Cat hunting optimization algorithm: a novel optimization algorithm

Abstract This article introduces a new version of cat behavior in hunting. Cat Hunting Optimization (CHO) algorithm Unlike cat optimization algorithms, search strategy is not limited to tracking and attacking. The CHO algorithm has various behaviors such as changing the nature of the search from glo...
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Autor*in:	Ghaedi, Azam [verfasserIn] Bardsiri, Amid Khatibi Shahbazzadeh, Mehdi Jafari

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Optimization problems Metaheuristic algorithms Swarm intelligence Cat hunting optimization (CHO) algorithm

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: Evolutionary intelligence - Berlin : Springer, 2008, 16(2021), 2 vom: 22. Sept., Seite 417-438
Übergeordnetes Werk:	volume:16 ; year:2021 ; number:2 ; day:22 ; month:09 ; pages:417-438

Links:	Volltext

DOI / URN:	10.1007/s12065-021-00668-w

Katalog-ID:	SPR049788639

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520			\|a Abstract This article introduces a new version of cat behavior in hunting. Cat Hunting Optimization (CHO) algorithm Unlike cat optimization algorithms, search strategy is not limited to tracking and attacking. The CHO algorithm has various behaviors such as changing the nature of the search from global to local, creating the number of positions based on the merit of a point for the cat to roam, searching between the optimal space and the current solution, search strategy at the gravity point of the solution and so on. The advantage of the proposed algorithm over the standard cat algorithm is that the answers do not attack the prey quickly, and this provides a chance to find more efficient solutions in the global search. In the proposed method for intelligent search, the problem space of the search radius around the optimal solutions changes in a new way based on trigonometric relations such as cosine. The CHO algorithm considers a kind of individual learning between cats, and experienced cats inspire young cats to identify optimal solutions. Experiments on a set of standard benchmark functions show that in most of the functions the optimal calculation error of the CHO is less than SHO, GOA, GWO, FA, DE, PSO, COA algorithms. Implement on the Experiments show that in the most exam the CHO has the first rank in terms of the accuracy of the global optimal calculation in the Frieden test. The CHO algorithm offers less cost in solving engineering optimization problems than WOA, LDF, and HHO algorithms.
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allfields	10.1007/s12065-021-00668-w doi (DE-627)SPR049788639 (SPR)s12065-021-00668-w-e DE-627 ger DE-627 rakwb eng Ghaedi, Azam verfasserin (orcid)0000-0003-1408-5941 aut Cat hunting optimization algorithm: a novel optimization algorithm 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract This article introduces a new version of cat behavior in hunting. Cat Hunting Optimization (CHO) algorithm Unlike cat optimization algorithms, search strategy is not limited to tracking and attacking. The CHO algorithm has various behaviors such as changing the nature of the search from global to local, creating the number of positions based on the merit of a point for the cat to roam, searching between the optimal space and the current solution, search strategy at the gravity point of the solution and so on. The advantage of the proposed algorithm over the standard cat algorithm is that the answers do not attack the prey quickly, and this provides a chance to find more efficient solutions in the global search. In the proposed method for intelligent search, the problem space of the search radius around the optimal solutions changes in a new way based on trigonometric relations such as cosine. The CHO algorithm considers a kind of individual learning between cats, and experienced cats inspire young cats to identify optimal solutions. Experiments on a set of standard benchmark functions show that in most of the functions the optimal calculation error of the CHO is less than SHO, GOA, GWO, FA, DE, PSO, COA algorithms. Implement on the Experiments show that in the most exam the CHO has the first rank in terms of the accuracy of the global optimal calculation in the Frieden test. The CHO algorithm offers less cost in solving engineering optimization problems than WOA, LDF, and HHO algorithms. Optimization problems (dpeaa)DE-He213 Metaheuristic algorithms (dpeaa)DE-He213 Swarm intelligence (dpeaa)DE-He213 Cat hunting optimization (CHO) algorithm (dpeaa)DE-He213 Bardsiri, Amid Khatibi aut Shahbazzadeh, Mehdi Jafari aut Enthalten in Evolutionary intelligence Berlin : Springer, 2008 16(2021), 2 vom: 22. Sept., Seite 417-438 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:16 year:2021 number:2 day:22 month:09 pages:417-438 https://dx.doi.org/10.1007/s12065-021-00668-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2021 2 22 09 417-438
spelling	10.1007/s12065-021-00668-w doi (DE-627)SPR049788639 (SPR)s12065-021-00668-w-e DE-627 ger DE-627 rakwb eng Ghaedi, Azam verfasserin (orcid)0000-0003-1408-5941 aut Cat hunting optimization algorithm: a novel optimization algorithm 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract This article introduces a new version of cat behavior in hunting. Cat Hunting Optimization (CHO) algorithm Unlike cat optimization algorithms, search strategy is not limited to tracking and attacking. The CHO algorithm has various behaviors such as changing the nature of the search from global to local, creating the number of positions based on the merit of a point for the cat to roam, searching between the optimal space and the current solution, search strategy at the gravity point of the solution and so on. The advantage of the proposed algorithm over the standard cat algorithm is that the answers do not attack the prey quickly, and this provides a chance to find more efficient solutions in the global search. In the proposed method for intelligent search, the problem space of the search radius around the optimal solutions changes in a new way based on trigonometric relations such as cosine. The CHO algorithm considers a kind of individual learning between cats, and experienced cats inspire young cats to identify optimal solutions. Experiments on a set of standard benchmark functions show that in most of the functions the optimal calculation error of the CHO is less than SHO, GOA, GWO, FA, DE, PSO, COA algorithms. Implement on the Experiments show that in the most exam the CHO has the first rank in terms of the accuracy of the global optimal calculation in the Frieden test. The CHO algorithm offers less cost in solving engineering optimization problems than WOA, LDF, and HHO algorithms. Optimization problems (dpeaa)DE-He213 Metaheuristic algorithms (dpeaa)DE-He213 Swarm intelligence (dpeaa)DE-He213 Cat hunting optimization (CHO) algorithm (dpeaa)DE-He213 Bardsiri, Amid Khatibi aut Shahbazzadeh, Mehdi Jafari aut Enthalten in Evolutionary intelligence Berlin : Springer, 2008 16(2021), 2 vom: 22. Sept., Seite 417-438 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:16 year:2021 number:2 day:22 month:09 pages:417-438 https://dx.doi.org/10.1007/s12065-021-00668-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2021 2 22 09 417-438
allfields_unstemmed	10.1007/s12065-021-00668-w doi (DE-627)SPR049788639 (SPR)s12065-021-00668-w-e DE-627 ger DE-627 rakwb eng Ghaedi, Azam verfasserin (orcid)0000-0003-1408-5941 aut Cat hunting optimization algorithm: a novel optimization algorithm 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract This article introduces a new version of cat behavior in hunting. Cat Hunting Optimization (CHO) algorithm Unlike cat optimization algorithms, search strategy is not limited to tracking and attacking. The CHO algorithm has various behaviors such as changing the nature of the search from global to local, creating the number of positions based on the merit of a point for the cat to roam, searching between the optimal space and the current solution, search strategy at the gravity point of the solution and so on. The advantage of the proposed algorithm over the standard cat algorithm is that the answers do not attack the prey quickly, and this provides a chance to find more efficient solutions in the global search. In the proposed method for intelligent search, the problem space of the search radius around the optimal solutions changes in a new way based on trigonometric relations such as cosine. The CHO algorithm considers a kind of individual learning between cats, and experienced cats inspire young cats to identify optimal solutions. Experiments on a set of standard benchmark functions show that in most of the functions the optimal calculation error of the CHO is less than SHO, GOA, GWO, FA, DE, PSO, COA algorithms. Implement on the Experiments show that in the most exam the CHO has the first rank in terms of the accuracy of the global optimal calculation in the Frieden test. The CHO algorithm offers less cost in solving engineering optimization problems than WOA, LDF, and HHO algorithms. Optimization problems (dpeaa)DE-He213 Metaheuristic algorithms (dpeaa)DE-He213 Swarm intelligence (dpeaa)DE-He213 Cat hunting optimization (CHO) algorithm (dpeaa)DE-He213 Bardsiri, Amid Khatibi aut Shahbazzadeh, Mehdi Jafari aut Enthalten in Evolutionary intelligence Berlin : Springer, 2008 16(2021), 2 vom: 22. Sept., Seite 417-438 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:16 year:2021 number:2 day:22 month:09 pages:417-438 https://dx.doi.org/10.1007/s12065-021-00668-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2021 2 22 09 417-438
allfieldsGer	10.1007/s12065-021-00668-w doi (DE-627)SPR049788639 (SPR)s12065-021-00668-w-e DE-627 ger DE-627 rakwb eng Ghaedi, Azam verfasserin (orcid)0000-0003-1408-5941 aut Cat hunting optimization algorithm: a novel optimization algorithm 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract This article introduces a new version of cat behavior in hunting. Cat Hunting Optimization (CHO) algorithm Unlike cat optimization algorithms, search strategy is not limited to tracking and attacking. The CHO algorithm has various behaviors such as changing the nature of the search from global to local, creating the number of positions based on the merit of a point for the cat to roam, searching between the optimal space and the current solution, search strategy at the gravity point of the solution and so on. The advantage of the proposed algorithm over the standard cat algorithm is that the answers do not attack the prey quickly, and this provides a chance to find more efficient solutions in the global search. In the proposed method for intelligent search, the problem space of the search radius around the optimal solutions changes in a new way based on trigonometric relations such as cosine. The CHO algorithm considers a kind of individual learning between cats, and experienced cats inspire young cats to identify optimal solutions. Experiments on a set of standard benchmark functions show that in most of the functions the optimal calculation error of the CHO is less than SHO, GOA, GWO, FA, DE, PSO, COA algorithms. Implement on the Experiments show that in the most exam the CHO has the first rank in terms of the accuracy of the global optimal calculation in the Frieden test. The CHO algorithm offers less cost in solving engineering optimization problems than WOA, LDF, and HHO algorithms. Optimization problems (dpeaa)DE-He213 Metaheuristic algorithms (dpeaa)DE-He213 Swarm intelligence (dpeaa)DE-He213 Cat hunting optimization (CHO) algorithm (dpeaa)DE-He213 Bardsiri, Amid Khatibi aut Shahbazzadeh, Mehdi Jafari aut Enthalten in Evolutionary intelligence Berlin : Springer, 2008 16(2021), 2 vom: 22. Sept., Seite 417-438 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:16 year:2021 number:2 day:22 month:09 pages:417-438 https://dx.doi.org/10.1007/s12065-021-00668-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2021 2 22 09 417-438
allfieldsSound	10.1007/s12065-021-00668-w doi (DE-627)SPR049788639 (SPR)s12065-021-00668-w-e DE-627 ger DE-627 rakwb eng Ghaedi, Azam verfasserin (orcid)0000-0003-1408-5941 aut Cat hunting optimization algorithm: a novel optimization algorithm 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract This article introduces a new version of cat behavior in hunting. Cat Hunting Optimization (CHO) algorithm Unlike cat optimization algorithms, search strategy is not limited to tracking and attacking. The CHO algorithm has various behaviors such as changing the nature of the search from global to local, creating the number of positions based on the merit of a point for the cat to roam, searching between the optimal space and the current solution, search strategy at the gravity point of the solution and so on. The advantage of the proposed algorithm over the standard cat algorithm is that the answers do not attack the prey quickly, and this provides a chance to find more efficient solutions in the global search. In the proposed method for intelligent search, the problem space of the search radius around the optimal solutions changes in a new way based on trigonometric relations such as cosine. The CHO algorithm considers a kind of individual learning between cats, and experienced cats inspire young cats to identify optimal solutions. Experiments on a set of standard benchmark functions show that in most of the functions the optimal calculation error of the CHO is less than SHO, GOA, GWO, FA, DE, PSO, COA algorithms. Implement on the Experiments show that in the most exam the CHO has the first rank in terms of the accuracy of the global optimal calculation in the Frieden test. The CHO algorithm offers less cost in solving engineering optimization problems than WOA, LDF, and HHO algorithms. Optimization problems (dpeaa)DE-He213 Metaheuristic algorithms (dpeaa)DE-He213 Swarm intelligence (dpeaa)DE-He213 Cat hunting optimization (CHO) algorithm (dpeaa)DE-He213 Bardsiri, Amid Khatibi aut Shahbazzadeh, Mehdi Jafari aut Enthalten in Evolutionary intelligence Berlin : Springer, 2008 16(2021), 2 vom: 22. Sept., Seite 417-438 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:16 year:2021 number:2 day:22 month:09 pages:417-438 https://dx.doi.org/10.1007/s12065-021-00668-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2021 2 22 09 417-438
language	English
source	Enthalten in Evolutionary intelligence 16(2021), 2 vom: 22. Sept., Seite 417-438 volume:16 year:2021 number:2 day:22 month:09 pages:417-438
sourceStr	Enthalten in Evolutionary intelligence 16(2021), 2 vom: 22. Sept., Seite 417-438 volume:16 year:2021 number:2 day:22 month:09 pages:417-438
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Optimization problems Metaheuristic algorithms Swarm intelligence Cat hunting optimization (CHO) algorithm
isfreeaccess_bool	false
container_title	Evolutionary intelligence
authorswithroles_txt_mv	Ghaedi, Azam @@aut@@ Bardsiri, Amid Khatibi @@aut@@ Shahbazzadeh, Mehdi Jafari @@aut@@
publishDateDaySort_date	2021-09-22T00:00:00Z
hierarchy_top_id	566007215
id	SPR049788639
language_de	englisch
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author	Ghaedi, Azam
spellingShingle	Ghaedi, Azam misc Optimization problems misc Metaheuristic algorithms misc Swarm intelligence misc Cat hunting optimization (CHO) algorithm Cat hunting optimization algorithm: a novel optimization algorithm
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topic_title	Cat hunting optimization algorithm: a novel optimization algorithm Optimization problems (dpeaa)DE-He213 Metaheuristic algorithms (dpeaa)DE-He213 Swarm intelligence (dpeaa)DE-He213 Cat hunting optimization (CHO) algorithm (dpeaa)DE-He213
topic	misc Optimization problems misc Metaheuristic algorithms misc Swarm intelligence misc Cat hunting optimization (CHO) algorithm
topic_unstemmed	misc Optimization problems misc Metaheuristic algorithms misc Swarm intelligence misc Cat hunting optimization (CHO) algorithm
topic_browse	misc Optimization problems misc Metaheuristic algorithms misc Swarm intelligence misc Cat hunting optimization (CHO) algorithm
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title	Cat hunting optimization algorithm: a novel optimization algorithm
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title_full	Cat hunting optimization algorithm: a novel optimization algorithm
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author_browse	Ghaedi, Azam Bardsiri, Amid Khatibi Shahbazzadeh, Mehdi Jafari
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title_sort	cat hunting optimization algorithm: a novel optimization algorithm
title_auth	Cat hunting optimization algorithm: a novel optimization algorithm
abstract	Abstract This article introduces a new version of cat behavior in hunting. Cat Hunting Optimization (CHO) algorithm Unlike cat optimization algorithms, search strategy is not limited to tracking and attacking. The CHO algorithm has various behaviors such as changing the nature of the search from global to local, creating the number of positions based on the merit of a point for the cat to roam, searching between the optimal space and the current solution, search strategy at the gravity point of the solution and so on. The advantage of the proposed algorithm over the standard cat algorithm is that the answers do not attack the prey quickly, and this provides a chance to find more efficient solutions in the global search. In the proposed method for intelligent search, the problem space of the search radius around the optimal solutions changes in a new way based on trigonometric relations such as cosine. The CHO algorithm considers a kind of individual learning between cats, and experienced cats inspire young cats to identify optimal solutions. Experiments on a set of standard benchmark functions show that in most of the functions the optimal calculation error of the CHO is less than SHO, GOA, GWO, FA, DE, PSO, COA algorithms. Implement on the Experiments show that in the most exam the CHO has the first rank in terms of the accuracy of the global optimal calculation in the Frieden test. The CHO algorithm offers less cost in solving engineering optimization problems than WOA, LDF, and HHO algorithms. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
abstractGer	Abstract This article introduces a new version of cat behavior in hunting. Cat Hunting Optimization (CHO) algorithm Unlike cat optimization algorithms, search strategy is not limited to tracking and attacking. The CHO algorithm has various behaviors such as changing the nature of the search from global to local, creating the number of positions based on the merit of a point for the cat to roam, searching between the optimal space and the current solution, search strategy at the gravity point of the solution and so on. The advantage of the proposed algorithm over the standard cat algorithm is that the answers do not attack the prey quickly, and this provides a chance to find more efficient solutions in the global search. In the proposed method for intelligent search, the problem space of the search radius around the optimal solutions changes in a new way based on trigonometric relations such as cosine. The CHO algorithm considers a kind of individual learning between cats, and experienced cats inspire young cats to identify optimal solutions. Experiments on a set of standard benchmark functions show that in most of the functions the optimal calculation error of the CHO is less than SHO, GOA, GWO, FA, DE, PSO, COA algorithms. Implement on the Experiments show that in the most exam the CHO has the first rank in terms of the accuracy of the global optimal calculation in the Frieden test. The CHO algorithm offers less cost in solving engineering optimization problems than WOA, LDF, and HHO algorithms. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
abstract_unstemmed	Abstract This article introduces a new version of cat behavior in hunting. Cat Hunting Optimization (CHO) algorithm Unlike cat optimization algorithms, search strategy is not limited to tracking and attacking. The CHO algorithm has various behaviors such as changing the nature of the search from global to local, creating the number of positions based on the merit of a point for the cat to roam, searching between the optimal space and the current solution, search strategy at the gravity point of the solution and so on. The advantage of the proposed algorithm over the standard cat algorithm is that the answers do not attack the prey quickly, and this provides a chance to find more efficient solutions in the global search. In the proposed method for intelligent search, the problem space of the search radius around the optimal solutions changes in a new way based on trigonometric relations such as cosine. The CHO algorithm considers a kind of individual learning between cats, and experienced cats inspire young cats to identify optimal solutions. Experiments on a set of standard benchmark functions show that in most of the functions the optimal calculation error of the CHO is less than SHO, GOA, GWO, FA, DE, PSO, COA algorithms. Implement on the Experiments show that in the most exam the CHO has the first rank in terms of the accuracy of the global optimal calculation in the Frieden test. The CHO algorithm offers less cost in solving engineering optimization problems than WOA, LDF, and HHO algorithms. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
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title_short	Cat hunting optimization algorithm: a novel optimization algorithm
url	https://dx.doi.org/10.1007/s12065-021-00668-w
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author2	Bardsiri, Amid Khatibi Shahbazzadeh, Mehdi Jafari
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Cat Hunting Optimization (CHO) algorithm Unlike cat optimization algorithms, search strategy is not limited to tracking and attacking. The CHO algorithm has various behaviors such as changing the nature of the search from global to local, creating the number of positions based on the merit of a point for the cat to roam, searching between the optimal space and the current solution, search strategy at the gravity point of the solution and so on. The advantage of the proposed algorithm over the standard cat algorithm is that the answers do not attack the prey quickly, and this provides a chance to find more efficient solutions in the global search. In the proposed method for intelligent search, the problem space of the search radius around the optimal solutions changes in a new way based on trigonometric relations such as cosine. The CHO algorithm considers a kind of individual learning between cats, and experienced cats inspire young cats to identify optimal solutions. Experiments on a set of standard benchmark functions show that in most of the functions the optimal calculation error of the CHO is less than SHO, GOA, GWO, FA, DE, PSO, COA algorithms. Implement on the Experiments show that in the most exam the CHO has the first rank in terms of the accuracy of the global optimal calculation in the Frieden test. The CHO algorithm offers less cost in solving engineering optimization problems than WOA, LDF, and HHO algorithms.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Optimization problems</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Metaheuristic algorithms</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Swarm intelligence</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cat hunting optimization (CHO) algorithm</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bardsiri, Amid Khatibi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shahbazzadeh, Mehdi Jafari</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Evolutionary intelligence</subfield><subfield code="d">Berlin : Springer, 2008</subfield><subfield code="g">16(2021), 2 vom: 22. 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