A Multi-Objective Carnivorous Plant Algorithm for Solving Constrained Multi-Objective Optimization Problems

Satisfying various constraints and multiple objectives simultaneously is a significant challenge in solving constrained multi-objective optimization problems. To address this issue, a new approach is proposed in this paper that combines multi-population and multi-stage methods with a Carnivorous Pla...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yufei Yang [verfasserIn] Changsheng Zhang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	carnivorous plant algorithm constrained multi-objective optimization cross-pollination quasi-reflection learning quadratic interpolation

Übergeordnetes Werk:	In: Biomimetics - MDPI AG, 2017, 8(2023), 2, p 136
Übergeordnetes Werk:	volume:8 ; year:2023 ; number:2, p 136

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/biomimetics8020136

Katalog-ID:	DOAJ094201625

Internformat


LEADER	01000naa a22002652 4500
001	DOAJ094201625
003	DE-627
005	20240413031221.0
007	cr uuu---uuuuu
008	240413s2023 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.3390/biomimetics8020136 \|2 doi
035			\|a (DE-627)DOAJ094201625
035			\|a (DE-599)DOAJef35ccf00b2343e9b7a5251ea7c99b34
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
100	0		\|a Yufei Yang \|e verfasserin \|4 aut
245	1	2	\|a A Multi-Objective Carnivorous Plant Algorithm for Solving Constrained Multi-Objective Optimization Problems
264		1	\|c 2023
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Satisfying various constraints and multiple objectives simultaneously is a significant challenge in solving constrained multi-objective optimization problems. To address this issue, a new approach is proposed in this paper that combines multi-population and multi-stage methods with a Carnivorous Plant Algorithm. The algorithm employs the <inline-formula<<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"<<semantics<<mi<ϵ</mi<</semantics<</math<</inline-formula<-constraint handling method, with the <inline-formula<<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"<<semantics<<mi<ϵ</mi<</semantics<</math<</inline-formula< value adjusted according to different stages to meet the algorithm’s requirements. To improve the search efficiency, a cross-pollination is designed based on the trapping mechanism and pollination behavior of carnivorous plants, thus balancing the exploration and exploitation abilities and accelerating the convergence speed. Moreover, a quasi-reflection learning mechanism is introduced for the growth process of carnivorous plants, enhancing the optimization efficiency and improving its global convergence ability. Furthermore, the quadratic interpolation method is introduced for the reproduction process of carnivorous plants, which enables the algorithm to escape from local optima and enhances the optimization precision and convergence speed. The proposed algorithm’s performance is evaluated on several test suites, including DC-DTLZ, FCP, DASCMOP, ZDT, DTLZ, and RWMOPs. The experimental results indicate competitive performance of the proposed algorithm over the state-of-the-art constrained multi-objective optimization algorithms.
650		4	\|a carnivorous plant algorithm
650		4	\|a constrained multi-objective optimization
650		4	\|a cross-pollination
650		4	\|a quasi-reflection learning
650		4	\|a quadratic interpolation
653		0	\|a Technology
653		0	\|a T
700	0		\|a Changsheng Zhang \|e verfasserin \|4 aut
773	0	8	\|i In \|t Biomimetics \|d MDPI AG, 2017 \|g 8(2023), 2, p 136 \|w (DE-627)85960781X \|w (DE-600)2856245-8 \|x 23137673 \|7 nnns
773	1	8	\|g volume:8 \|g year:2023 \|g number:2, p 136
856	4	0	\|u https://doi.org/10.3390/biomimetics8020136 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/ef35ccf00b2343e9b7a5251ea7c99b34 \|z kostenfrei
856	4	0	\|u https://www.mdpi.com/2313-7673/8/2/136 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2313-7673 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
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_39
912			\|a GBV_ILN_40
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_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
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_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 8 \|j 2023 \|e 2, p 136

Indexfelder

author_variant	y y yy c z cz
matchkey_str	article:23137673:2023----::mlibetvcriooslnagrtmosligosriemlib
hierarchy_sort_str	2023
publishDate	2023
allfields	10.3390/biomimetics8020136 doi (DE-627)DOAJ094201625 (DE-599)DOAJef35ccf00b2343e9b7a5251ea7c99b34 DE-627 ger DE-627 rakwb eng Yufei Yang verfasserin aut A Multi-Objective Carnivorous Plant Algorithm for Solving Constrained Multi-Objective Optimization Problems 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Satisfying various constraints and multiple objectives simultaneously is a significant challenge in solving constrained multi-objective optimization problems. To address this issue, a new approach is proposed in this paper that combines multi-population and multi-stage methods with a Carnivorous Plant Algorithm. The algorithm employs the <inline-formula<<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"<<semantics<<mi<ϵ</mi<</semantics<</math<</inline-formula<-constraint handling method, with the <inline-formula<<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"<<semantics<<mi<ϵ</mi<</semantics<</math<</inline-formula< value adjusted according to different stages to meet the algorithm’s requirements. To improve the search efficiency, a cross-pollination is designed based on the trapping mechanism and pollination behavior of carnivorous plants, thus balancing the exploration and exploitation abilities and accelerating the convergence speed. Moreover, a quasi-reflection learning mechanism is introduced for the growth process of carnivorous plants, enhancing the optimization efficiency and improving its global convergence ability. Furthermore, the quadratic interpolation method is introduced for the reproduction process of carnivorous plants, which enables the algorithm to escape from local optima and enhances the optimization precision and convergence speed. The proposed algorithm’s performance is evaluated on several test suites, including DC-DTLZ, FCP, DASCMOP, ZDT, DTLZ, and RWMOPs. The experimental results indicate competitive performance of the proposed algorithm over the state-of-the-art constrained multi-objective optimization algorithms. carnivorous plant algorithm constrained multi-objective optimization cross-pollination quasi-reflection learning quadratic interpolation Technology T Changsheng Zhang verfasserin aut In Biomimetics MDPI AG, 2017 8(2023), 2, p 136 (DE-627)85960781X (DE-600)2856245-8 23137673 nnns volume:8 year:2023 number:2, p 136 https://doi.org/10.3390/biomimetics8020136 kostenfrei https://doaj.org/article/ef35ccf00b2343e9b7a5251ea7c99b34 kostenfrei https://www.mdpi.com/2313-7673/8/2/136 kostenfrei https://doaj.org/toc/2313-7673 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2023 2, p 136
spelling	10.3390/biomimetics8020136 doi (DE-627)DOAJ094201625 (DE-599)DOAJef35ccf00b2343e9b7a5251ea7c99b34 DE-627 ger DE-627 rakwb eng Yufei Yang verfasserin aut A Multi-Objective Carnivorous Plant Algorithm for Solving Constrained Multi-Objective Optimization Problems 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Satisfying various constraints and multiple objectives simultaneously is a significant challenge in solving constrained multi-objective optimization problems. To address this issue, a new approach is proposed in this paper that combines multi-population and multi-stage methods with a Carnivorous Plant Algorithm. The algorithm employs the <inline-formula<<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"<<semantics<<mi<ϵ</mi<</semantics<</math<</inline-formula<-constraint handling method, with the <inline-formula<<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"<<semantics<<mi<ϵ</mi<</semantics<</math<</inline-formula< value adjusted according to different stages to meet the algorithm’s requirements. To improve the search efficiency, a cross-pollination is designed based on the trapping mechanism and pollination behavior of carnivorous plants, thus balancing the exploration and exploitation abilities and accelerating the convergence speed. Moreover, a quasi-reflection learning mechanism is introduced for the growth process of carnivorous plants, enhancing the optimization efficiency and improving its global convergence ability. Furthermore, the quadratic interpolation method is introduced for the reproduction process of carnivorous plants, which enables the algorithm to escape from local optima and enhances the optimization precision and convergence speed. The proposed algorithm’s performance is evaluated on several test suites, including DC-DTLZ, FCP, DASCMOP, ZDT, DTLZ, and RWMOPs. The experimental results indicate competitive performance of the proposed algorithm over the state-of-the-art constrained multi-objective optimization algorithms. carnivorous plant algorithm constrained multi-objective optimization cross-pollination quasi-reflection learning quadratic interpolation Technology T Changsheng Zhang verfasserin aut In Biomimetics MDPI AG, 2017 8(2023), 2, p 136 (DE-627)85960781X (DE-600)2856245-8 23137673 nnns volume:8 year:2023 number:2, p 136 https://doi.org/10.3390/biomimetics8020136 kostenfrei https://doaj.org/article/ef35ccf00b2343e9b7a5251ea7c99b34 kostenfrei https://www.mdpi.com/2313-7673/8/2/136 kostenfrei https://doaj.org/toc/2313-7673 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2023 2, p 136
allfields_unstemmed	10.3390/biomimetics8020136 doi (DE-627)DOAJ094201625 (DE-599)DOAJef35ccf00b2343e9b7a5251ea7c99b34 DE-627 ger DE-627 rakwb eng Yufei Yang verfasserin aut A Multi-Objective Carnivorous Plant Algorithm for Solving Constrained Multi-Objective Optimization Problems 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Satisfying various constraints and multiple objectives simultaneously is a significant challenge in solving constrained multi-objective optimization problems. To address this issue, a new approach is proposed in this paper that combines multi-population and multi-stage methods with a Carnivorous Plant Algorithm. The algorithm employs the <inline-formula<<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"<<semantics<<mi<ϵ</mi<</semantics<</math<</inline-formula<-constraint handling method, with the <inline-formula<<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"<<semantics<<mi<ϵ</mi<</semantics<</math<</inline-formula< value adjusted according to different stages to meet the algorithm’s requirements. To improve the search efficiency, a cross-pollination is designed based on the trapping mechanism and pollination behavior of carnivorous plants, thus balancing the exploration and exploitation abilities and accelerating the convergence speed. Moreover, a quasi-reflection learning mechanism is introduced for the growth process of carnivorous plants, enhancing the optimization efficiency and improving its global convergence ability. Furthermore, the quadratic interpolation method is introduced for the reproduction process of carnivorous plants, which enables the algorithm to escape from local optima and enhances the optimization precision and convergence speed. The proposed algorithm’s performance is evaluated on several test suites, including DC-DTLZ, FCP, DASCMOP, ZDT, DTLZ, and RWMOPs. The experimental results indicate competitive performance of the proposed algorithm over the state-of-the-art constrained multi-objective optimization algorithms. carnivorous plant algorithm constrained multi-objective optimization cross-pollination quasi-reflection learning quadratic interpolation Technology T Changsheng Zhang verfasserin aut In Biomimetics MDPI AG, 2017 8(2023), 2, p 136 (DE-627)85960781X (DE-600)2856245-8 23137673 nnns volume:8 year:2023 number:2, p 136 https://doi.org/10.3390/biomimetics8020136 kostenfrei https://doaj.org/article/ef35ccf00b2343e9b7a5251ea7c99b34 kostenfrei https://www.mdpi.com/2313-7673/8/2/136 kostenfrei https://doaj.org/toc/2313-7673 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2023 2, p 136
allfieldsGer	10.3390/biomimetics8020136 doi (DE-627)DOAJ094201625 (DE-599)DOAJef35ccf00b2343e9b7a5251ea7c99b34 DE-627 ger DE-627 rakwb eng Yufei Yang verfasserin aut A Multi-Objective Carnivorous Plant Algorithm for Solving Constrained Multi-Objective Optimization Problems 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Satisfying various constraints and multiple objectives simultaneously is a significant challenge in solving constrained multi-objective optimization problems. To address this issue, a new approach is proposed in this paper that combines multi-population and multi-stage methods with a Carnivorous Plant Algorithm. The algorithm employs the <inline-formula<<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"<<semantics<<mi<ϵ</mi<</semantics<</math<</inline-formula<-constraint handling method, with the <inline-formula<<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"<<semantics<<mi<ϵ</mi<</semantics<</math<</inline-formula< value adjusted according to different stages to meet the algorithm’s requirements. To improve the search efficiency, a cross-pollination is designed based on the trapping mechanism and pollination behavior of carnivorous plants, thus balancing the exploration and exploitation abilities and accelerating the convergence speed. Moreover, a quasi-reflection learning mechanism is introduced for the growth process of carnivorous plants, enhancing the optimization efficiency and improving its global convergence ability. Furthermore, the quadratic interpolation method is introduced for the reproduction process of carnivorous plants, which enables the algorithm to escape from local optima and enhances the optimization precision and convergence speed. The proposed algorithm’s performance is evaluated on several test suites, including DC-DTLZ, FCP, DASCMOP, ZDT, DTLZ, and RWMOPs. The experimental results indicate competitive performance of the proposed algorithm over the state-of-the-art constrained multi-objective optimization algorithms. carnivorous plant algorithm constrained multi-objective optimization cross-pollination quasi-reflection learning quadratic interpolation Technology T Changsheng Zhang verfasserin aut In Biomimetics MDPI AG, 2017 8(2023), 2, p 136 (DE-627)85960781X (DE-600)2856245-8 23137673 nnns volume:8 year:2023 number:2, p 136 https://doi.org/10.3390/biomimetics8020136 kostenfrei https://doaj.org/article/ef35ccf00b2343e9b7a5251ea7c99b34 kostenfrei https://www.mdpi.com/2313-7673/8/2/136 kostenfrei https://doaj.org/toc/2313-7673 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2023 2, p 136
allfieldsSound	10.3390/biomimetics8020136 doi (DE-627)DOAJ094201625 (DE-599)DOAJef35ccf00b2343e9b7a5251ea7c99b34 DE-627 ger DE-627 rakwb eng Yufei Yang verfasserin aut A Multi-Objective Carnivorous Plant Algorithm for Solving Constrained Multi-Objective Optimization Problems 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Satisfying various constraints and multiple objectives simultaneously is a significant challenge in solving constrained multi-objective optimization problems. To address this issue, a new approach is proposed in this paper that combines multi-population and multi-stage methods with a Carnivorous Plant Algorithm. The algorithm employs the <inline-formula<<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"<<semantics<<mi<ϵ</mi<</semantics<</math<</inline-formula<-constraint handling method, with the <inline-formula<<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"<<semantics<<mi<ϵ</mi<</semantics<</math<</inline-formula< value adjusted according to different stages to meet the algorithm’s requirements. To improve the search efficiency, a cross-pollination is designed based on the trapping mechanism and pollination behavior of carnivorous plants, thus balancing the exploration and exploitation abilities and accelerating the convergence speed. Moreover, a quasi-reflection learning mechanism is introduced for the growth process of carnivorous plants, enhancing the optimization efficiency and improving its global convergence ability. Furthermore, the quadratic interpolation method is introduced for the reproduction process of carnivorous plants, which enables the algorithm to escape from local optima and enhances the optimization precision and convergence speed. The proposed algorithm’s performance is evaluated on several test suites, including DC-DTLZ, FCP, DASCMOP, ZDT, DTLZ, and RWMOPs. The experimental results indicate competitive performance of the proposed algorithm over the state-of-the-art constrained multi-objective optimization algorithms. carnivorous plant algorithm constrained multi-objective optimization cross-pollination quasi-reflection learning quadratic interpolation Technology T Changsheng Zhang verfasserin aut In Biomimetics MDPI AG, 2017 8(2023), 2, p 136 (DE-627)85960781X (DE-600)2856245-8 23137673 nnns volume:8 year:2023 number:2, p 136 https://doi.org/10.3390/biomimetics8020136 kostenfrei https://doaj.org/article/ef35ccf00b2343e9b7a5251ea7c99b34 kostenfrei https://www.mdpi.com/2313-7673/8/2/136 kostenfrei https://doaj.org/toc/2313-7673 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2023 2, p 136
language	English
source	In Biomimetics 8(2023), 2, p 136 volume:8 year:2023 number:2, p 136
sourceStr	In Biomimetics 8(2023), 2, p 136 volume:8 year:2023 number:2, p 136
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	carnivorous plant algorithm constrained multi-objective optimization cross-pollination quasi-reflection learning quadratic interpolation Technology T
isfreeaccess_bool	true
container_title	Biomimetics
authorswithroles_txt_mv	Yufei Yang @@aut@@ Changsheng Zhang @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	85960781X
id	DOAJ094201625
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ094201625</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413031221.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240413s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/biomimetics8020136</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ094201625</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJef35ccf00b2343e9b7a5251ea7c99b34</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Yufei Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A Multi-Objective Carnivorous Plant Algorithm for Solving Constrained Multi-Objective Optimization Problems</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Satisfying various constraints and multiple objectives simultaneously is a significant challenge in solving constrained multi-objective optimization problems. To address this issue, a new approach is proposed in this paper that combines multi-population and multi-stage methods with a Carnivorous Plant Algorithm. The algorithm employs the <inline-formula<<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"<<semantics<<mi<ϵ</mi<</semantics<</math<</inline-formula<-constraint handling method, with the <inline-formula<<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"<<semantics<<mi<ϵ</mi<</semantics<</math<</inline-formula< value adjusted according to different stages to meet the algorithm’s requirements. To improve the search efficiency, a cross-pollination is designed based on the trapping mechanism and pollination behavior of carnivorous plants, thus balancing the exploration and exploitation abilities and accelerating the convergence speed. Moreover, a quasi-reflection learning mechanism is introduced for the growth process of carnivorous plants, enhancing the optimization efficiency and improving its global convergence ability. Furthermore, the quadratic interpolation method is introduced for the reproduction process of carnivorous plants, which enables the algorithm to escape from local optima and enhances the optimization precision and convergence speed. The proposed algorithm’s performance is evaluated on several test suites, including DC-DTLZ, FCP, DASCMOP, ZDT, DTLZ, and RWMOPs. The experimental results indicate competitive performance of the proposed algorithm over the state-of-the-art constrained multi-objective optimization algorithms.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">carnivorous plant algorithm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">constrained multi-objective optimization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">cross-pollination</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">quasi-reflection learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">quadratic interpolation</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">T</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Changsheng Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Biomimetics</subfield><subfield code="d">MDPI AG, 2017</subfield><subfield code="g">8(2023), 2, p 136</subfield><subfield code="w">(DE-627)85960781X</subfield><subfield code="w">(DE-600)2856245-8</subfield><subfield code="x">23137673</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:8</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:2, p 136</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/biomimetics8020136</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/ef35ccf00b2343e9b7a5251ea7c99b34</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2313-7673/8/2/136</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2313-7673</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">8</subfield><subfield code="j">2023</subfield><subfield code="e">2, p 136</subfield></datafield></record></collection>
author	Yufei Yang
spellingShingle	Yufei Yang misc carnivorous plant algorithm misc constrained multi-objective optimization misc cross-pollination misc quasi-reflection learning misc quadratic interpolation misc Technology misc T A Multi-Objective Carnivorous Plant Algorithm for Solving Constrained Multi-Objective Optimization Problems
authorStr	Yufei Yang
ppnlink_with_tag_str_mv	@@773@@(DE-627)85960781X
format	electronic Article
delete_txt_mv	keep
author_role	aut aut
collection	DOAJ
remote_str	true
illustrated	Not Illustrated
issn	23137673
topic_title	A Multi-Objective Carnivorous Plant Algorithm for Solving Constrained Multi-Objective Optimization Problems carnivorous plant algorithm constrained multi-objective optimization cross-pollination quasi-reflection learning quadratic interpolation
topic	misc carnivorous plant algorithm misc constrained multi-objective optimization misc cross-pollination misc quasi-reflection learning misc quadratic interpolation misc Technology misc T
topic_unstemmed	misc carnivorous plant algorithm misc constrained multi-objective optimization misc cross-pollination misc quasi-reflection learning misc quadratic interpolation misc Technology misc T
topic_browse	misc carnivorous plant algorithm misc constrained multi-objective optimization misc cross-pollination misc quasi-reflection learning misc quadratic interpolation misc Technology misc T
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Biomimetics
hierarchy_parent_id	85960781X
hierarchy_top_title	Biomimetics
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)85960781X (DE-600)2856245-8
title	A Multi-Objective Carnivorous Plant Algorithm for Solving Constrained Multi-Objective Optimization Problems
ctrlnum	(DE-627)DOAJ094201625 (DE-599)DOAJef35ccf00b2343e9b7a5251ea7c99b34
title_full	A Multi-Objective Carnivorous Plant Algorithm for Solving Constrained Multi-Objective Optimization Problems
author_sort	Yufei Yang
journal	Biomimetics
journalStr	Biomimetics
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2023
contenttype_str_mv	txt
author_browse	Yufei Yang Changsheng Zhang
container_volume	8
format_se	Elektronische Aufsätze
author-letter	Yufei Yang
doi_str_mv	10.3390/biomimetics8020136
author2-role	verfasserin
title_sort	multi-objective carnivorous plant algorithm for solving constrained multi-objective optimization problems
title_auth	A Multi-Objective Carnivorous Plant Algorithm for Solving Constrained Multi-Objective Optimization Problems
abstract	Satisfying various constraints and multiple objectives simultaneously is a significant challenge in solving constrained multi-objective optimization problems. To address this issue, a new approach is proposed in this paper that combines multi-population and multi-stage methods with a Carnivorous Plant Algorithm. The algorithm employs the <inline-formula<<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"<<semantics<<mi<ϵ</mi<</semantics<</math<</inline-formula<-constraint handling method, with the <inline-formula<<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"<<semantics<<mi<ϵ</mi<</semantics<</math<</inline-formula< value adjusted according to different stages to meet the algorithm’s requirements. To improve the search efficiency, a cross-pollination is designed based on the trapping mechanism and pollination behavior of carnivorous plants, thus balancing the exploration and exploitation abilities and accelerating the convergence speed. Moreover, a quasi-reflection learning mechanism is introduced for the growth process of carnivorous plants, enhancing the optimization efficiency and improving its global convergence ability. Furthermore, the quadratic interpolation method is introduced for the reproduction process of carnivorous plants, which enables the algorithm to escape from local optima and enhances the optimization precision and convergence speed. The proposed algorithm’s performance is evaluated on several test suites, including DC-DTLZ, FCP, DASCMOP, ZDT, DTLZ, and RWMOPs. The experimental results indicate competitive performance of the proposed algorithm over the state-of-the-art constrained multi-objective optimization algorithms.
abstractGer	Satisfying various constraints and multiple objectives simultaneously is a significant challenge in solving constrained multi-objective optimization problems. To address this issue, a new approach is proposed in this paper that combines multi-population and multi-stage methods with a Carnivorous Plant Algorithm. The algorithm employs the <inline-formula<<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"<<semantics<<mi<ϵ</mi<</semantics<</math<</inline-formula<-constraint handling method, with the <inline-formula<<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"<<semantics<<mi<ϵ</mi<</semantics<</math<</inline-formula< value adjusted according to different stages to meet the algorithm’s requirements. To improve the search efficiency, a cross-pollination is designed based on the trapping mechanism and pollination behavior of carnivorous plants, thus balancing the exploration and exploitation abilities and accelerating the convergence speed. Moreover, a quasi-reflection learning mechanism is introduced for the growth process of carnivorous plants, enhancing the optimization efficiency and improving its global convergence ability. Furthermore, the quadratic interpolation method is introduced for the reproduction process of carnivorous plants, which enables the algorithm to escape from local optima and enhances the optimization precision and convergence speed. The proposed algorithm’s performance is evaluated on several test suites, including DC-DTLZ, FCP, DASCMOP, ZDT, DTLZ, and RWMOPs. The experimental results indicate competitive performance of the proposed algorithm over the state-of-the-art constrained multi-objective optimization algorithms.
abstract_unstemmed	Satisfying various constraints and multiple objectives simultaneously is a significant challenge in solving constrained multi-objective optimization problems. To address this issue, a new approach is proposed in this paper that combines multi-population and multi-stage methods with a Carnivorous Plant Algorithm. The algorithm employs the <inline-formula<<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"<<semantics<<mi<ϵ</mi<</semantics<</math<</inline-formula<-constraint handling method, with the <inline-formula<<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"<<semantics<<mi<ϵ</mi<</semantics<</math<</inline-formula< value adjusted according to different stages to meet the algorithm’s requirements. To improve the search efficiency, a cross-pollination is designed based on the trapping mechanism and pollination behavior of carnivorous plants, thus balancing the exploration and exploitation abilities and accelerating the convergence speed. Moreover, a quasi-reflection learning mechanism is introduced for the growth process of carnivorous plants, enhancing the optimization efficiency and improving its global convergence ability. Furthermore, the quadratic interpolation method is introduced for the reproduction process of carnivorous plants, which enables the algorithm to escape from local optima and enhances the optimization precision and convergence speed. The proposed algorithm’s performance is evaluated on several test suites, including DC-DTLZ, FCP, DASCMOP, ZDT, DTLZ, and RWMOPs. The experimental results indicate competitive performance of the proposed algorithm over the state-of-the-art constrained multi-objective optimization algorithms.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	2, p 136
title_short	A Multi-Objective Carnivorous Plant Algorithm for Solving Constrained Multi-Objective Optimization Problems
url	https://doi.org/10.3390/biomimetics8020136 https://doaj.org/article/ef35ccf00b2343e9b7a5251ea7c99b34 https://www.mdpi.com/2313-7673/8/2/136 https://doaj.org/toc/2313-7673
remote_bool	true
author2	Changsheng Zhang
author2Str	Changsheng Zhang
ppnlink	85960781X
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.3390/biomimetics8020136
up_date	2024-07-03T21:50:18.911Z
_version_	1803596248029069312
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ094201625</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413031221.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240413s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/biomimetics8020136</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ094201625</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJef35ccf00b2343e9b7a5251ea7c99b34</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Yufei Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A Multi-Objective Carnivorous Plant Algorithm for Solving Constrained Multi-Objective Optimization Problems</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Satisfying various constraints and multiple objectives simultaneously is a significant challenge in solving constrained multi-objective optimization problems. To address this issue, a new approach is proposed in this paper that combines multi-population and multi-stage methods with a Carnivorous Plant Algorithm. The algorithm employs the <inline-formula<<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"<<semantics<<mi<ϵ</mi<</semantics<</math<</inline-formula<-constraint handling method, with the <inline-formula<<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"<<semantics<<mi<ϵ</mi<</semantics<</math<</inline-formula< value adjusted according to different stages to meet the algorithm’s requirements. To improve the search efficiency, a cross-pollination is designed based on the trapping mechanism and pollination behavior of carnivorous plants, thus balancing the exploration and exploitation abilities and accelerating the convergence speed. Moreover, a quasi-reflection learning mechanism is introduced for the growth process of carnivorous plants, enhancing the optimization efficiency and improving its global convergence ability. Furthermore, the quadratic interpolation method is introduced for the reproduction process of carnivorous plants, which enables the algorithm to escape from local optima and enhances the optimization precision and convergence speed. The proposed algorithm’s performance is evaluated on several test suites, including DC-DTLZ, FCP, DASCMOP, ZDT, DTLZ, and RWMOPs. The experimental results indicate competitive performance of the proposed algorithm over the state-of-the-art constrained multi-objective optimization algorithms.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">carnivorous plant algorithm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">constrained multi-objective optimization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">cross-pollination</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">quasi-reflection learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">quadratic interpolation</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">T</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Changsheng Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Biomimetics</subfield><subfield code="d">MDPI AG, 2017</subfield><subfield code="g">8(2023), 2, p 136</subfield><subfield code="w">(DE-627)85960781X</subfield><subfield code="w">(DE-600)2856245-8</subfield><subfield code="x">23137673</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:8</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:2, p 136</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/biomimetics8020136</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/ef35ccf00b2343e9b7a5251ea7c99b34</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2313-7673/8/2/136</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2313-7673</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">8</subfield><subfield code="j">2023</subfield><subfield code="e">2, p 136</subfield></datafield></record></collection>
score	7.399581

Nicht das Richtige dabei?

Schreiben Sie uns!

A Multi-Objective Carnivorous Plant Algorithm for Solving Constrained Multi-Objective Optimization Problems

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?