A Self-Organizing Multiobjective Evolutionary Algorithm

Under mild conditions, the Pareto front (Pareto set) of a continuous m-objective optimization problem forms an (m - 1)-dimensional piecewise continuous manifold. Based on this property, this paper proposes a self-organizing multiobjective evolutionary algorithm. At each generation, a self-organizing...

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Vydané v:	IEEE transactions on evolutionary computation Ročník 20; číslo 5; s. 792 - 806
Hlavní autori:	Zhang, Hu, Zhou, Aimin, Song, Shenmin, Zhang, Qingfu, Gao, Xiao-Zhi, Zhang, Jun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 01.10.2016
Predmet:	Algorithm design and analysis Clustering algorithm Electronic mail evolutionary algorithms Evolutionary computation multiobjective optimization self-organizing map (SOM) Shape Sociology Statistics Training
ISSN:	1089-778X, 1941-0026
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Abstract	Under mild conditions, the Pareto front (Pareto set) of a continuous m-objective optimization problem forms an (m - 1)-dimensional piecewise continuous manifold. Based on this property, this paper proposes a self-organizing multiobjective evolutionary algorithm. At each generation, a self-organizing mapping method with (m - 1) latent variables is applied to establish the neighborhood relationship among current solutions. A solution is only allowed to mate with its neighboring solutions to generate a new solution. To reduce the computational overhead, the self-organizing training step and the evolution step are conducted in an alternative manner. In other words, the self-organizing training is performed only one single step at each generation. The proposed algorithm has been applied to a number of test instances and compared with some state-of-the-art multiobjective evolutionary methods. The results have demonstrated its advantages over other approaches.
AbstractList	Under mild conditions, the Pareto front (Pareto set) of a continuous m-objective optimization problem forms an (m - 1)-dimensional piecewise continuous manifold. Based on this property, this paper proposes a self-organizing multiobjective evolutionary algorithm. At each generation, a self-organizing mapping method with (m - 1) latent variables is applied to establish the neighborhood relationship among current solutions. A solution is only allowed to mate with its neighboring solutions to generate a new solution. To reduce the computational overhead, the self-organizing training step and the evolution step are conducted in an alternative manner. In other words, the self-organizing training is performed only one single step at each generation. The proposed algorithm has been applied to a number of test instances and compared with some state-of-the-art multiobjective evolutionary methods. The results have demonstrated its advantages over other approaches.
Author	Jun Zhang Shenmin Song Xiao-Zhi Gao Aimin Zhou Hu Zhang Qingfu Zhang
Author_xml	– sequence: 1 givenname: Hu surname: Zhang fullname: Zhang, Hu – sequence: 2 givenname: Aimin orcidid: 0000-0002-4768-5946 surname: Zhou fullname: Zhou, Aimin – sequence: 3 givenname: Shenmin surname: Song fullname: Song, Shenmin – sequence: 4 givenname: Qingfu surname: Zhang fullname: Zhang, Qingfu – sequence: 5 givenname: Xiao-Zhi surname: Gao fullname: Gao, Xiao-Zhi – sequence: 6 givenname: Jun surname: Zhang fullname: Zhang, Jun
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Cites_doi	10.1109/TEVC.2007.892759 10.1162/EVCO_a_00109 10.1109/4235.797969 10.1109/TEVC.2014.2308305 10.1109/TEVC.2014.2312208 10.1162/neco.1997.9.7.1493 10.1080/03052150701821328 10.1016/j.asoc.2009.08.016 10.1109/TEVC.2014.2303783 10.1007/978-3-540-87700-4_66 10.1109/TEVC.2008.925798 10.1109/4235.996017 10.1109/TEVC.2015.2424251 10.1016/S0925-2312(98)00030-7 10.1007/s13042-014-0277-6 10.1007/978-0-387-84858-7 10.1007/978-3-0348-8280-4 10.1016/j.ejor.2006.08.008 10.1109/TEVC.2014.2315442 10.1109/TEVC.2009.2021467 10.1109/TEVC.2015.2457616 10.1109/TEVC.2013.2281524 10.1016/j.cie.2008.10.010 10.1007/3-540-45105-6_116 10.1109/TEVC.2009.2024143 10.1002/int.20358 10.1007/3-540-45712-7_12 10.1109/TEVC.2015.2433672 10.1162/EVCO_a_00009 10.1016/j.swevo.2011.03.001 10.1109/TEVC.2007.894202 10.1016/j.neucom.2014.03.070 10.1007/978-3-540-31880-4_20 10.1109/TEVC.2013.2293776
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References	ref35 ref13 ref34 ref12 ref37 ref15 ref36 ref14 ref31 ref30 ref11 ref10 miettinen (ref1) 1999 zhou (ref44) 2005; 3 zhan (ref29) 2012; 4 norouzi (ref32) 2011; 35 ref16 ref19 deb (ref2) 2001 ref46 ref24 ref45 ref23 ref26 bishop (ref38) 2006 ref25 ref20 li (ref42) 2014; 18 ref41 murata (ref33) 2000 ref22 ref21 jin (ref18) 2003 ref28 price (ref40) 2006 ehrgott (ref17) 2005 ref8 gu (ref43) 2012; 8 ref7 huband (ref47) 2005 ref9 ref4 ref3 ref6 büche (ref27) 2002 ref5 haykin (ref39) 2009; 3
References_xml	– year: 2006 ident: ref38 publication-title: Pattern Recognition and Machine Learning – ident: ref12 doi: 10.1109/TEVC.2007.892759 – volume: 4 start-page: 250 year: 2012 ident: ref29 article-title: Low earth orbit regional satellite constellation design via self organization feature maps publication-title: Int J Adv Comput Technol – ident: ref34 doi: 10.1162/EVCO_a_00109 – year: 1999 ident: ref1 publication-title: Nonlinear Multiobjective Optimization – ident: ref45 doi: 10.1109/4235.797969 – ident: ref7 doi: 10.1109/TEVC.2014.2308305 – ident: ref9 doi: 10.1109/TEVC.2014.2312208 – volume: 3 start-page: 2568 year: 2005 ident: ref44 article-title: A model-based evolutionary algorithm for bi-objective optimization publication-title: Proc IEEE Congr Evol Comput (CEC) – ident: ref35 doi: 10.1162/neco.1997.9.7.1493 – ident: ref21 doi: 10.1080/03052150701821328 – ident: ref31 doi: 10.1016/j.asoc.2009.08.016 – ident: ref6 doi: 10.1109/TEVC.2014.2303783 – ident: ref24 doi: 10.1007/978-3-540-87700-4_66 – ident: ref13 doi: 10.1109/TEVC.2008.925798 – year: 2006 ident: ref40 publication-title: Differential Evolution A Practical Approach to Global Optimization – ident: ref8 doi: 10.1109/4235.996017 – volume: 8 start-page: 3677 year: 2012 ident: ref43 article-title: A multiobjective evolutionary algorithm using dynamic weight design method publication-title: Int J Innov Comput Inf Control – ident: ref15 doi: 10.1109/TEVC.2015.2424251 – ident: ref26 doi: 10.1016/S0925-2312(98)00030-7 – volume: 3 year: 2009 ident: ref39 publication-title: Neural Networks and Learning Machines – volume: 35 start-page: 105 year: 2011 ident: ref32 article-title: A self organizing map based hybrid multi-objective optimization of water distribution networks publication-title: IJST Transactions of Civil Engineering – ident: ref25 doi: 10.1007/s13042-014-0277-6 – ident: ref37 doi: 10.1007/978-0-387-84858-7 – ident: ref16 doi: 10.1007/978-3-0348-8280-4 – start-page: 307 year: 2000 ident: ref33 article-title: Cellular genetic local search for multi-objective optimization publication-title: Proc Genet Evol Comput Conf (GECCO) – ident: ref10 doi: 10.1016/j.ejor.2006.08.008 – ident: ref4 doi: 10.1109/TEVC.2014.2315442 – ident: ref20 doi: 10.1109/TEVC.2009.2021467 – ident: ref14 doi: 10.1109/TEVC.2015.2457616 – ident: ref5 doi: 10.1109/TEVC.2013.2281524 – ident: ref30 doi: 10.1016/j.cie.2008.10.010 – start-page: 152 year: 2002 ident: ref27 article-title: Self-organizing maps for multi-objective optimization publication-title: Proc Genet Evol Comput Conf (GECCO) – ident: ref46 doi: 10.1007/3-540-45105-6_116 – ident: ref22 doi: 10.1109/TEVC.2009.2024143 – ident: ref23 doi: 10.1002/int.20358 – ident: ref28 doi: 10.1007/3-540-45712-7_12 – year: 2005 ident: ref17 publication-title: Multicriteria Optimization – year: 2001 ident: ref2 publication-title: Multi-Objective Optimization Using Evolutionary Algorithms – ident: ref41 doi: 10.1109/TEVC.2015.2433672 – start-page: 1910 year: 2003 ident: ref18 article-title: Connectedness, regularity and the success of local search in evolutionary multi-objective optimization publication-title: Proc IEEE Congr Evol Comput (CEC) – ident: ref11 doi: 10.1162/EVCO_a_00009 – ident: ref3 doi: 10.1016/j.swevo.2011.03.001 – ident: ref19 doi: 10.1109/TEVC.2007.894202 – ident: ref36 doi: 10.1016/j.neucom.2014.03.070 – start-page: 280 year: 2005 ident: ref47 article-title: A scalable multi-objective test problem toolkit publication-title: Proc Int Conf Evolutionary Multi-Criterion Optimization (EMO) doi: 10.1007/978-3-540-31880-4_20 – volume: 18 start-page: 909 year: 2014 ident: ref42 article-title: Stable matching-based selection in evolutionary multiobjective optimization publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2013.2293776
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SubjectTerms	Algorithm design and analysis Clustering algorithm Electronic mail evolutionary algorithms Evolutionary computation multiobjective optimization self-organizing map (SOM) Shape Sociology Statistics Training
Title	A Self-Organizing Multiobjective Evolutionary Algorithm
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