.cor.2016.06.001_bib29 article-title: Optimization of the core configuration design using a hybrid artificial intelligence algorithm for research reactors publication-title: Nucl Eng Des doi: 10.1016/j.nucengdes.2009.08.027 – volume: 33 start-page: 745 year: 2011 ident: 10.1016/j.cor.2016.06.001_bib13 article-title: Multi-objective harmony search algorithm for optimal power flow problem publication-title: Electr Power Energy Syst doi: 10.1016/j.ijepes.2010.12.031 – year: 1999 ident: 10.1016/j.cor.2016.06.001_bib22 – ident: 10.1016/j.cor.2016.06.001_bib7 – ident: 10.1016/j.cor.2016.06.001_bib8 doi: 10.1007/978-3-540-31880-4_35 – volume: 162 start-page: 134 year: 2009 ident: 10.1016/j.cor.2016.06.001_bib28 article-title: Multiobjective loading pattern optimization by simulated annealing employing discontinuous penalty function and screening technique publication-title: Nucl Sci Eng doi: 10.13182/NSE162-134 – ident: 10.1016/j.cor.2016.06.001_bib48 – volume: 44 start-page: 263 issue: 2 year: 2004 ident: 10.1016/j.cor.2016.06.001_bib4 article-title: Application of artificial neural network for safety core parameters prediction in LWRRs publication-title: Progr Nucl Energy doi: 10.1016/S0149-1970(04)90014-5 – year: 1988 ident: 10.1016/j.cor.2016.06.001_bib42 – ident: 10.1016/j.cor.2016.06.001_bib31 – volume: 13 start-page: 129 year: 1999 ident: 10.1016/j.cor.2016.06.001_bib37 article-title: Genetic algorithms for the travelling salesman problem publication-title: Artif Intell Rev doi: 10.1023/A:1006529012972 – ident: 10.1016/j.cor.2016.06.001_bib35 – ident: 10.1016/j.cor.2016.06.001_bib39 – ident: 10.1016/j.cor.2016.06.001_bib26 – volume: 43 start-page: 91 year: 2012 ident: 10.1016/j.cor.2016.06.001_bib45 article-title: Automatic pressurized water reactor loading pattern design using ant colony algorithms publication-title: Ann Nucl Energy doi: 10.1016/j.anucene.2011.12.002 – volume: 6 start-page: 154 issue: 2 year: 1994 ident: 10.1016/j.cor.2016.06.001_bib40 article-title: Genetic algorithms and random keys for sequencing and optimization publication-title: ORSA J Comput doi: 10.1287/ijoc.6.2.154 – ident: 10.1016/j.cor.2016.06.001_bib16 doi: 10.1007/978-3-540-88908-3 – year: 1987 ident: 10.1016/j.cor.2016.06.001_bib18 – volume: 1 start-page: 3 year: 2011 ident: 10.1016/j.cor.2016.06.001_bib49 article-title: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms publication-title: Swarm Evol Comput doi: 10.1016/j.swevo.2011.02.002 – ident: 10.1016/j.cor.2016.06.001_bib1 – ident: 10.1016/j.cor.2016.06.001_bib11 – volume: 5 start-page: 4519 issue: 3 year: 2014 ident: 10.1016/j.cor.2016.06.001_bib38 article-title: Study of various mutation operators in genetic algorithms publication-title: Int J Comput Sci Inf Technol – ident: 10.1016/j.cor.2016.06.001_bib32
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Snippet	In this paper, the topic of constrained multiobjective in-core fuel management optimisation (MICFMO) using metaheuristics is considered. Several modern and...
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SubjectTerms	Algorithms Comparative studies Constraint handling Constraints Genetic algorithms Handling Heuristic Heuristic methods In-core fuel management optimisation Mathematical models Metaheuristics Multiobjective optimisation Nuclear reactor Nuclear reactors Optimization Penalty function Problem solving Search algorithms Statistical analysis
Title	A comparative study on multiobjective metaheuristics for solving constrained in-core fuel management optimisation problems
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