Exposing the grey wolf, moth-flame, whale, firefly, bat, and antlion algorithms : six misleading optimization techniques inspired by bestial metaphors
Gespeichert in:
| Autor*in: |
Camacho-Villalón, Christian L. [verfasserIn] Dorigo, Marco - 1961- [verfasserIn] Stützle, Thomas G. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: International transactions in operational research - Oxford : Wiley-Blackwell, 1994, 30(2023), 6 vom: Nov., Seite 2945-2971 |
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| Übergeordnetes Werk: |
volume:30 ; year:2023 ; number:6 ; month:11 ; pages:2945-2971 |
| Links: |
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| DOI / URN: |
10.1111/itor.13176 |
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| 982 | |2 26 |1 00 |x DE-206 |b We present a rigorous, component-based analysis of six widespread metaphor-based algorithms for tackling continuous optimization problems. In addition to deconstructing the six algorithms into their components and relating them with equivalent components proposed in well-established techniques, such as particle swarm optimization and evolutionary algorithms, we analyze the use of the metaphors that inspired these algorithms to understand whether their usage has brought any novel and useful concepts to the field of metaheuristics. Our result is that the ideas proposed in the six studied algorithms have been in the literature of metaheuristics for years and that the only novelty in these self-proclaimed novel algorithms is six different terminologies derived from the use of new metaphors. We discuss the reasons why the metaphors that inspired these algorithms are misleading and ultimately useless as a source of inspiration to design effective optimization tools. Finally, we discuss the rationale often presented by the authors of metaphor-based algorithms as their motivation to propose more algorithms of this type, which is based on a wrong understanding of the no-free-lunch theorems for optimization. | ||
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10.1111/itor.13176 doi (DE-627)185652308X (DE-599)KXP185652308X DE-627 ger DE-627 rda eng Camacho-Villalón, Christian L. verfasserin aut Exposing the grey wolf, moth-flame, whale, firefly, bat, and antlion algorithms six misleading optimization techniques inspired by bestial metaphors Christian L. Camacho-Villalón, Marco Dorigo and Thomas Stützle 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier evolution strategies (dpeaa)DE-206 nature-inspired algorithm (dpeaa)DE-206 novel algorithm (dpeaa)DE-206 particle swarm optimization (dpeaa)DE-206 swarm intelligence (dpeaa)DE-206 Dorigo, Marco 1961- verfasserin (DE-588)1036708837 (DE-627)751009946 (DE-576)178495484 aut Stützle, Thomas G. verfasserin (DE-588)12164183X (DE-627)601229703 (DE-576)182056732 aut Enthalten in International transactions in operational research Oxford : Wiley-Blackwell, 1994 30(2023), 6 vom: Nov., Seite 2945-2971 Online-Ressource (DE-627)320598004 (DE-600)2019815-2 (DE-576)273908545 1475-3995 nnns volume:30 year:2023 number:6 month:11 pages:2945-2971 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/itor.13176 Verlag kostenfrei https://doi.org/10.1111/itor.13176 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_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_266 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_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_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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_2190 GBV_ILN_2232 GBV_ILN_2336 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 30 2023 6 11 2945-2971 26 01 0206 4367416585 x1z 18-08-23 2403 01 DE-LFER 4375853417 00 --%%-- --%%-- n --%%-- l01 14-09-23 2403 01 DE-LFER https://doi.org/10.1111/itor.13176 26 00 DE-206 We present a rigorous, component-based analysis of six widespread metaphor-based algorithms for tackling continuous optimization problems. In addition to deconstructing the six algorithms into their components and relating them with equivalent components proposed in well-established techniques, such as particle swarm optimization and evolutionary algorithms, we analyze the use of the metaphors that inspired these algorithms to understand whether their usage has brought any novel and useful concepts to the field of metaheuristics. Our result is that the ideas proposed in the six studied algorithms have been in the literature of metaheuristics for years and that the only novelty in these self-proclaimed novel algorithms is six different terminologies derived from the use of new metaphors. We discuss the reasons why the metaphors that inspired these algorithms are misleading and ultimately useless as a source of inspiration to design effective optimization tools. Finally, we discuss the rationale often presented by the authors of metaphor-based algorithms as their motivation to propose more algorithms of this type, which is based on a wrong understanding of the no-free-lunch theorems for optimization. |
| spelling |
10.1111/itor.13176 doi (DE-627)185652308X (DE-599)KXP185652308X DE-627 ger DE-627 rda eng Camacho-Villalón, Christian L. verfasserin aut Exposing the grey wolf, moth-flame, whale, firefly, bat, and antlion algorithms six misleading optimization techniques inspired by bestial metaphors Christian L. Camacho-Villalón, Marco Dorigo and Thomas Stützle 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier evolution strategies (dpeaa)DE-206 nature-inspired algorithm (dpeaa)DE-206 novel algorithm (dpeaa)DE-206 particle swarm optimization (dpeaa)DE-206 swarm intelligence (dpeaa)DE-206 Dorigo, Marco 1961- verfasserin (DE-588)1036708837 (DE-627)751009946 (DE-576)178495484 aut Stützle, Thomas G. verfasserin (DE-588)12164183X (DE-627)601229703 (DE-576)182056732 aut Enthalten in International transactions in operational research Oxford : Wiley-Blackwell, 1994 30(2023), 6 vom: Nov., Seite 2945-2971 Online-Ressource (DE-627)320598004 (DE-600)2019815-2 (DE-576)273908545 1475-3995 nnns volume:30 year:2023 number:6 month:11 pages:2945-2971 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/itor.13176 Verlag kostenfrei https://doi.org/10.1111/itor.13176 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_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_266 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_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_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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_2190 GBV_ILN_2232 GBV_ILN_2336 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 30 2023 6 11 2945-2971 26 01 0206 4367416585 x1z 18-08-23 2403 01 DE-LFER 4375853417 00 --%%-- --%%-- n --%%-- l01 14-09-23 2403 01 DE-LFER https://doi.org/10.1111/itor.13176 26 00 DE-206 We present a rigorous, component-based analysis of six widespread metaphor-based algorithms for tackling continuous optimization problems. In addition to deconstructing the six algorithms into their components and relating them with equivalent components proposed in well-established techniques, such as particle swarm optimization and evolutionary algorithms, we analyze the use of the metaphors that inspired these algorithms to understand whether their usage has brought any novel and useful concepts to the field of metaheuristics. Our result is that the ideas proposed in the six studied algorithms have been in the literature of metaheuristics for years and that the only novelty in these self-proclaimed novel algorithms is six different terminologies derived from the use of new metaphors. We discuss the reasons why the metaphors that inspired these algorithms are misleading and ultimately useless as a source of inspiration to design effective optimization tools. Finally, we discuss the rationale often presented by the authors of metaphor-based algorithms as their motivation to propose more algorithms of this type, which is based on a wrong understanding of the no-free-lunch theorems for optimization. |
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10.1111/itor.13176 doi (DE-627)185652308X (DE-599)KXP185652308X DE-627 ger DE-627 rda eng Camacho-Villalón, Christian L. verfasserin aut Exposing the grey wolf, moth-flame, whale, firefly, bat, and antlion algorithms six misleading optimization techniques inspired by bestial metaphors Christian L. Camacho-Villalón, Marco Dorigo and Thomas Stützle 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier evolution strategies (dpeaa)DE-206 nature-inspired algorithm (dpeaa)DE-206 novel algorithm (dpeaa)DE-206 particle swarm optimization (dpeaa)DE-206 swarm intelligence (dpeaa)DE-206 Dorigo, Marco 1961- verfasserin (DE-588)1036708837 (DE-627)751009946 (DE-576)178495484 aut Stützle, Thomas G. verfasserin (DE-588)12164183X (DE-627)601229703 (DE-576)182056732 aut Enthalten in International transactions in operational research Oxford : Wiley-Blackwell, 1994 30(2023), 6 vom: Nov., Seite 2945-2971 Online-Ressource (DE-627)320598004 (DE-600)2019815-2 (DE-576)273908545 1475-3995 nnns volume:30 year:2023 number:6 month:11 pages:2945-2971 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/itor.13176 Verlag kostenfrei https://doi.org/10.1111/itor.13176 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_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_266 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_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_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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_2190 GBV_ILN_2232 GBV_ILN_2336 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 30 2023 6 11 2945-2971 26 01 0206 4367416585 x1z 18-08-23 2403 01 DE-LFER 4375853417 00 --%%-- --%%-- n --%%-- l01 14-09-23 2403 01 DE-LFER https://doi.org/10.1111/itor.13176 26 00 DE-206 We present a rigorous, component-based analysis of six widespread metaphor-based algorithms for tackling continuous optimization problems. In addition to deconstructing the six algorithms into their components and relating them with equivalent components proposed in well-established techniques, such as particle swarm optimization and evolutionary algorithms, we analyze the use of the metaphors that inspired these algorithms to understand whether their usage has brought any novel and useful concepts to the field of metaheuristics. Our result is that the ideas proposed in the six studied algorithms have been in the literature of metaheuristics for years and that the only novelty in these self-proclaimed novel algorithms is six different terminologies derived from the use of new metaphors. We discuss the reasons why the metaphors that inspired these algorithms are misleading and ultimately useless as a source of inspiration to design effective optimization tools. Finally, we discuss the rationale often presented by the authors of metaphor-based algorithms as their motivation to propose more algorithms of this type, which is based on a wrong understanding of the no-free-lunch theorems for optimization. |
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10.1111/itor.13176 doi (DE-627)185652308X (DE-599)KXP185652308X DE-627 ger DE-627 rda eng Camacho-Villalón, Christian L. verfasserin aut Exposing the grey wolf, moth-flame, whale, firefly, bat, and antlion algorithms six misleading optimization techniques inspired by bestial metaphors Christian L. Camacho-Villalón, Marco Dorigo and Thomas Stützle 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier evolution strategies (dpeaa)DE-206 nature-inspired algorithm (dpeaa)DE-206 novel algorithm (dpeaa)DE-206 particle swarm optimization (dpeaa)DE-206 swarm intelligence (dpeaa)DE-206 Dorigo, Marco 1961- verfasserin (DE-588)1036708837 (DE-627)751009946 (DE-576)178495484 aut Stützle, Thomas G. verfasserin (DE-588)12164183X (DE-627)601229703 (DE-576)182056732 aut Enthalten in International transactions in operational research Oxford : Wiley-Blackwell, 1994 30(2023), 6 vom: Nov., Seite 2945-2971 Online-Ressource (DE-627)320598004 (DE-600)2019815-2 (DE-576)273908545 1475-3995 nnns volume:30 year:2023 number:6 month:11 pages:2945-2971 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/itor.13176 Verlag kostenfrei https://doi.org/10.1111/itor.13176 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_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_266 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_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_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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_2190 GBV_ILN_2232 GBV_ILN_2336 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 30 2023 6 11 2945-2971 26 01 0206 4367416585 x1z 18-08-23 2403 01 DE-LFER 4375853417 00 --%%-- --%%-- n --%%-- l01 14-09-23 2403 01 DE-LFER https://doi.org/10.1111/itor.13176 26 00 DE-206 We present a rigorous, component-based analysis of six widespread metaphor-based algorithms for tackling continuous optimization problems. In addition to deconstructing the six algorithms into their components and relating them with equivalent components proposed in well-established techniques, such as particle swarm optimization and evolutionary algorithms, we analyze the use of the metaphors that inspired these algorithms to understand whether their usage has brought any novel and useful concepts to the field of metaheuristics. Our result is that the ideas proposed in the six studied algorithms have been in the literature of metaheuristics for years and that the only novelty in these self-proclaimed novel algorithms is six different terminologies derived from the use of new metaphors. We discuss the reasons why the metaphors that inspired these algorithms are misleading and ultimately useless as a source of inspiration to design effective optimization tools. Finally, we discuss the rationale often presented by the authors of metaphor-based algorithms as their motivation to propose more algorithms of this type, which is based on a wrong understanding of the no-free-lunch theorems for optimization. |
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10.1111/itor.13176 doi (DE-627)185652308X (DE-599)KXP185652308X DE-627 ger DE-627 rda eng Camacho-Villalón, Christian L. verfasserin aut Exposing the grey wolf, moth-flame, whale, firefly, bat, and antlion algorithms six misleading optimization techniques inspired by bestial metaphors Christian L. Camacho-Villalón, Marco Dorigo and Thomas Stützle 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier evolution strategies (dpeaa)DE-206 nature-inspired algorithm (dpeaa)DE-206 novel algorithm (dpeaa)DE-206 particle swarm optimization (dpeaa)DE-206 swarm intelligence (dpeaa)DE-206 Dorigo, Marco 1961- verfasserin (DE-588)1036708837 (DE-627)751009946 (DE-576)178495484 aut Stützle, Thomas G. verfasserin (DE-588)12164183X (DE-627)601229703 (DE-576)182056732 aut Enthalten in International transactions in operational research Oxford : Wiley-Blackwell, 1994 30(2023), 6 vom: Nov., Seite 2945-2971 Online-Ressource (DE-627)320598004 (DE-600)2019815-2 (DE-576)273908545 1475-3995 nnns volume:30 year:2023 number:6 month:11 pages:2945-2971 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/itor.13176 Verlag kostenfrei https://doi.org/10.1111/itor.13176 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_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_266 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_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_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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_2190 GBV_ILN_2232 GBV_ILN_2336 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 30 2023 6 11 2945-2971 26 01 0206 4367416585 x1z 18-08-23 2403 01 DE-LFER 4375853417 00 --%%-- --%%-- n --%%-- l01 14-09-23 2403 01 DE-LFER https://doi.org/10.1111/itor.13176 26 00 DE-206 We present a rigorous, component-based analysis of six widespread metaphor-based algorithms for tackling continuous optimization problems. In addition to deconstructing the six algorithms into their components and relating them with equivalent components proposed in well-established techniques, such as particle swarm optimization and evolutionary algorithms, we analyze the use of the metaphors that inspired these algorithms to understand whether their usage has brought any novel and useful concepts to the field of metaheuristics. Our result is that the ideas proposed in the six studied algorithms have been in the literature of metaheuristics for years and that the only novelty in these self-proclaimed novel algorithms is six different terminologies derived from the use of new metaphors. We discuss the reasons why the metaphors that inspired these algorithms are misleading and ultimately useless as a source of inspiration to design effective optimization tools. Finally, we discuss the rationale often presented by the authors of metaphor-based algorithms as their motivation to propose more algorithms of this type, which is based on a wrong understanding of the no-free-lunch theorems for optimization. |
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Camacho-Villalón, Christian L. misc evolution strategies misc nature-inspired algorithm misc novel algorithm misc particle swarm optimization misc swarm intelligence Exposing the grey wolf, moth-flame, whale, firefly, bat, and antlion algorithms six misleading optimization techniques inspired by bestial metaphors |
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26 00 DE-206 We present a rigorous, component-based analysis of six widespread metaphor-based algorithms for tackling continuous optimization problems. In addition to deconstructing the six algorithms into their components and relating them with equivalent components proposed in well-established techniques, such as particle swarm optimization and evolutionary algorithms, we analyze the use of the metaphors that inspired these algorithms to understand whether their usage has brought any novel and useful concepts to the field of metaheuristics. Our result is that the ideas proposed in the six studied algorithms have been in the literature of metaheuristics for years and that the only novelty in these self-proclaimed novel algorithms is six different terminologies derived from the use of new metaphors. We discuss the reasons why the metaphors that inspired these algorithms are misleading and ultimately useless as a source of inspiration to design effective optimization tools. Finally, we discuss the rationale often presented by the authors of metaphor-based algorithms as their motivation to propose more algorithms of this type, which is based on a wrong understanding of the no-free-lunch theorems for optimization Exposing the grey wolf, moth-flame, whale, firefly, bat, and antlion algorithms six misleading optimization techniques inspired by bestial metaphors Christian L. Camacho-Villalón, Marco Dorigo and Thomas Stützle evolution strategies (dpeaa)DE-206 nature-inspired algorithm (dpeaa)DE-206 novel algorithm (dpeaa)DE-206 particle swarm optimization (dpeaa)DE-206 swarm intelligence (dpeaa)DE-206 |
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ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4336</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_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2403</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2403</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-LFER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">30</subfield><subfield code="j">2023</subfield><subfield code="e">6</subfield><subfield code="c">11</subfield><subfield code="h">2945-2971</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4367416585</subfield><subfield code="y">x1z</subfield><subfield code="z">18-08-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="b">4375853417</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">n</subfield><subfield code="j">--%%--</subfield><subfield code="y">l01</subfield><subfield code="z">14-09-23</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="r">https://doi.org/10.1111/itor.13176</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">We present a rigorous, component-based analysis of six widespread metaphor-based algorithms for tackling continuous optimization problems. In addition to deconstructing the six algorithms into their components and relating them with equivalent components proposed in well-established techniques, such as particle swarm optimization and evolutionary algorithms, we analyze the use of the metaphors that inspired these algorithms to understand whether their usage has brought any novel and useful concepts to the field of metaheuristics. Our result is that the ideas proposed in the six studied algorithms have been in the literature of metaheuristics for years and that the only novelty in these self-proclaimed novel algorithms is six different terminologies derived from the use of new metaphors. We discuss the reasons why the metaphors that inspired these algorithms are misleading and ultimately useless as a source of inspiration to design effective optimization tools. Finally, we discuss the rationale often presented by the authors of metaphor-based algorithms as their motivation to propose more algorithms of this type, which is based on a wrong understanding of the no-free-lunch theorems for optimization.</subfield></datafield></record></collection>
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7.4013615 |