A New Evolutionary Model Based on Cellular Learning Automata and Chaos Theory
Abstract In this paper, a new fine-grained evolutionary model, called CCLA-EM, is proposed for solving the optimization problems, which greatly overcomes the premature convergence problem of the existing evolutionary algorithms. In the proposed model, a combination of an evolutionary algorithm with...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zarei, Bagher [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© Ohmsha, Ltd. and Springer Japan KK, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: New generation computing - Tokyo [u.a.] : Ohmsha, 1983, 40(2022), 1 vom: 21. Feb., Seite 285-310 |
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| Übergeordnetes Werk: |
volume:40 ; year:2022 ; number:1 ; day:21 ; month:02 ; pages:285-310 |
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| DOI / URN: |
10.1007/s00354-022-00159-1 |
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SPR050706403 |
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| 245 | 1 | 2 | |a A New Evolutionary Model Based on Cellular Learning Automata and Chaos Theory |
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| 520 | |a Abstract In this paper, a new fine-grained evolutionary model, called CCLA-EM, is proposed for solving the optimization problems, which greatly overcomes the premature convergence problem of the existing evolutionary algorithms. In the proposed model, a combination of an evolutionary algorithm with a cellular learning automaton is used. The population individuals are distributed on the cells of a cellular learning automaton. Each individual interacts and cooperates with the individuals of neighboring cells to reach the global optimum. Distributing the population individuals on the cells of a cellular learning automaton allows the parallel implementation of the proposed model. Also, in different stages of the proposed model, numbers generated by a chaotic process are used instead of random ones. The use of numbers generated by a chaotic process leads to a complete search of the search space and hence avoids being trapped in local optima. Experiments on various benchmarks of the community structure detection problem indicate the superiority of the proposed model to the well-known algorithms GA-net and ICLA-net. | ||
| 650 | 4 | |a Evolutionary algorithm |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Cellular evolutionary algorithm |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Cellular automata |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Learning automata |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Cellular learning automata |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Chaos theory |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Community structure detection |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Meybodi, Mohammad Reza |4 aut | |
| 700 | 1 | |a Masoumi, Behrooz |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t New generation computing |d Tokyo [u.a.] : Ohmsha, 1983 |g 40(2022), 1 vom: 21. Feb., Seite 285-310 |w (DE-627)470150122 |w (DE-600)2164639-9 |x 1882-7055 |7 nnns |
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10.1007/s00354-022-00159-1 doi (DE-627)SPR050706403 (SPR)s00354-022-00159-1-e DE-627 ger DE-627 rakwb eng Zarei, Bagher verfasserin (orcid)0000-0002-3535-5093 aut A New Evolutionary Model Based on Cellular Learning Automata and Chaos Theory 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Ohmsha, Ltd. and Springer Japan KK, part of Springer Nature 2022 Abstract In this paper, a new fine-grained evolutionary model, called CCLA-EM, is proposed for solving the optimization problems, which greatly overcomes the premature convergence problem of the existing evolutionary algorithms. In the proposed model, a combination of an evolutionary algorithm with a cellular learning automaton is used. The population individuals are distributed on the cells of a cellular learning automaton. Each individual interacts and cooperates with the individuals of neighboring cells to reach the global optimum. Distributing the population individuals on the cells of a cellular learning automaton allows the parallel implementation of the proposed model. Also, in different stages of the proposed model, numbers generated by a chaotic process are used instead of random ones. The use of numbers generated by a chaotic process leads to a complete search of the search space and hence avoids being trapped in local optima. Experiments on various benchmarks of the community structure detection problem indicate the superiority of the proposed model to the well-known algorithms GA-net and ICLA-net. Evolutionary algorithm (dpeaa)DE-He213 Cellular evolutionary algorithm (dpeaa)DE-He213 Cellular automata (dpeaa)DE-He213 Learning automata (dpeaa)DE-He213 Cellular learning automata (dpeaa)DE-He213 Chaos theory (dpeaa)DE-He213 Community structure detection (dpeaa)DE-He213 Meybodi, Mohammad Reza aut Masoumi, Behrooz aut Enthalten in New generation computing Tokyo [u.a.] : Ohmsha, 1983 40(2022), 1 vom: 21. Feb., Seite 285-310 (DE-627)470150122 (DE-600)2164639-9 1882-7055 nnns volume:40 year:2022 number:1 day:21 month:02 pages:285-310 https://dx.doi.org/10.1007/s00354-022-00159-1 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_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_206 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_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_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 40 2022 1 21 02 285-310 |
| spelling |
10.1007/s00354-022-00159-1 doi (DE-627)SPR050706403 (SPR)s00354-022-00159-1-e DE-627 ger DE-627 rakwb eng Zarei, Bagher verfasserin (orcid)0000-0002-3535-5093 aut A New Evolutionary Model Based on Cellular Learning Automata and Chaos Theory 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Ohmsha, Ltd. and Springer Japan KK, part of Springer Nature 2022 Abstract In this paper, a new fine-grained evolutionary model, called CCLA-EM, is proposed for solving the optimization problems, which greatly overcomes the premature convergence problem of the existing evolutionary algorithms. In the proposed model, a combination of an evolutionary algorithm with a cellular learning automaton is used. The population individuals are distributed on the cells of a cellular learning automaton. Each individual interacts and cooperates with the individuals of neighboring cells to reach the global optimum. Distributing the population individuals on the cells of a cellular learning automaton allows the parallel implementation of the proposed model. Also, in different stages of the proposed model, numbers generated by a chaotic process are used instead of random ones. The use of numbers generated by a chaotic process leads to a complete search of the search space and hence avoids being trapped in local optima. Experiments on various benchmarks of the community structure detection problem indicate the superiority of the proposed model to the well-known algorithms GA-net and ICLA-net. Evolutionary algorithm (dpeaa)DE-He213 Cellular evolutionary algorithm (dpeaa)DE-He213 Cellular automata (dpeaa)DE-He213 Learning automata (dpeaa)DE-He213 Cellular learning automata (dpeaa)DE-He213 Chaos theory (dpeaa)DE-He213 Community structure detection (dpeaa)DE-He213 Meybodi, Mohammad Reza aut Masoumi, Behrooz aut Enthalten in New generation computing Tokyo [u.a.] : Ohmsha, 1983 40(2022), 1 vom: 21. Feb., Seite 285-310 (DE-627)470150122 (DE-600)2164639-9 1882-7055 nnns volume:40 year:2022 number:1 day:21 month:02 pages:285-310 https://dx.doi.org/10.1007/s00354-022-00159-1 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_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_206 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_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_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 40 2022 1 21 02 285-310 |
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10.1007/s00354-022-00159-1 doi (DE-627)SPR050706403 (SPR)s00354-022-00159-1-e DE-627 ger DE-627 rakwb eng Zarei, Bagher verfasserin (orcid)0000-0002-3535-5093 aut A New Evolutionary Model Based on Cellular Learning Automata and Chaos Theory 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Ohmsha, Ltd. and Springer Japan KK, part of Springer Nature 2022 Abstract In this paper, a new fine-grained evolutionary model, called CCLA-EM, is proposed for solving the optimization problems, which greatly overcomes the premature convergence problem of the existing evolutionary algorithms. In the proposed model, a combination of an evolutionary algorithm with a cellular learning automaton is used. The population individuals are distributed on the cells of a cellular learning automaton. Each individual interacts and cooperates with the individuals of neighboring cells to reach the global optimum. Distributing the population individuals on the cells of a cellular learning automaton allows the parallel implementation of the proposed model. Also, in different stages of the proposed model, numbers generated by a chaotic process are used instead of random ones. The use of numbers generated by a chaotic process leads to a complete search of the search space and hence avoids being trapped in local optima. Experiments on various benchmarks of the community structure detection problem indicate the superiority of the proposed model to the well-known algorithms GA-net and ICLA-net. Evolutionary algorithm (dpeaa)DE-He213 Cellular evolutionary algorithm (dpeaa)DE-He213 Cellular automata (dpeaa)DE-He213 Learning automata (dpeaa)DE-He213 Cellular learning automata (dpeaa)DE-He213 Chaos theory (dpeaa)DE-He213 Community structure detection (dpeaa)DE-He213 Meybodi, Mohammad Reza aut Masoumi, Behrooz aut Enthalten in New generation computing Tokyo [u.a.] : Ohmsha, 1983 40(2022), 1 vom: 21. Feb., Seite 285-310 (DE-627)470150122 (DE-600)2164639-9 1882-7055 nnns volume:40 year:2022 number:1 day:21 month:02 pages:285-310 https://dx.doi.org/10.1007/s00354-022-00159-1 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_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_206 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_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_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 40 2022 1 21 02 285-310 |
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10.1007/s00354-022-00159-1 doi (DE-627)SPR050706403 (SPR)s00354-022-00159-1-e DE-627 ger DE-627 rakwb eng Zarei, Bagher verfasserin (orcid)0000-0002-3535-5093 aut A New Evolutionary Model Based on Cellular Learning Automata and Chaos Theory 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Ohmsha, Ltd. and Springer Japan KK, part of Springer Nature 2022 Abstract In this paper, a new fine-grained evolutionary model, called CCLA-EM, is proposed for solving the optimization problems, which greatly overcomes the premature convergence problem of the existing evolutionary algorithms. In the proposed model, a combination of an evolutionary algorithm with a cellular learning automaton is used. The population individuals are distributed on the cells of a cellular learning automaton. Each individual interacts and cooperates with the individuals of neighboring cells to reach the global optimum. Distributing the population individuals on the cells of a cellular learning automaton allows the parallel implementation of the proposed model. Also, in different stages of the proposed model, numbers generated by a chaotic process are used instead of random ones. The use of numbers generated by a chaotic process leads to a complete search of the search space and hence avoids being trapped in local optima. Experiments on various benchmarks of the community structure detection problem indicate the superiority of the proposed model to the well-known algorithms GA-net and ICLA-net. Evolutionary algorithm (dpeaa)DE-He213 Cellular evolutionary algorithm (dpeaa)DE-He213 Cellular automata (dpeaa)DE-He213 Learning automata (dpeaa)DE-He213 Cellular learning automata (dpeaa)DE-He213 Chaos theory (dpeaa)DE-He213 Community structure detection (dpeaa)DE-He213 Meybodi, Mohammad Reza aut Masoumi, Behrooz aut Enthalten in New generation computing Tokyo [u.a.] : Ohmsha, 1983 40(2022), 1 vom: 21. Feb., Seite 285-310 (DE-627)470150122 (DE-600)2164639-9 1882-7055 nnns volume:40 year:2022 number:1 day:21 month:02 pages:285-310 https://dx.doi.org/10.1007/s00354-022-00159-1 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_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_206 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_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_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 40 2022 1 21 02 285-310 |
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10.1007/s00354-022-00159-1 doi (DE-627)SPR050706403 (SPR)s00354-022-00159-1-e DE-627 ger DE-627 rakwb eng Zarei, Bagher verfasserin (orcid)0000-0002-3535-5093 aut A New Evolutionary Model Based on Cellular Learning Automata and Chaos Theory 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Ohmsha, Ltd. and Springer Japan KK, part of Springer Nature 2022 Abstract In this paper, a new fine-grained evolutionary model, called CCLA-EM, is proposed for solving the optimization problems, which greatly overcomes the premature convergence problem of the existing evolutionary algorithms. In the proposed model, a combination of an evolutionary algorithm with a cellular learning automaton is used. The population individuals are distributed on the cells of a cellular learning automaton. Each individual interacts and cooperates with the individuals of neighboring cells to reach the global optimum. Distributing the population individuals on the cells of a cellular learning automaton allows the parallel implementation of the proposed model. Also, in different stages of the proposed model, numbers generated by a chaotic process are used instead of random ones. The use of numbers generated by a chaotic process leads to a complete search of the search space and hence avoids being trapped in local optima. Experiments on various benchmarks of the community structure detection problem indicate the superiority of the proposed model to the well-known algorithms GA-net and ICLA-net. Evolutionary algorithm (dpeaa)DE-He213 Cellular evolutionary algorithm (dpeaa)DE-He213 Cellular automata (dpeaa)DE-He213 Learning automata (dpeaa)DE-He213 Cellular learning automata (dpeaa)DE-He213 Chaos theory (dpeaa)DE-He213 Community structure detection (dpeaa)DE-He213 Meybodi, Mohammad Reza aut Masoumi, Behrooz aut Enthalten in New generation computing Tokyo [u.a.] : Ohmsha, 1983 40(2022), 1 vom: 21. Feb., Seite 285-310 (DE-627)470150122 (DE-600)2164639-9 1882-7055 nnns volume:40 year:2022 number:1 day:21 month:02 pages:285-310 https://dx.doi.org/10.1007/s00354-022-00159-1 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_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_206 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_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_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 40 2022 1 21 02 285-310 |
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A New Evolutionary Model Based on Cellular Learning Automata and Chaos Theory |
| abstract |
Abstract In this paper, a new fine-grained evolutionary model, called CCLA-EM, is proposed for solving the optimization problems, which greatly overcomes the premature convergence problem of the existing evolutionary algorithms. In the proposed model, a combination of an evolutionary algorithm with a cellular learning automaton is used. The population individuals are distributed on the cells of a cellular learning automaton. Each individual interacts and cooperates with the individuals of neighboring cells to reach the global optimum. Distributing the population individuals on the cells of a cellular learning automaton allows the parallel implementation of the proposed model. Also, in different stages of the proposed model, numbers generated by a chaotic process are used instead of random ones. The use of numbers generated by a chaotic process leads to a complete search of the search space and hence avoids being trapped in local optima. Experiments on various benchmarks of the community structure detection problem indicate the superiority of the proposed model to the well-known algorithms GA-net and ICLA-net. © Ohmsha, Ltd. and Springer Japan KK, part of Springer Nature 2022 |
| abstractGer |
Abstract In this paper, a new fine-grained evolutionary model, called CCLA-EM, is proposed for solving the optimization problems, which greatly overcomes the premature convergence problem of the existing evolutionary algorithms. In the proposed model, a combination of an evolutionary algorithm with a cellular learning automaton is used. The population individuals are distributed on the cells of a cellular learning automaton. Each individual interacts and cooperates with the individuals of neighboring cells to reach the global optimum. Distributing the population individuals on the cells of a cellular learning automaton allows the parallel implementation of the proposed model. Also, in different stages of the proposed model, numbers generated by a chaotic process are used instead of random ones. The use of numbers generated by a chaotic process leads to a complete search of the search space and hence avoids being trapped in local optima. Experiments on various benchmarks of the community structure detection problem indicate the superiority of the proposed model to the well-known algorithms GA-net and ICLA-net. © Ohmsha, Ltd. and Springer Japan KK, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract In this paper, a new fine-grained evolutionary model, called CCLA-EM, is proposed for solving the optimization problems, which greatly overcomes the premature convergence problem of the existing evolutionary algorithms. In the proposed model, a combination of an evolutionary algorithm with a cellular learning automaton is used. The population individuals are distributed on the cells of a cellular learning automaton. Each individual interacts and cooperates with the individuals of neighboring cells to reach the global optimum. Distributing the population individuals on the cells of a cellular learning automaton allows the parallel implementation of the proposed model. Also, in different stages of the proposed model, numbers generated by a chaotic process are used instead of random ones. The use of numbers generated by a chaotic process leads to a complete search of the search space and hence avoids being trapped in local optima. Experiments on various benchmarks of the community structure detection problem indicate the superiority of the proposed model to the well-known algorithms GA-net and ICLA-net. © Ohmsha, Ltd. and Springer Japan KK, part of Springer Nature 2022 |
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A New Evolutionary Model Based on Cellular Learning Automata and Chaos Theory |
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Meybodi, Mohammad Reza Masoumi, Behrooz |
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Meybodi, Mohammad Reza Masoumi, Behrooz |
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| doi_str |
10.1007/s00354-022-00159-1 |
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2024-07-03T17:15:33.843Z |
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|
| score |
7.399967 |