A new selection operator for differential evolution algorithm

Most research on improving differential evolution algorithms has focused on mutation operator and parameter control. In this paper, a new selection operator is proposed to improve differential evolution algorithm performance. When the individual is not in a state of stagnation, the proposed selectio...
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Gespeichert in:

Autor*in:	Zeng, Zhiqiang [verfasserIn] Zhang, Min [verfasserIn] Chen, Tao [verfasserIn] Hong, Zhiyong [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Differential evolution Selection operator Global numerical optimization

Übergeordnetes Werk:	Enthalten in: Knowledge-based systems - Amsterdam [u.a.] : Elsevier Science, 1987, 226
Übergeordnetes Werk:	volume:226

DOI / URN:	10.1016/j.knosys.2021.107150

Katalog-ID:	ELV006183603

Internformat


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245	1	0	\|a A new selection operator for differential evolution algorithm
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337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Most research on improving differential evolution algorithms has focused on mutation operator and parameter control. In this paper, a new selection operator is proposed to improve differential evolution algorithm performance. When the individual is not in a state of stagnation, the proposed selection operator is the same as the classical selection operator, meaning that it chooses the best vector from the trial vector and parent vector to survive to the next generation. When the individual is in a state of stagnation, the three other candidate vectors may survive to the next generation. The first candidate vector is the best vector of all the discarded trial vectors of the parent vector. The second candidate vector is the second-best vector of all the discarded trial vectors of the parent vector. The third candidate vector is randomly chosen from all the successfully updated solutions. The proposed selection operator will improve the differential evolution algorithm’s ability to escape the local optimal value. 58 benchmark functions are used for verification of the proposed selection operator’s performance. Experiments were conducted in order to compare six differential evolution algorithms’ performances using the proposed selection operator and not using the proposed selection operator. Simulation results showed that the proposed selection operator significantly improved the differential evolution algorithm’s performance.
650		4	\|a Differential evolution
650		4	\|a Selection operator
650		4	\|a Global numerical optimization
700	1		\|a Zhang, Min \|e verfasserin \|4 aut
700	1		\|a Chen, Tao \|e verfasserin \|0 (orcid)0000-0002-3634-0854 \|4 aut
700	1		\|a Hong, Zhiyong \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Knowledge-based systems \|d Amsterdam [u.a.] : Elsevier Science, 1987 \|g 226 \|h Online-Ressource \|w (DE-627)320580024 \|w (DE-600)2017495-0 \|w (DE-576)253018722 \|x 0950-7051 \|7 nnns
773	1	8	\|g volume:226
912			\|a GBV_USEFLAG_U
912			\|a SYSFLAG_U
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912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 54.72 \|j Künstliche Intelligenz
951			\|a AR
952			\|d 226

Indexfelder

author_variant	z z zz m z mz t c tc z h zh
matchkey_str	article:09507051:2021----::nweetooeaofrifrnilv
hierarchy_sort_str	2021
bklnumber	54.72
publishDate	2021
allfields	10.1016/j.knosys.2021.107150 doi (DE-627)ELV006183603 (ELSEVIER)S0950-7051(21)00413-5 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Zeng, Zhiqiang verfasserin aut A new selection operator for differential evolution algorithm 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Most research on improving differential evolution algorithms has focused on mutation operator and parameter control. In this paper, a new selection operator is proposed to improve differential evolution algorithm performance. When the individual is not in a state of stagnation, the proposed selection operator is the same as the classical selection operator, meaning that it chooses the best vector from the trial vector and parent vector to survive to the next generation. When the individual is in a state of stagnation, the three other candidate vectors may survive to the next generation. The first candidate vector is the best vector of all the discarded trial vectors of the parent vector. The second candidate vector is the second-best vector of all the discarded trial vectors of the parent vector. The third candidate vector is randomly chosen from all the successfully updated solutions. The proposed selection operator will improve the differential evolution algorithm’s ability to escape the local optimal value. 58 benchmark functions are used for verification of the proposed selection operator’s performance. Experiments were conducted in order to compare six differential evolution algorithms’ performances using the proposed selection operator and not using the proposed selection operator. Simulation results showed that the proposed selection operator significantly improved the differential evolution algorithm’s performance. Differential evolution Selection operator Global numerical optimization Zhang, Min verfasserin aut Chen, Tao verfasserin (orcid)0000-0002-3634-0854 aut Hong, Zhiyong verfasserin aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 226 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:226 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_2008 GBV_ILN_2010 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_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 54.72 Künstliche Intelligenz AR 226
spelling	10.1016/j.knosys.2021.107150 doi (DE-627)ELV006183603 (ELSEVIER)S0950-7051(21)00413-5 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Zeng, Zhiqiang verfasserin aut A new selection operator for differential evolution algorithm 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Most research on improving differential evolution algorithms has focused on mutation operator and parameter control. In this paper, a new selection operator is proposed to improve differential evolution algorithm performance. When the individual is not in a state of stagnation, the proposed selection operator is the same as the classical selection operator, meaning that it chooses the best vector from the trial vector and parent vector to survive to the next generation. When the individual is in a state of stagnation, the three other candidate vectors may survive to the next generation. The first candidate vector is the best vector of all the discarded trial vectors of the parent vector. The second candidate vector is the second-best vector of all the discarded trial vectors of the parent vector. The third candidate vector is randomly chosen from all the successfully updated solutions. The proposed selection operator will improve the differential evolution algorithm’s ability to escape the local optimal value. 58 benchmark functions are used for verification of the proposed selection operator’s performance. Experiments were conducted in order to compare six differential evolution algorithms’ performances using the proposed selection operator and not using the proposed selection operator. Simulation results showed that the proposed selection operator significantly improved the differential evolution algorithm’s performance. Differential evolution Selection operator Global numerical optimization Zhang, Min verfasserin aut Chen, Tao verfasserin (orcid)0000-0002-3634-0854 aut Hong, Zhiyong verfasserin aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 226 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:226 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_2008 GBV_ILN_2010 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_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 54.72 Künstliche Intelligenz AR 226
allfields_unstemmed	10.1016/j.knosys.2021.107150 doi (DE-627)ELV006183603 (ELSEVIER)S0950-7051(21)00413-5 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Zeng, Zhiqiang verfasserin aut A new selection operator for differential evolution algorithm 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Most research on improving differential evolution algorithms has focused on mutation operator and parameter control. In this paper, a new selection operator is proposed to improve differential evolution algorithm performance. When the individual is not in a state of stagnation, the proposed selection operator is the same as the classical selection operator, meaning that it chooses the best vector from the trial vector and parent vector to survive to the next generation. When the individual is in a state of stagnation, the three other candidate vectors may survive to the next generation. The first candidate vector is the best vector of all the discarded trial vectors of the parent vector. The second candidate vector is the second-best vector of all the discarded trial vectors of the parent vector. The third candidate vector is randomly chosen from all the successfully updated solutions. The proposed selection operator will improve the differential evolution algorithm’s ability to escape the local optimal value. 58 benchmark functions are used for verification of the proposed selection operator’s performance. Experiments were conducted in order to compare six differential evolution algorithms’ performances using the proposed selection operator and not using the proposed selection operator. Simulation results showed that the proposed selection operator significantly improved the differential evolution algorithm’s performance. Differential evolution Selection operator Global numerical optimization Zhang, Min verfasserin aut Chen, Tao verfasserin (orcid)0000-0002-3634-0854 aut Hong, Zhiyong verfasserin aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 226 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:226 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_2008 GBV_ILN_2010 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_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 54.72 Künstliche Intelligenz AR 226
allfieldsGer	10.1016/j.knosys.2021.107150 doi (DE-627)ELV006183603 (ELSEVIER)S0950-7051(21)00413-5 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Zeng, Zhiqiang verfasserin aut A new selection operator for differential evolution algorithm 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Most research on improving differential evolution algorithms has focused on mutation operator and parameter control. In this paper, a new selection operator is proposed to improve differential evolution algorithm performance. When the individual is not in a state of stagnation, the proposed selection operator is the same as the classical selection operator, meaning that it chooses the best vector from the trial vector and parent vector to survive to the next generation. When the individual is in a state of stagnation, the three other candidate vectors may survive to the next generation. The first candidate vector is the best vector of all the discarded trial vectors of the parent vector. The second candidate vector is the second-best vector of all the discarded trial vectors of the parent vector. The third candidate vector is randomly chosen from all the successfully updated solutions. The proposed selection operator will improve the differential evolution algorithm’s ability to escape the local optimal value. 58 benchmark functions are used for verification of the proposed selection operator’s performance. Experiments were conducted in order to compare six differential evolution algorithms’ performances using the proposed selection operator and not using the proposed selection operator. Simulation results showed that the proposed selection operator significantly improved the differential evolution algorithm’s performance. Differential evolution Selection operator Global numerical optimization Zhang, Min verfasserin aut Chen, Tao verfasserin (orcid)0000-0002-3634-0854 aut Hong, Zhiyong verfasserin aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 226 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:226 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_2008 GBV_ILN_2010 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_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 54.72 Künstliche Intelligenz AR 226
allfieldsSound	10.1016/j.knosys.2021.107150 doi (DE-627)ELV006183603 (ELSEVIER)S0950-7051(21)00413-5 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Zeng, Zhiqiang verfasserin aut A new selection operator for differential evolution algorithm 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Most research on improving differential evolution algorithms has focused on mutation operator and parameter control. In this paper, a new selection operator is proposed to improve differential evolution algorithm performance. When the individual is not in a state of stagnation, the proposed selection operator is the same as the classical selection operator, meaning that it chooses the best vector from the trial vector and parent vector to survive to the next generation. When the individual is in a state of stagnation, the three other candidate vectors may survive to the next generation. The first candidate vector is the best vector of all the discarded trial vectors of the parent vector. The second candidate vector is the second-best vector of all the discarded trial vectors of the parent vector. The third candidate vector is randomly chosen from all the successfully updated solutions. The proposed selection operator will improve the differential evolution algorithm’s ability to escape the local optimal value. 58 benchmark functions are used for verification of the proposed selection operator’s performance. Experiments were conducted in order to compare six differential evolution algorithms’ performances using the proposed selection operator and not using the proposed selection operator. Simulation results showed that the proposed selection operator significantly improved the differential evolution algorithm’s performance. Differential evolution Selection operator Global numerical optimization Zhang, Min verfasserin aut Chen, Tao verfasserin (orcid)0000-0002-3634-0854 aut Hong, Zhiyong verfasserin aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 226 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:226 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_2008 GBV_ILN_2010 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_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 54.72 Künstliche Intelligenz AR 226
language	English
source	Enthalten in Knowledge-based systems 226 volume:226
sourceStr	Enthalten in Knowledge-based systems 226 volume:226
format_phy_str_mv	Article
bklname	Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Differential evolution Selection operator Global numerical optimization
dewey-raw	004
isfreeaccess_bool	false
container_title	Knowledge-based systems
authorswithroles_txt_mv	Zeng, Zhiqiang @@aut@@ Zhang, Min @@aut@@ Chen, Tao @@aut@@ Hong, Zhiyong @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	320580024
dewey-sort	14
id	ELV006183603
language_de	englisch
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author	Zeng, Zhiqiang
spellingShingle	Zeng, Zhiqiang ddc 004 bkl 54.72 misc Differential evolution misc Selection operator misc Global numerical optimization A new selection operator for differential evolution algorithm
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topic_title	004 DE-600 54.72 bkl A new selection operator for differential evolution algorithm Differential evolution Selection operator Global numerical optimization
topic	ddc 004 bkl 54.72 misc Differential evolution misc Selection operator misc Global numerical optimization
topic_unstemmed	ddc 004 bkl 54.72 misc Differential evolution misc Selection operator misc Global numerical optimization
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title	A new selection operator for differential evolution algorithm
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title_full	A new selection operator for differential evolution algorithm
author_sort	Zeng, Zhiqiang
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author_browse	Zeng, Zhiqiang Zhang, Min Chen, Tao Hong, Zhiyong
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author-letter	Zeng, Zhiqiang
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title_sort	a new selection operator for differential evolution algorithm
title_auth	A new selection operator for differential evolution algorithm
abstract	Most research on improving differential evolution algorithms has focused on mutation operator and parameter control. In this paper, a new selection operator is proposed to improve differential evolution algorithm performance. When the individual is not in a state of stagnation, the proposed selection operator is the same as the classical selection operator, meaning that it chooses the best vector from the trial vector and parent vector to survive to the next generation. When the individual is in a state of stagnation, the three other candidate vectors may survive to the next generation. The first candidate vector is the best vector of all the discarded trial vectors of the parent vector. The second candidate vector is the second-best vector of all the discarded trial vectors of the parent vector. The third candidate vector is randomly chosen from all the successfully updated solutions. The proposed selection operator will improve the differential evolution algorithm’s ability to escape the local optimal value. 58 benchmark functions are used for verification of the proposed selection operator’s performance. Experiments were conducted in order to compare six differential evolution algorithms’ performances using the proposed selection operator and not using the proposed selection operator. Simulation results showed that the proposed selection operator significantly improved the differential evolution algorithm’s performance.
abstractGer	Most research on improving differential evolution algorithms has focused on mutation operator and parameter control. In this paper, a new selection operator is proposed to improve differential evolution algorithm performance. When the individual is not in a state of stagnation, the proposed selection operator is the same as the classical selection operator, meaning that it chooses the best vector from the trial vector and parent vector to survive to the next generation. When the individual is in a state of stagnation, the three other candidate vectors may survive to the next generation. The first candidate vector is the best vector of all the discarded trial vectors of the parent vector. The second candidate vector is the second-best vector of all the discarded trial vectors of the parent vector. The third candidate vector is randomly chosen from all the successfully updated solutions. The proposed selection operator will improve the differential evolution algorithm’s ability to escape the local optimal value. 58 benchmark functions are used for verification of the proposed selection operator’s performance. Experiments were conducted in order to compare six differential evolution algorithms’ performances using the proposed selection operator and not using the proposed selection operator. Simulation results showed that the proposed selection operator significantly improved the differential evolution algorithm’s performance.
abstract_unstemmed	Most research on improving differential evolution algorithms has focused on mutation operator and parameter control. In this paper, a new selection operator is proposed to improve differential evolution algorithm performance. When the individual is not in a state of stagnation, the proposed selection operator is the same as the classical selection operator, meaning that it chooses the best vector from the trial vector and parent vector to survive to the next generation. When the individual is in a state of stagnation, the three other candidate vectors may survive to the next generation. The first candidate vector is the best vector of all the discarded trial vectors of the parent vector. The second candidate vector is the second-best vector of all the discarded trial vectors of the parent vector. The third candidate vector is randomly chosen from all the successfully updated solutions. The proposed selection operator will improve the differential evolution algorithm’s ability to escape the local optimal value. 58 benchmark functions are used for verification of the proposed selection operator’s performance. Experiments were conducted in order to compare six differential evolution algorithms’ performances using the proposed selection operator and not using the proposed selection operator. Simulation results showed that the proposed selection operator significantly improved the differential evolution algorithm’s performance.
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title_short	A new selection operator for differential evolution algorithm
remote_bool	true
author2	Zhang, Min Chen, Tao Hong, Zhiyong
author2Str	Zhang, Min Chen, Tao Hong, Zhiyong
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doi_str	10.1016/j.knosys.2021.107150
up_date	2024-07-06T20:30:31.484Z
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A new selection operator for differential evolution algorithm

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