An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts

The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sh...

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Veröffentlicht in:	IEEE transactions on cybernetics Jg. 46; H. 2; S. 421 - 437
Hauptverfasser:	Jiang, Shouyong, Yang, Shengxiang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.02.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Decomposition Disengaging Evolutionary algorithms Evolutionary computation Mathematical models Mathematical programming Mopping Multiobjective evolutionary algorithm (MOEA) multiobjective evolutionary algorithm based on decomposition (MOEA/D) multiobjective optimization niching Optical fibers Optimization Reproduction Shape Sociology Statistics test problems Vectors multiobjective evolutionary algorithm based on decomposition (MOEA/D) multiobjective optimization test problems Multiobjective evolutionary algorithm (MOEA) niching
ISSN:	2168-2267, 2168-2275, 2168-2275
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Abstract	The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantly degrades the performance of MOEA/D. This paper proposes an improved MOEA/D for handling such kind of complex problems. In the proposed algorithm, a two-phase strategy (TP) is employed to divide the whole optimization procedure into two phases. Based on the crowdedness of solutions found in the first phase, the algorithm decides whether or not to delicate computational resources to handle unsolved subproblems in the second phase. Besides, a new niche scheme is introduced into the improved MOEA/D to guide the selection of mating parents to avoid producing duplicate solutions, which is very helpful for maintaining the population diversity when the POF of the MOP being optimized is discontinuous. The performance of the proposed algorithm is investigated on some existing benchmark and newly designed MOPs with complex POF shapes in comparison with several MOEA/D variants and other approaches. The experimental results show that the proposed algorithm produces promising performance on these complex problems.
AbstractList	The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantly degrades the performance of MOEA/D. This paper proposes an improved MOEA/D for handling such kind of complex problems. In the proposed algorithm, a two-phase strategy (TP) is employed to divide the whole optimization procedure into two phases. Based on the crowdedness of solutions found in the first phase, the algorithm decides whether or not to delicate computational resources to handle unsolved subproblems in the second phase. Besides, a new niche scheme is introduced into the improved MOEA/D to guide the selection of mating parents to avoid producing duplicate solutions, which is very helpful for maintaining the population diversity when the POF of the MOP being optimized is discontinuous. The performance of the proposed algorithm is investigated on some existing benchmark and newly designed MOPs with complex POF shapes in comparison with several MOEA/D variants and other approaches. The experimental results show that the proposed algorithm produces promising performance on these complex problems. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantly degrades the performance of MOEA/D. This paper proposes an improved MOEA/D for handling such kind of complex problems. In the proposed algorithm, a two-phase strategy (TP) is employed to divide the whole optimization procedure into two phases. Based on the crowdedness of solutions found in the first phase, the algorithm decides whether or not to delicate computational resources to handle unsolved subproblems in the second phase. Besides, a new niche scheme is introduced into the improved MOEA/D to guide the selection of mating parents to avoid producing duplicate solutions, which is very helpful for maintaining the population diversity when the POF of the MOP being optimized is discontinuous. The performance of the proposed algorithm is investigated on some existing benchmark and newly designed MOPs with complex POF shapes in comparison with several MOEA/D variants and other approaches. The experimental results show that the proposed algorithm produces promising performance on these complex problems.The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantly degrades the performance of MOEA/D. This paper proposes an improved MOEA/D for handling such kind of complex problems. In the proposed algorithm, a two-phase strategy (TP) is employed to divide the whole optimization procedure into two phases. Based on the crowdedness of solutions found in the first phase, the algorithm decides whether or not to delicate computational resources to handle unsolved subproblems in the second phase. Besides, a new niche scheme is introduced into the improved MOEA/D to guide the selection of mating parents to avoid producing duplicate solutions, which is very helpful for maintaining the population diversity when the POF of the MOP being optimized is discontinuous. The performance of the proposed algorithm is investigated on some existing benchmark and newly designed MOPs with complex POF shapes in comparison with several MOEA/D variants and other approaches. The experimental results show that the proposed algorithm produces promising performance on these complex problems.
Author	Shengxiang Yang Shouyong Jiang
Author_xml	– sequence: 1 givenname: Shouyong surname: Jiang fullname: Jiang, Shouyong – sequence: 2 givenname: Shengxiang orcidid: 0000-0001-7222-4917 surname: Yang fullname: Yang, Shengxiang
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Cites_doi	10.1109/TCYB.2014.2360074 10.1145/1830483.1830577 10.1080/0305215042000274942 10.1109/TCYB.2014.2367526 10.1109/CEC.2010.5586185 10.1109/TEVC.2013.2262178 10.1155/2014/423621 10.1109/CEC.2003.1299427 10.1162/EVCO_a_00109 10.1007/978-3-642-01020-0_35 10.1109/4235.996017 10.1109/TSMCB.2012.2231860 10.1109/TCYB.2014.2360923 10.1109/TEVC.2013.2293776 10.1007/s00158-013-0925-6 10.1109/4235.797969 10.1109/TCYB.2013.2282503 10.1109/CIS.2010.37 10.1109/TEVC.2011.2166159 10.1016/j.cor.2012.01.001 10.1109/CEC.1999.781913 10.1109/TCYB.2013.2295886 10.1142/S021821301450002X 10.1109/5326.704576 10.1016/j.neucom.2014.04.068 10.1109/TEVC.2003.810758 10.1109/TEVC.2007.892759 10.1155/2014/906147 10.1016/j.ins.2014.05.045 10.1109/TEVC.2003.812220 10.1109/TEVC.2008.925798 10.1109/TEVC.2010.2051446 10.1109/TCYB.2014.2317693 10.1007/978-1-4757-5184-0 10.1109/TEVC.2013.2281533 10.1109/TEVC.2013.2281535 10.1109/TEVC.2012.2204403 10.1109/CEC.2009.4982949
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References	ref13 ref12 ref15 ref14 ref10 ishibuchi (ref16) 2006 price (ref38) 2005 li (ref23) 0 tan (ref42) 2005; 166 ref17 ref19 li (ref28) 2014; 44 goldberg (ref11) 1987 ref46 ref45 ref48 ref47 ref41 ref44 ref43 deb (ref9) 2001 chiang (ref4) 2011 ref49 ref8 ref7 ref3 ref5 ref40 deb (ref6) 2001 ref35 ref34 ref37 ref36 ref31 ishibuchi (ref18) 2009 ref33 ref32 ref2 ref1 li (ref27) 2013; 43 ref24 ref26 ref25 ref22 ref21 jiang (ref20) 2011; 3 shanghai china poloni (ref39) 1997 ref29 liu (ref30) 2010
References_xml	– ident: ref1 doi: 10.1109/TCYB.2014.2360074 – ident: ref19 doi: 10.1145/1830483.1830577 – ident: ref35 doi: 10.1080/0305215042000274942 – ident: ref21 doi: 10.1109/TCYB.2014.2367526 – ident: ref45 doi: 10.1109/CEC.2010.5586185 – ident: ref24 doi: 10.1109/TEVC.2013.2262178 – ident: ref33 doi: 10.1155/2014/423621 – year: 2001 ident: ref6 publication-title: Multi-Objective Optimization Using Evolutionary Algorithms – ident: ref15 doi: 10.1109/CEC.2003.1299427 – ident: ref40 doi: 10.1162/EVCO_a_00109 – volume: 3 shanghai china start-page: 1260 year: 2011 ident: ref20 article-title: Multiobjective optimization by decomposition with Pareto-adaptive weight vectors publication-title: Proc Int Conf Nat Comput – start-page: 438 year: 2009 ident: ref18 article-title: Adaptation of scalarizing functions in MOEA/D: An adaptive scalarizing function-based multiobjective evolutionary algorithm publication-title: Proc 5th Int Conf Evol Multi-Criterion Optim doi: 10.1007/978-3-642-01020-0_35 – ident: ref7 doi: 10.1109/4235.996017 – volume: 43 start-page: 1845 year: 2013 ident: ref27 article-title: MOEA/D-ACO: A multiobjective evolutionary algorithm using decomposition and ant colony publication-title: IEEE Trans Cybern doi: 10.1109/TSMCB.2012.2231860 – ident: ref3 doi: 10.1109/TCYB.2014.2360923 – ident: ref29 doi: 10.1109/TEVC.2013.2293776 – ident: ref13 doi: 10.1007/s00158-013-0925-6 – year: 2005 ident: ref38 publication-title: Differential Evolution A Practical Approach to Global Optimization (Natural Computing Series) – ident: ref48 doi: 10.1109/4235.797969 – ident: ref25 doi: 10.1109/TCYB.2013.2282503 – ident: ref12 doi: 10.1109/CIS.2010.37 – ident: ref47 doi: 10.1109/TEVC.2011.2166159 – ident: ref43 doi: 10.1016/j.cor.2012.01.001 – ident: ref22 doi: 10.1109/CEC.1999.781913 – year: 0 ident: ref23 article-title: Inter-relationship based selection for decomposition multiobjective optimization publication-title: IEEE Trans Cybern – start-page: 282 year: 2010 ident: ref30 article-title: T-MOEA/D: MOEA/D with objective transform in multiobjective problems publication-title: Proc Int Conf Inf Sci Manage Eng – start-page: 41 year: 1987 ident: ref11 article-title: Genetic algorithms with sharing for multimodal function optimization publication-title: Proc 2nd Int Conf Genet Algorithms – year: 2001 ident: ref9 article-title: Scable test problems for evolutionary multi-objective optimization – volume: 44 start-page: 1808 year: 2014 ident: ref28 article-title: Hybridization of decomposition and local search for multiobjective optimization publication-title: IEEE Trans Cybern doi: 10.1109/TCYB.2013.2295886 – ident: ref14 doi: 10.1142/S021821301450002X – ident: ref17 doi: 10.1109/5326.704576 – ident: ref34 doi: 10.1016/j.neucom.2014.04.068 – ident: ref49 doi: 10.1109/TEVC.2003.810758 – ident: ref44 doi: 10.1109/TEVC.2007.892759 – ident: ref5 doi: 10.1155/2014/906147 – start-page: 397 year: 1997 ident: ref39 article-title: Hybrid GA for multiobjective aerodynamic shape optimization publication-title: Genetic Algorithms in Engineering and Computer Science – ident: ref10 doi: 10.1016/j.ins.2014.05.045 – ident: ref32 doi: 10.1109/TEVC.2003.812220 – start-page: 493 year: 2006 ident: ref16 article-title: Incorporation of scalarizing fitness functions into evolutionary multiobjective optimization algorithms publication-title: Proc 9th Int Conf Parallel Probl Solv Nat – ident: ref26 doi: 10.1109/TEVC.2008.925798 – ident: ref36 doi: 10.1109/TEVC.2010.2051446 – ident: ref37 doi: 10.1109/TCYB.2014.2317693 – volume: 166 start-page: 313 year: 2005 ident: ref42 publication-title: Multiobjective Evolutionary Algorithms and Applications – ident: ref2 doi: 10.1007/978-1-4757-5184-0 – ident: ref31 doi: 10.1109/TEVC.2013.2281533 – start-page: 1473 year: 2011 ident: ref4 article-title: MOEA/D-AWS: Improving MOEA/D by an adaptive mating selection mechanism publication-title: Proc IEEE Congr Evol Comput – ident: ref8 doi: 10.1109/TEVC.2013.2281535 – ident: ref41 doi: 10.1109/TEVC.2012.2204403 – ident: ref46 doi: 10.1109/CEC.2009.4982949
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Snippet	The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems...
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SubjectTerms	Algorithms Decomposition Disengaging Evolutionary algorithms Evolutionary computation Mathematical models Mathematical programming Mopping Multiobjective evolutionary algorithm (MOEA) multiobjective evolutionary algorithm based on decomposition (MOEA/D) multiobjective optimization niching Optical fibers Optimization Reproduction Shape Sociology Statistics test problems Vectors
Title	An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts
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