A constrained multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism

Constrained multi-objective optimization problems (CMOPs) are common in real-world engineering application, and are difficult to solve because of the conflicting nature of the objectives and many constraints. Some constrained multi-objective evolutionary algorithms (CMOEAs) have been developed for C...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yang, Yongkuan [verfasserIn] Liu, Jianchang [verfasserIn] Tan, Shubin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Constrained multi-objective optimization MOEA/D Constraint-handling techniques Epsilon constraint-handling Differential evolution

Übergeordnetes Werk:	Enthalten in: Applied soft computing - Amsterdam [u.a.] : Elsevier Science, 2001, 89
Übergeordnetes Werk:	volume:89

DOI / URN:	10.1016/j.asoc.2020.106104

Katalog-ID:	ELV003828719

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245	1	0	\|a A constrained multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism
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520			\|a Constrained multi-objective optimization problems (CMOPs) are common in real-world engineering application, and are difficult to solve because of the conflicting nature of the objectives and many constraints. Some constrained multi-objective evolutionary algorithms (CMOEAs) have been developed for CMOPs, but they still suffer from the problems of easily getting trapped into local optimal solutions and low convergence. This paper introduces a multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism (MOEA/D-DCH) to tackle this issue. Firstly, the dynamic constraint-handling mechanism divides the search modes into the unconstrained search mode and the constrained search mode, which are dynamically adjusted by the generation number and the proportion of feasible solutions in the population. This mechanism could lead to a faster convergence than the traditional constraint-handling mechanisms. For the constrained search mode, an improved epsilon constraint-handling method is used to enhance the diversity of the population. Then, an individual update mechanism based on the best feasible solution of each sub-problem is designed to update the feasible individuals for maintaining the convergence of the feasible solutions. Finally, MOEA/D-DCH dynamically regulates the parameters of the differential evolution operator to enhance the local search ability. Experiments on 21 benchmark test functions are conducted to test MOEA/D-DCH and five other typical CMOEAs. Meanwhile, a real-world problem is employed to evaluate the practical performance of MOEA/D-DCH. MOEA/D-DCH achieves significantly better results than the other five algorithms on most of the test problems. The results indicate the effectiveness and competitiveness of MOEA/D-DCH for solving CMOPs.
650		4	\|a Constrained multi-objective optimization
650		4	\|a MOEA/D
650		4	\|a Constraint-handling techniques
650		4	\|a Epsilon constraint-handling
650		4	\|a Differential evolution
700	1		\|a Liu, Jianchang \|e verfasserin \|0 (orcid)0000-0002-2801-8312 \|4 aut
700	1		\|a Tan, Shubin \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Applied soft computing \|d Amsterdam [u.a.] : Elsevier Science, 2001 \|g 89 \|h Online-Ressource \|w (DE-627)334375754 \|w (DE-600)2057709-6 \|w (DE-576)256145733 \|x 1568-4946 \|7 nnns
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912			\|a GBV_ILN_4325
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912			\|a GBV_ILN_4393
936	b	k	\|a 54.00 \|j Informatik: Allgemeines
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952			\|d 89

Indexfelder

author_variant	y y yy j l jl s t st
matchkey_str	article:15684946:2020----::cntandutojcievltoayloihbsdneopstoadyaic
hierarchy_sort_str	2020
bklnumber	54.00
publishDate	2020
allfields	10.1016/j.asoc.2020.106104 doi (DE-627)ELV003828719 (ELSEVIER)S1568-4946(20)30044-2 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Yang, Yongkuan verfasserin aut A constrained multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Constrained multi-objective optimization problems (CMOPs) are common in real-world engineering application, and are difficult to solve because of the conflicting nature of the objectives and many constraints. Some constrained multi-objective evolutionary algorithms (CMOEAs) have been developed for CMOPs, but they still suffer from the problems of easily getting trapped into local optimal solutions and low convergence. This paper introduces a multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism (MOEA/D-DCH) to tackle this issue. Firstly, the dynamic constraint-handling mechanism divides the search modes into the unconstrained search mode and the constrained search mode, which are dynamically adjusted by the generation number and the proportion of feasible solutions in the population. This mechanism could lead to a faster convergence than the traditional constraint-handling mechanisms. For the constrained search mode, an improved epsilon constraint-handling method is used to enhance the diversity of the population. Then, an individual update mechanism based on the best feasible solution of each sub-problem is designed to update the feasible individuals for maintaining the convergence of the feasible solutions. Finally, MOEA/D-DCH dynamically regulates the parameters of the differential evolution operator to enhance the local search ability. Experiments on 21 benchmark test functions are conducted to test MOEA/D-DCH and five other typical CMOEAs. Meanwhile, a real-world problem is employed to evaluate the practical performance of MOEA/D-DCH. MOEA/D-DCH achieves significantly better results than the other five algorithms on most of the test problems. The results indicate the effectiveness and competitiveness of MOEA/D-DCH for solving CMOPs. Constrained multi-objective optimization MOEA/D Constraint-handling techniques Epsilon constraint-handling Differential evolution Liu, Jianchang verfasserin (orcid)0000-0002-2801-8312 aut Tan, Shubin verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 89 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:89 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 89
spelling	10.1016/j.asoc.2020.106104 doi (DE-627)ELV003828719 (ELSEVIER)S1568-4946(20)30044-2 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Yang, Yongkuan verfasserin aut A constrained multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Constrained multi-objective optimization problems (CMOPs) are common in real-world engineering application, and are difficult to solve because of the conflicting nature of the objectives and many constraints. Some constrained multi-objective evolutionary algorithms (CMOEAs) have been developed for CMOPs, but they still suffer from the problems of easily getting trapped into local optimal solutions and low convergence. This paper introduces a multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism (MOEA/D-DCH) to tackle this issue. Firstly, the dynamic constraint-handling mechanism divides the search modes into the unconstrained search mode and the constrained search mode, which are dynamically adjusted by the generation number and the proportion of feasible solutions in the population. This mechanism could lead to a faster convergence than the traditional constraint-handling mechanisms. For the constrained search mode, an improved epsilon constraint-handling method is used to enhance the diversity of the population. Then, an individual update mechanism based on the best feasible solution of each sub-problem is designed to update the feasible individuals for maintaining the convergence of the feasible solutions. Finally, MOEA/D-DCH dynamically regulates the parameters of the differential evolution operator to enhance the local search ability. Experiments on 21 benchmark test functions are conducted to test MOEA/D-DCH and five other typical CMOEAs. Meanwhile, a real-world problem is employed to evaluate the practical performance of MOEA/D-DCH. MOEA/D-DCH achieves significantly better results than the other five algorithms on most of the test problems. The results indicate the effectiveness and competitiveness of MOEA/D-DCH for solving CMOPs. Constrained multi-objective optimization MOEA/D Constraint-handling techniques Epsilon constraint-handling Differential evolution Liu, Jianchang verfasserin (orcid)0000-0002-2801-8312 aut Tan, Shubin verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 89 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:89 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 89
allfields_unstemmed	10.1016/j.asoc.2020.106104 doi (DE-627)ELV003828719 (ELSEVIER)S1568-4946(20)30044-2 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Yang, Yongkuan verfasserin aut A constrained multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Constrained multi-objective optimization problems (CMOPs) are common in real-world engineering application, and are difficult to solve because of the conflicting nature of the objectives and many constraints. Some constrained multi-objective evolutionary algorithms (CMOEAs) have been developed for CMOPs, but they still suffer from the problems of easily getting trapped into local optimal solutions and low convergence. This paper introduces a multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism (MOEA/D-DCH) to tackle this issue. Firstly, the dynamic constraint-handling mechanism divides the search modes into the unconstrained search mode and the constrained search mode, which are dynamically adjusted by the generation number and the proportion of feasible solutions in the population. This mechanism could lead to a faster convergence than the traditional constraint-handling mechanisms. For the constrained search mode, an improved epsilon constraint-handling method is used to enhance the diversity of the population. Then, an individual update mechanism based on the best feasible solution of each sub-problem is designed to update the feasible individuals for maintaining the convergence of the feasible solutions. Finally, MOEA/D-DCH dynamically regulates the parameters of the differential evolution operator to enhance the local search ability. Experiments on 21 benchmark test functions are conducted to test MOEA/D-DCH and five other typical CMOEAs. Meanwhile, a real-world problem is employed to evaluate the practical performance of MOEA/D-DCH. MOEA/D-DCH achieves significantly better results than the other five algorithms on most of the test problems. The results indicate the effectiveness and competitiveness of MOEA/D-DCH for solving CMOPs. Constrained multi-objective optimization MOEA/D Constraint-handling techniques Epsilon constraint-handling Differential evolution Liu, Jianchang verfasserin (orcid)0000-0002-2801-8312 aut Tan, Shubin verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 89 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:89 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 89
allfieldsGer	10.1016/j.asoc.2020.106104 doi (DE-627)ELV003828719 (ELSEVIER)S1568-4946(20)30044-2 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Yang, Yongkuan verfasserin aut A constrained multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Constrained multi-objective optimization problems (CMOPs) are common in real-world engineering application, and are difficult to solve because of the conflicting nature of the objectives and many constraints. Some constrained multi-objective evolutionary algorithms (CMOEAs) have been developed for CMOPs, but they still suffer from the problems of easily getting trapped into local optimal solutions and low convergence. This paper introduces a multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism (MOEA/D-DCH) to tackle this issue. Firstly, the dynamic constraint-handling mechanism divides the search modes into the unconstrained search mode and the constrained search mode, which are dynamically adjusted by the generation number and the proportion of feasible solutions in the population. This mechanism could lead to a faster convergence than the traditional constraint-handling mechanisms. For the constrained search mode, an improved epsilon constraint-handling method is used to enhance the diversity of the population. Then, an individual update mechanism based on the best feasible solution of each sub-problem is designed to update the feasible individuals for maintaining the convergence of the feasible solutions. Finally, MOEA/D-DCH dynamically regulates the parameters of the differential evolution operator to enhance the local search ability. Experiments on 21 benchmark test functions are conducted to test MOEA/D-DCH and five other typical CMOEAs. Meanwhile, a real-world problem is employed to evaluate the practical performance of MOEA/D-DCH. MOEA/D-DCH achieves significantly better results than the other five algorithms on most of the test problems. The results indicate the effectiveness and competitiveness of MOEA/D-DCH for solving CMOPs. Constrained multi-objective optimization MOEA/D Constraint-handling techniques Epsilon constraint-handling Differential evolution Liu, Jianchang verfasserin (orcid)0000-0002-2801-8312 aut Tan, Shubin verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 89 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:89 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 89
allfieldsSound	10.1016/j.asoc.2020.106104 doi (DE-627)ELV003828719 (ELSEVIER)S1568-4946(20)30044-2 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Yang, Yongkuan verfasserin aut A constrained multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Constrained multi-objective optimization problems (CMOPs) are common in real-world engineering application, and are difficult to solve because of the conflicting nature of the objectives and many constraints. Some constrained multi-objective evolutionary algorithms (CMOEAs) have been developed for CMOPs, but they still suffer from the problems of easily getting trapped into local optimal solutions and low convergence. This paper introduces a multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism (MOEA/D-DCH) to tackle this issue. Firstly, the dynamic constraint-handling mechanism divides the search modes into the unconstrained search mode and the constrained search mode, which are dynamically adjusted by the generation number and the proportion of feasible solutions in the population. This mechanism could lead to a faster convergence than the traditional constraint-handling mechanisms. For the constrained search mode, an improved epsilon constraint-handling method is used to enhance the diversity of the population. Then, an individual update mechanism based on the best feasible solution of each sub-problem is designed to update the feasible individuals for maintaining the convergence of the feasible solutions. Finally, MOEA/D-DCH dynamically regulates the parameters of the differential evolution operator to enhance the local search ability. Experiments on 21 benchmark test functions are conducted to test MOEA/D-DCH and five other typical CMOEAs. Meanwhile, a real-world problem is employed to evaluate the practical performance of MOEA/D-DCH. MOEA/D-DCH achieves significantly better results than the other five algorithms on most of the test problems. The results indicate the effectiveness and competitiveness of MOEA/D-DCH for solving CMOPs. Constrained multi-objective optimization MOEA/D Constraint-handling techniques Epsilon constraint-handling Differential evolution Liu, Jianchang verfasserin (orcid)0000-0002-2801-8312 aut Tan, Shubin verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 89 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:89 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 89
language	English
source	Enthalten in Applied soft computing 89 volume:89
sourceStr	Enthalten in Applied soft computing 89 volume:89
format_phy_str_mv	Article
bklname	Informatik: Allgemeines
institution	findex.gbv.de
topic_facet	Constrained multi-objective optimization MOEA/D Constraint-handling techniques Epsilon constraint-handling Differential evolution
dewey-raw	004
isfreeaccess_bool	false
container_title	Applied soft computing
authorswithroles_txt_mv	Yang, Yongkuan @@aut@@ Liu, Jianchang @@aut@@ Tan, Shubin @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	334375754
dewey-sort	14
id	ELV003828719
language_de	englisch
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author	Yang, Yongkuan
spellingShingle	Yang, Yongkuan ddc 004 bkl 54.00 misc Constrained multi-objective optimization misc MOEA/D misc Constraint-handling techniques misc Epsilon constraint-handling misc Differential evolution A constrained multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism
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topic_title	004 DE-600 54.00 bkl A constrained multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism Constrained multi-objective optimization MOEA/D Constraint-handling techniques Epsilon constraint-handling Differential evolution
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title	A constrained multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism
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title_full	A constrained multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism
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title_sort	a constrained multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism
title_auth	A constrained multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism
abstract	Constrained multi-objective optimization problems (CMOPs) are common in real-world engineering application, and are difficult to solve because of the conflicting nature of the objectives and many constraints. Some constrained multi-objective evolutionary algorithms (CMOEAs) have been developed for CMOPs, but they still suffer from the problems of easily getting trapped into local optimal solutions and low convergence. This paper introduces a multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism (MOEA/D-DCH) to tackle this issue. Firstly, the dynamic constraint-handling mechanism divides the search modes into the unconstrained search mode and the constrained search mode, which are dynamically adjusted by the generation number and the proportion of feasible solutions in the population. This mechanism could lead to a faster convergence than the traditional constraint-handling mechanisms. For the constrained search mode, an improved epsilon constraint-handling method is used to enhance the diversity of the population. Then, an individual update mechanism based on the best feasible solution of each sub-problem is designed to update the feasible individuals for maintaining the convergence of the feasible solutions. Finally, MOEA/D-DCH dynamically regulates the parameters of the differential evolution operator to enhance the local search ability. Experiments on 21 benchmark test functions are conducted to test MOEA/D-DCH and five other typical CMOEAs. Meanwhile, a real-world problem is employed to evaluate the practical performance of MOEA/D-DCH. MOEA/D-DCH achieves significantly better results than the other five algorithms on most of the test problems. The results indicate the effectiveness and competitiveness of MOEA/D-DCH for solving CMOPs.
abstractGer	Constrained multi-objective optimization problems (CMOPs) are common in real-world engineering application, and are difficult to solve because of the conflicting nature of the objectives and many constraints. Some constrained multi-objective evolutionary algorithms (CMOEAs) have been developed for CMOPs, but they still suffer from the problems of easily getting trapped into local optimal solutions and low convergence. This paper introduces a multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism (MOEA/D-DCH) to tackle this issue. Firstly, the dynamic constraint-handling mechanism divides the search modes into the unconstrained search mode and the constrained search mode, which are dynamically adjusted by the generation number and the proportion of feasible solutions in the population. This mechanism could lead to a faster convergence than the traditional constraint-handling mechanisms. For the constrained search mode, an improved epsilon constraint-handling method is used to enhance the diversity of the population. Then, an individual update mechanism based on the best feasible solution of each sub-problem is designed to update the feasible individuals for maintaining the convergence of the feasible solutions. Finally, MOEA/D-DCH dynamically regulates the parameters of the differential evolution operator to enhance the local search ability. Experiments on 21 benchmark test functions are conducted to test MOEA/D-DCH and five other typical CMOEAs. Meanwhile, a real-world problem is employed to evaluate the practical performance of MOEA/D-DCH. MOEA/D-DCH achieves significantly better results than the other five algorithms on most of the test problems. The results indicate the effectiveness and competitiveness of MOEA/D-DCH for solving CMOPs.
abstract_unstemmed	Constrained multi-objective optimization problems (CMOPs) are common in real-world engineering application, and are difficult to solve because of the conflicting nature of the objectives and many constraints. Some constrained multi-objective evolutionary algorithms (CMOEAs) have been developed for CMOPs, but they still suffer from the problems of easily getting trapped into local optimal solutions and low convergence. This paper introduces a multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism (MOEA/D-DCH) to tackle this issue. Firstly, the dynamic constraint-handling mechanism divides the search modes into the unconstrained search mode and the constrained search mode, which are dynamically adjusted by the generation number and the proportion of feasible solutions in the population. This mechanism could lead to a faster convergence than the traditional constraint-handling mechanisms. For the constrained search mode, an improved epsilon constraint-handling method is used to enhance the diversity of the population. Then, an individual update mechanism based on the best feasible solution of each sub-problem is designed to update the feasible individuals for maintaining the convergence of the feasible solutions. Finally, MOEA/D-DCH dynamically regulates the parameters of the differential evolution operator to enhance the local search ability. Experiments on 21 benchmark test functions are conducted to test MOEA/D-DCH and five other typical CMOEAs. Meanwhile, a real-world problem is employed to evaluate the practical performance of MOEA/D-DCH. MOEA/D-DCH achieves significantly better results than the other five algorithms on most of the test problems. The results indicate the effectiveness and competitiveness of MOEA/D-DCH for solving CMOPs.
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title_short	A constrained multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism
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up_date	2024-07-06T20:55:42.605Z
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A constrained multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism

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