An effective method for global optimization – Improved slime mould algorithm combine multiple strategies

The stochastic search algorithms are an important optimization technique used to solve complex global optimization problems. The Slime Mould Algorithm (SMA) is one of stochastic search algorithm inspired by the observed behaviors and morphological changes in the foraging process of slime moulds. SMA...
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Autor*in:	Wenqing Xiong [verfasserIn] Donglin Zhu [verfasserIn] Rui Li [verfasserIn] Yilin Yao [verfasserIn] Changjun Zhou [verfasserIn] Shi Cheng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Slime mould algorithm Global exploration Elite learning operator Differential mutation Engineering problems optimization

Übergeordnetes Werk:	In: Egyptian Informatics Journal - Elsevier, 2016, 25(2024), Seite 100442-
Übergeordnetes Werk:	volume:25 ; year:2024 ; pages:100442-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.eij.2024.100442

Katalog-ID:	DOAJ095782710

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520			\|a The stochastic search algorithms are an important optimization technique used to solve complex global optimization problems. The Slime Mould Algorithm (SMA) is one of stochastic search algorithm inspired by the observed behaviors and morphological changes in the foraging process of slime moulds. SMA has the advantage of having few parameters and a simple structure, making it applicable to various real-world optimization problems. However, it also has some drawbacks, such as high randomness during the search process and a tendency to converge to local optima, resulting in decreased accuracy. Therefore, we propose an effective method for global optimization - improved slime mould algorithm combine multiple strategy, called EISMA. In EISMA, we introduce a method of exploration that combines the average position of the population with Levy flights to enhance the algorithm's search capability in the previous phase. Then, a novel information-exchange hybrid elite learning operator is proposed to improve the guidance ability of the best search agent. Finally, a dual differential mutation search method that combines global and local optimization is introduced to maintain the diversity of the population by updating the search agents obtained in each iteration. These operations facilitate the algorithm's ability to escape local optima and ensure continuous optimization. To validate the applicability of EISMA, we numerically test it on 39 benchmark functions from CEC2013 and CEC2017 and compare its performance with SMA, as well as 7 modified, 5 standard and 4 classic stochastic search algorithms. Experimental results demonstrate that EISMA outperforms other versions in terms of optimization search performance. Furthermore, EISMA has achieved promising outcomes in testing problems related to path planning for three-dimensional unmanned aerial vehicles, pressure vessel design and robot gripper problem.
650		4	\|a Slime mould algorithm
650		4	\|a Global exploration
650		4	\|a Elite learning operator
650		4	\|a Differential mutation
650		4	\|a Engineering problems optimization
653		0	\|a Electronic computers. Computer science
700	0		\|a Donglin Zhu \|e verfasserin \|4 aut
700	0		\|a Rui Li \|e verfasserin \|4 aut
700	0		\|a Yilin Yao \|e verfasserin \|4 aut
700	0		\|a Changjun Zhou \|e verfasserin \|4 aut
700	0		\|a Shi Cheng \|e verfasserin \|4 aut
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allfields	10.1016/j.eij.2024.100442 doi (DE-627)DOAJ095782710 (DE-599)DOAJ42448865f5b240bd889d0b86bfcaa022 DE-627 ger DE-627 rakwb eng QA75.5-76.95 Wenqing Xiong verfasserin aut An effective method for global optimization – Improved slime mould algorithm combine multiple strategies 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The stochastic search algorithms are an important optimization technique used to solve complex global optimization problems. The Slime Mould Algorithm (SMA) is one of stochastic search algorithm inspired by the observed behaviors and morphological changes in the foraging process of slime moulds. SMA has the advantage of having few parameters and a simple structure, making it applicable to various real-world optimization problems. However, it also has some drawbacks, such as high randomness during the search process and a tendency to converge to local optima, resulting in decreased accuracy. Therefore, we propose an effective method for global optimization - improved slime mould algorithm combine multiple strategy, called EISMA. In EISMA, we introduce a method of exploration that combines the average position of the population with Levy flights to enhance the algorithm's search capability in the previous phase. Then, a novel information-exchange hybrid elite learning operator is proposed to improve the guidance ability of the best search agent. Finally, a dual differential mutation search method that combines global and local optimization is introduced to maintain the diversity of the population by updating the search agents obtained in each iteration. These operations facilitate the algorithm's ability to escape local optima and ensure continuous optimization. To validate the applicability of EISMA, we numerically test it on 39 benchmark functions from CEC2013 and CEC2017 and compare its performance with SMA, as well as 7 modified, 5 standard and 4 classic stochastic search algorithms. Experimental results demonstrate that EISMA outperforms other versions in terms of optimization search performance. Furthermore, EISMA has achieved promising outcomes in testing problems related to path planning for three-dimensional unmanned aerial vehicles, pressure vessel design and robot gripper problem. Slime mould algorithm Global exploration Elite learning operator Differential mutation Engineering problems optimization Electronic computers. Computer science Donglin Zhu verfasserin aut Rui Li verfasserin aut Yilin Yao verfasserin aut Changjun Zhou verfasserin aut Shi Cheng verfasserin aut In Egyptian Informatics Journal Elsevier, 2016 25(2024), Seite 100442- (DE-627)635604523 (DE-600)2573668-1 11108665 nnns volume:25 year:2024 pages:100442- https://doi.org/10.1016/j.eij.2024.100442 kostenfrei https://doaj.org/article/42448865f5b240bd889d0b86bfcaa022 kostenfrei http://www.sciencedirect.com/science/article/pii/S1110866524000057 kostenfrei https://doaj.org/toc/1110-8665 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 25 2024 100442-
spelling	10.1016/j.eij.2024.100442 doi (DE-627)DOAJ095782710 (DE-599)DOAJ42448865f5b240bd889d0b86bfcaa022 DE-627 ger DE-627 rakwb eng QA75.5-76.95 Wenqing Xiong verfasserin aut An effective method for global optimization – Improved slime mould algorithm combine multiple strategies 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The stochastic search algorithms are an important optimization technique used to solve complex global optimization problems. The Slime Mould Algorithm (SMA) is one of stochastic search algorithm inspired by the observed behaviors and morphological changes in the foraging process of slime moulds. SMA has the advantage of having few parameters and a simple structure, making it applicable to various real-world optimization problems. However, it also has some drawbacks, such as high randomness during the search process and a tendency to converge to local optima, resulting in decreased accuracy. Therefore, we propose an effective method for global optimization - improved slime mould algorithm combine multiple strategy, called EISMA. In EISMA, we introduce a method of exploration that combines the average position of the population with Levy flights to enhance the algorithm's search capability in the previous phase. Then, a novel information-exchange hybrid elite learning operator is proposed to improve the guidance ability of the best search agent. Finally, a dual differential mutation search method that combines global and local optimization is introduced to maintain the diversity of the population by updating the search agents obtained in each iteration. These operations facilitate the algorithm's ability to escape local optima and ensure continuous optimization. To validate the applicability of EISMA, we numerically test it on 39 benchmark functions from CEC2013 and CEC2017 and compare its performance with SMA, as well as 7 modified, 5 standard and 4 classic stochastic search algorithms. Experimental results demonstrate that EISMA outperforms other versions in terms of optimization search performance. Furthermore, EISMA has achieved promising outcomes in testing problems related to path planning for three-dimensional unmanned aerial vehicles, pressure vessel design and robot gripper problem. Slime mould algorithm Global exploration Elite learning operator Differential mutation Engineering problems optimization Electronic computers. Computer science Donglin Zhu verfasserin aut Rui Li verfasserin aut Yilin Yao verfasserin aut Changjun Zhou verfasserin aut Shi Cheng verfasserin aut In Egyptian Informatics Journal Elsevier, 2016 25(2024), Seite 100442- (DE-627)635604523 (DE-600)2573668-1 11108665 nnns volume:25 year:2024 pages:100442- https://doi.org/10.1016/j.eij.2024.100442 kostenfrei https://doaj.org/article/42448865f5b240bd889d0b86bfcaa022 kostenfrei http://www.sciencedirect.com/science/article/pii/S1110866524000057 kostenfrei https://doaj.org/toc/1110-8665 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 25 2024 100442-
allfields_unstemmed	10.1016/j.eij.2024.100442 doi (DE-627)DOAJ095782710 (DE-599)DOAJ42448865f5b240bd889d0b86bfcaa022 DE-627 ger DE-627 rakwb eng QA75.5-76.95 Wenqing Xiong verfasserin aut An effective method for global optimization – Improved slime mould algorithm combine multiple strategies 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The stochastic search algorithms are an important optimization technique used to solve complex global optimization problems. The Slime Mould Algorithm (SMA) is one of stochastic search algorithm inspired by the observed behaviors and morphological changes in the foraging process of slime moulds. SMA has the advantage of having few parameters and a simple structure, making it applicable to various real-world optimization problems. However, it also has some drawbacks, such as high randomness during the search process and a tendency to converge to local optima, resulting in decreased accuracy. Therefore, we propose an effective method for global optimization - improved slime mould algorithm combine multiple strategy, called EISMA. In EISMA, we introduce a method of exploration that combines the average position of the population with Levy flights to enhance the algorithm's search capability in the previous phase. Then, a novel information-exchange hybrid elite learning operator is proposed to improve the guidance ability of the best search agent. Finally, a dual differential mutation search method that combines global and local optimization is introduced to maintain the diversity of the population by updating the search agents obtained in each iteration. These operations facilitate the algorithm's ability to escape local optima and ensure continuous optimization. To validate the applicability of EISMA, we numerically test it on 39 benchmark functions from CEC2013 and CEC2017 and compare its performance with SMA, as well as 7 modified, 5 standard and 4 classic stochastic search algorithms. Experimental results demonstrate that EISMA outperforms other versions in terms of optimization search performance. Furthermore, EISMA has achieved promising outcomes in testing problems related to path planning for three-dimensional unmanned aerial vehicles, pressure vessel design and robot gripper problem. Slime mould algorithm Global exploration Elite learning operator Differential mutation Engineering problems optimization Electronic computers. Computer science Donglin Zhu verfasserin aut Rui Li verfasserin aut Yilin Yao verfasserin aut Changjun Zhou verfasserin aut Shi Cheng verfasserin aut In Egyptian Informatics Journal Elsevier, 2016 25(2024), Seite 100442- (DE-627)635604523 (DE-600)2573668-1 11108665 nnns volume:25 year:2024 pages:100442- https://doi.org/10.1016/j.eij.2024.100442 kostenfrei https://doaj.org/article/42448865f5b240bd889d0b86bfcaa022 kostenfrei http://www.sciencedirect.com/science/article/pii/S1110866524000057 kostenfrei https://doaj.org/toc/1110-8665 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 25 2024 100442-
allfieldsGer	10.1016/j.eij.2024.100442 doi (DE-627)DOAJ095782710 (DE-599)DOAJ42448865f5b240bd889d0b86bfcaa022 DE-627 ger DE-627 rakwb eng QA75.5-76.95 Wenqing Xiong verfasserin aut An effective method for global optimization – Improved slime mould algorithm combine multiple strategies 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The stochastic search algorithms are an important optimization technique used to solve complex global optimization problems. The Slime Mould Algorithm (SMA) is one of stochastic search algorithm inspired by the observed behaviors and morphological changes in the foraging process of slime moulds. SMA has the advantage of having few parameters and a simple structure, making it applicable to various real-world optimization problems. However, it also has some drawbacks, such as high randomness during the search process and a tendency to converge to local optima, resulting in decreased accuracy. Therefore, we propose an effective method for global optimization - improved slime mould algorithm combine multiple strategy, called EISMA. In EISMA, we introduce a method of exploration that combines the average position of the population with Levy flights to enhance the algorithm's search capability in the previous phase. Then, a novel information-exchange hybrid elite learning operator is proposed to improve the guidance ability of the best search agent. Finally, a dual differential mutation search method that combines global and local optimization is introduced to maintain the diversity of the population by updating the search agents obtained in each iteration. These operations facilitate the algorithm's ability to escape local optima and ensure continuous optimization. To validate the applicability of EISMA, we numerically test it on 39 benchmark functions from CEC2013 and CEC2017 and compare its performance with SMA, as well as 7 modified, 5 standard and 4 classic stochastic search algorithms. Experimental results demonstrate that EISMA outperforms other versions in terms of optimization search performance. Furthermore, EISMA has achieved promising outcomes in testing problems related to path planning for three-dimensional unmanned aerial vehicles, pressure vessel design and robot gripper problem. Slime mould algorithm Global exploration Elite learning operator Differential mutation Engineering problems optimization Electronic computers. Computer science Donglin Zhu verfasserin aut Rui Li verfasserin aut Yilin Yao verfasserin aut Changjun Zhou verfasserin aut Shi Cheng verfasserin aut In Egyptian Informatics Journal Elsevier, 2016 25(2024), Seite 100442- (DE-627)635604523 (DE-600)2573668-1 11108665 nnns volume:25 year:2024 pages:100442- https://doi.org/10.1016/j.eij.2024.100442 kostenfrei https://doaj.org/article/42448865f5b240bd889d0b86bfcaa022 kostenfrei http://www.sciencedirect.com/science/article/pii/S1110866524000057 kostenfrei https://doaj.org/toc/1110-8665 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 25 2024 100442-
allfieldsSound	10.1016/j.eij.2024.100442 doi (DE-627)DOAJ095782710 (DE-599)DOAJ42448865f5b240bd889d0b86bfcaa022 DE-627 ger DE-627 rakwb eng QA75.5-76.95 Wenqing Xiong verfasserin aut An effective method for global optimization – Improved slime mould algorithm combine multiple strategies 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The stochastic search algorithms are an important optimization technique used to solve complex global optimization problems. The Slime Mould Algorithm (SMA) is one of stochastic search algorithm inspired by the observed behaviors and morphological changes in the foraging process of slime moulds. SMA has the advantage of having few parameters and a simple structure, making it applicable to various real-world optimization problems. However, it also has some drawbacks, such as high randomness during the search process and a tendency to converge to local optima, resulting in decreased accuracy. Therefore, we propose an effective method for global optimization - improved slime mould algorithm combine multiple strategy, called EISMA. In EISMA, we introduce a method of exploration that combines the average position of the population with Levy flights to enhance the algorithm's search capability in the previous phase. Then, a novel information-exchange hybrid elite learning operator is proposed to improve the guidance ability of the best search agent. Finally, a dual differential mutation search method that combines global and local optimization is introduced to maintain the diversity of the population by updating the search agents obtained in each iteration. These operations facilitate the algorithm's ability to escape local optima and ensure continuous optimization. To validate the applicability of EISMA, we numerically test it on 39 benchmark functions from CEC2013 and CEC2017 and compare its performance with SMA, as well as 7 modified, 5 standard and 4 classic stochastic search algorithms. Experimental results demonstrate that EISMA outperforms other versions in terms of optimization search performance. Furthermore, EISMA has achieved promising outcomes in testing problems related to path planning for three-dimensional unmanned aerial vehicles, pressure vessel design and robot gripper problem. Slime mould algorithm Global exploration Elite learning operator Differential mutation Engineering problems optimization Electronic computers. Computer science Donglin Zhu verfasserin aut Rui Li verfasserin aut Yilin Yao verfasserin aut Changjun Zhou verfasserin aut Shi Cheng verfasserin aut In Egyptian Informatics Journal Elsevier, 2016 25(2024), Seite 100442- (DE-627)635604523 (DE-600)2573668-1 11108665 nnns volume:25 year:2024 pages:100442- https://doi.org/10.1016/j.eij.2024.100442 kostenfrei https://doaj.org/article/42448865f5b240bd889d0b86bfcaa022 kostenfrei http://www.sciencedirect.com/science/article/pii/S1110866524000057 kostenfrei https://doaj.org/toc/1110-8665 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 25 2024 100442-
language	English
source	In Egyptian Informatics Journal 25(2024), Seite 100442- volume:25 year:2024 pages:100442-
sourceStr	In Egyptian Informatics Journal 25(2024), Seite 100442- volume:25 year:2024 pages:100442-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Slime mould algorithm Global exploration Elite learning operator Differential mutation Engineering problems optimization Electronic computers. Computer science
isfreeaccess_bool	true
container_title	Egyptian Informatics Journal
authorswithroles_txt_mv	Wenqing Xiong @@aut@@ Donglin Zhu @@aut@@ Rui Li @@aut@@ Yilin Yao @@aut@@ Changjun Zhou @@aut@@ Shi Cheng @@aut@@
publishDateDaySort_date	2024-01-01T00:00:00Z
hierarchy_top_id	635604523
id	DOAJ095782710
language_de	englisch
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callnumber-first	Q - Science
author	Wenqing Xiong
spellingShingle	Wenqing Xiong misc QA75.5-76.95 misc Slime mould algorithm misc Global exploration misc Elite learning operator misc Differential mutation misc Engineering problems optimization misc Electronic computers. Computer science An effective method for global optimization – Improved slime mould algorithm combine multiple strategies
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topic_title	QA75.5-76.95 An effective method for global optimization – Improved slime mould algorithm combine multiple strategies Slime mould algorithm Global exploration Elite learning operator Differential mutation Engineering problems optimization
topic	misc QA75.5-76.95 misc Slime mould algorithm misc Global exploration misc Elite learning operator misc Differential mutation misc Engineering problems optimization misc Electronic computers. Computer science
topic_unstemmed	misc QA75.5-76.95 misc Slime mould algorithm misc Global exploration misc Elite learning operator misc Differential mutation misc Engineering problems optimization misc Electronic computers. Computer science
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title	An effective method for global optimization – Improved slime mould algorithm combine multiple strategies
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title_full	An effective method for global optimization – Improved slime mould algorithm combine multiple strategies
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title_sort	effective method for global optimization – improved slime mould algorithm combine multiple strategies
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title_auth	An effective method for global optimization – Improved slime mould algorithm combine multiple strategies
abstract	The stochastic search algorithms are an important optimization technique used to solve complex global optimization problems. The Slime Mould Algorithm (SMA) is one of stochastic search algorithm inspired by the observed behaviors and morphological changes in the foraging process of slime moulds. SMA has the advantage of having few parameters and a simple structure, making it applicable to various real-world optimization problems. However, it also has some drawbacks, such as high randomness during the search process and a tendency to converge to local optima, resulting in decreased accuracy. Therefore, we propose an effective method for global optimization - improved slime mould algorithm combine multiple strategy, called EISMA. In EISMA, we introduce a method of exploration that combines the average position of the population with Levy flights to enhance the algorithm's search capability in the previous phase. Then, a novel information-exchange hybrid elite learning operator is proposed to improve the guidance ability of the best search agent. Finally, a dual differential mutation search method that combines global and local optimization is introduced to maintain the diversity of the population by updating the search agents obtained in each iteration. These operations facilitate the algorithm's ability to escape local optima and ensure continuous optimization. To validate the applicability of EISMA, we numerically test it on 39 benchmark functions from CEC2013 and CEC2017 and compare its performance with SMA, as well as 7 modified, 5 standard and 4 classic stochastic search algorithms. Experimental results demonstrate that EISMA outperforms other versions in terms of optimization search performance. Furthermore, EISMA has achieved promising outcomes in testing problems related to path planning for three-dimensional unmanned aerial vehicles, pressure vessel design and robot gripper problem.
abstractGer	The stochastic search algorithms are an important optimization technique used to solve complex global optimization problems. The Slime Mould Algorithm (SMA) is one of stochastic search algorithm inspired by the observed behaviors and morphological changes in the foraging process of slime moulds. SMA has the advantage of having few parameters and a simple structure, making it applicable to various real-world optimization problems. However, it also has some drawbacks, such as high randomness during the search process and a tendency to converge to local optima, resulting in decreased accuracy. Therefore, we propose an effective method for global optimization - improved slime mould algorithm combine multiple strategy, called EISMA. In EISMA, we introduce a method of exploration that combines the average position of the population with Levy flights to enhance the algorithm's search capability in the previous phase. Then, a novel information-exchange hybrid elite learning operator is proposed to improve the guidance ability of the best search agent. Finally, a dual differential mutation search method that combines global and local optimization is introduced to maintain the diversity of the population by updating the search agents obtained in each iteration. These operations facilitate the algorithm's ability to escape local optima and ensure continuous optimization. To validate the applicability of EISMA, we numerically test it on 39 benchmark functions from CEC2013 and CEC2017 and compare its performance with SMA, as well as 7 modified, 5 standard and 4 classic stochastic search algorithms. Experimental results demonstrate that EISMA outperforms other versions in terms of optimization search performance. Furthermore, EISMA has achieved promising outcomes in testing problems related to path planning for three-dimensional unmanned aerial vehicles, pressure vessel design and robot gripper problem.
abstract_unstemmed	The stochastic search algorithms are an important optimization technique used to solve complex global optimization problems. The Slime Mould Algorithm (SMA) is one of stochastic search algorithm inspired by the observed behaviors and morphological changes in the foraging process of slime moulds. SMA has the advantage of having few parameters and a simple structure, making it applicable to various real-world optimization problems. However, it also has some drawbacks, such as high randomness during the search process and a tendency to converge to local optima, resulting in decreased accuracy. Therefore, we propose an effective method for global optimization - improved slime mould algorithm combine multiple strategy, called EISMA. In EISMA, we introduce a method of exploration that combines the average position of the population with Levy flights to enhance the algorithm's search capability in the previous phase. Then, a novel information-exchange hybrid elite learning operator is proposed to improve the guidance ability of the best search agent. Finally, a dual differential mutation search method that combines global and local optimization is introduced to maintain the diversity of the population by updating the search agents obtained in each iteration. These operations facilitate the algorithm's ability to escape local optima and ensure continuous optimization. To validate the applicability of EISMA, we numerically test it on 39 benchmark functions from CEC2013 and CEC2017 and compare its performance with SMA, as well as 7 modified, 5 standard and 4 classic stochastic search algorithms. Experimental results demonstrate that EISMA outperforms other versions in terms of optimization search performance. Furthermore, EISMA has achieved promising outcomes in testing problems related to path planning for three-dimensional unmanned aerial vehicles, pressure vessel design and robot gripper problem.
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title_short	An effective method for global optimization – Improved slime mould algorithm combine multiple strategies
url	https://doi.org/10.1016/j.eij.2024.100442 https://doaj.org/article/42448865f5b240bd889d0b86bfcaa022 http://www.sciencedirect.com/science/article/pii/S1110866524000057 https://doaj.org/toc/1110-8665
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up_date	2024-07-03T16:35:39.218Z
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The Slime Mould Algorithm (SMA) is one of stochastic search algorithm inspired by the observed behaviors and morphological changes in the foraging process of slime moulds. SMA has the advantage of having few parameters and a simple structure, making it applicable to various real-world optimization problems. However, it also has some drawbacks, such as high randomness during the search process and a tendency to converge to local optima, resulting in decreased accuracy. Therefore, we propose an effective method for global optimization - improved slime mould algorithm combine multiple strategy, called EISMA. In EISMA, we introduce a method of exploration that combines the average position of the population with Levy flights to enhance the algorithm's search capability in the previous phase. Then, a novel information-exchange hybrid elite learning operator is proposed to improve the guidance ability of the best search agent. Finally, a dual differential mutation search method that combines global and local optimization is introduced to maintain the diversity of the population by updating the search agents obtained in each iteration. These operations facilitate the algorithm's ability to escape local optima and ensure continuous optimization. To validate the applicability of EISMA, we numerically test it on 39 benchmark functions from CEC2013 and CEC2017 and compare its performance with SMA, as well as 7 modified, 5 standard and 4 classic stochastic search algorithms. Experimental results demonstrate that EISMA outperforms other versions in terms of optimization search performance. 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