Gompertz PSO variants for Knapsack and Multi-Knapsack Problems

Abstract Particle Swarm Optimization, a potential swarm intelligence heuristic, has been recognized as a global optimizer for solving various continuous as well as discrete optimization problems. Encourged by the performance of Gompertz PSO on a set of continuous problems, this works extends the app...
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Autor*in:	Chauhan, Pinkey [verfasserIn] Pant, Millie Deep, Kusum

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Binary PSO Knapsack Problems Multi Knapsack Problems Gompertz function chaos

Anmerkung:	© Editorial Committee of Applied Mathematics 2021

Übergeordnetes Werk:	Enthalten in: Applied mathematics - Berlin [u.a.] : Editorial commitee of applied mathematics, 1993, 36(2021), 4 vom: Okt., Seite 611-630
Übergeordnetes Werk:	volume:36 ; year:2021 ; number:4 ; month:10 ; pages:611-630

Links:	Volltext

DOI / URN:	10.1007/s11766-021-4583-y

Katalog-ID:	SPR050379445

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520			\|a Abstract Particle Swarm Optimization, a potential swarm intelligence heuristic, has been recognized as a global optimizer for solving various continuous as well as discrete optimization problems. Encourged by the performance of Gompertz PSO on a set of continuous problems, this works extends the application of Gompertz PSO for solving binary optimization problems. Moreover, a new chaotic variant of Gompertz PSO namely Chaotic Gompertz Binary Particle Swarm Optimization (CGBPSO) has also been proposed. The new variant is further analysed for solving binary optimization problems. The new chaotic variant embeds the notion of chaos into GBPSO in later stages of searching process to avoid stagnation phenomena. The efficiency of both the Binary PSO variants has been tested on different sets of Knapsack Problems (KPs): 0–1 Knapsack Problem (0-1 KP) and Multidimensional Knapsack Problems (MKP). The concluding remarks have made on the basis of detailed analysis of results, which comprises the comparison of results for Knapsack and Multidimensional Knapsack problems obtained using BPSO, GBPSO and CGBPSO.
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912			\|a GBV_ILN_187
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
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912			\|a GBV_ILN_230
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912			\|a GBV_ILN_281
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912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_374
912			\|a GBV_ILN_602
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912			\|a GBV_ILN_2007
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912			\|a GBV_ILN_2025
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912			\|a GBV_ILN_2034
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912			\|a GBV_ILN_2037
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912			\|a GBV_ILN_2055
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912			\|a GBV_ILN_2057
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912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
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912			\|a GBV_ILN_2106
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publishDate	2021
allfields	10.1007/s11766-021-4583-y doi (DE-627)SPR050379445 (SPR)s11766-021-4583-y-e DE-627 ger DE-627 rakwb eng Chauhan, Pinkey verfasserin aut Gompertz PSO variants for Knapsack and Multi-Knapsack Problems 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Editorial Committee of Applied Mathematics 2021 Abstract Particle Swarm Optimization, a potential swarm intelligence heuristic, has been recognized as a global optimizer for solving various continuous as well as discrete optimization problems. Encourged by the performance of Gompertz PSO on a set of continuous problems, this works extends the application of Gompertz PSO for solving binary optimization problems. Moreover, a new chaotic variant of Gompertz PSO namely Chaotic Gompertz Binary Particle Swarm Optimization (CGBPSO) has also been proposed. The new variant is further analysed for solving binary optimization problems. The new chaotic variant embeds the notion of chaos into GBPSO in later stages of searching process to avoid stagnation phenomena. The efficiency of both the Binary PSO variants has been tested on different sets of Knapsack Problems (KPs): 0–1 Knapsack Problem (0-1 KP) and Multidimensional Knapsack Problems (MKP). The concluding remarks have made on the basis of detailed analysis of results, which comprises the comparison of results for Knapsack and Multidimensional Knapsack problems obtained using BPSO, GBPSO and CGBPSO. Binary PSO (dpeaa)DE-He213 Knapsack Problems (dpeaa)DE-He213 Multi Knapsack Problems (dpeaa)DE-He213 Gompertz function (dpeaa)DE-He213 chaos (dpeaa)DE-He213 Pant, Millie aut Deep, Kusum aut Enthalten in Applied mathematics Berlin [u.a.] : Editorial commitee of applied mathematics, 1993 36(2021), 4 vom: Okt., Seite 611-630 (DE-627)527579777 (DE-600)2277401-4 1993-0445 nnns volume:36 year:2021 number:4 month:10 pages:611-630 https://dx.doi.org/10.1007/s11766-021-4583-y 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_121 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_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 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_2700 GBV_ILN_2817 GBV_ILN_4012 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_4277 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_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 36 2021 4 10 611-630
spelling	10.1007/s11766-021-4583-y doi (DE-627)SPR050379445 (SPR)s11766-021-4583-y-e DE-627 ger DE-627 rakwb eng Chauhan, Pinkey verfasserin aut Gompertz PSO variants for Knapsack and Multi-Knapsack Problems 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Editorial Committee of Applied Mathematics 2021 Abstract Particle Swarm Optimization, a potential swarm intelligence heuristic, has been recognized as a global optimizer for solving various continuous as well as discrete optimization problems. Encourged by the performance of Gompertz PSO on a set of continuous problems, this works extends the application of Gompertz PSO for solving binary optimization problems. Moreover, a new chaotic variant of Gompertz PSO namely Chaotic Gompertz Binary Particle Swarm Optimization (CGBPSO) has also been proposed. The new variant is further analysed for solving binary optimization problems. The new chaotic variant embeds the notion of chaos into GBPSO in later stages of searching process to avoid stagnation phenomena. The efficiency of both the Binary PSO variants has been tested on different sets of Knapsack Problems (KPs): 0–1 Knapsack Problem (0-1 KP) and Multidimensional Knapsack Problems (MKP). The concluding remarks have made on the basis of detailed analysis of results, which comprises the comparison of results for Knapsack and Multidimensional Knapsack problems obtained using BPSO, GBPSO and CGBPSO. Binary PSO (dpeaa)DE-He213 Knapsack Problems (dpeaa)DE-He213 Multi Knapsack Problems (dpeaa)DE-He213 Gompertz function (dpeaa)DE-He213 chaos (dpeaa)DE-He213 Pant, Millie aut Deep, Kusum aut Enthalten in Applied mathematics Berlin [u.a.] : Editorial commitee of applied mathematics, 1993 36(2021), 4 vom: Okt., Seite 611-630 (DE-627)527579777 (DE-600)2277401-4 1993-0445 nnns volume:36 year:2021 number:4 month:10 pages:611-630 https://dx.doi.org/10.1007/s11766-021-4583-y 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_121 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_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 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_2700 GBV_ILN_2817 GBV_ILN_4012 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_4277 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_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 36 2021 4 10 611-630
allfields_unstemmed	10.1007/s11766-021-4583-y doi (DE-627)SPR050379445 (SPR)s11766-021-4583-y-e DE-627 ger DE-627 rakwb eng Chauhan, Pinkey verfasserin aut Gompertz PSO variants for Knapsack and Multi-Knapsack Problems 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Editorial Committee of Applied Mathematics 2021 Abstract Particle Swarm Optimization, a potential swarm intelligence heuristic, has been recognized as a global optimizer for solving various continuous as well as discrete optimization problems. Encourged by the performance of Gompertz PSO on a set of continuous problems, this works extends the application of Gompertz PSO for solving binary optimization problems. Moreover, a new chaotic variant of Gompertz PSO namely Chaotic Gompertz Binary Particle Swarm Optimization (CGBPSO) has also been proposed. The new variant is further analysed for solving binary optimization problems. The new chaotic variant embeds the notion of chaos into GBPSO in later stages of searching process to avoid stagnation phenomena. The efficiency of both the Binary PSO variants has been tested on different sets of Knapsack Problems (KPs): 0–1 Knapsack Problem (0-1 KP) and Multidimensional Knapsack Problems (MKP). The concluding remarks have made on the basis of detailed analysis of results, which comprises the comparison of results for Knapsack and Multidimensional Knapsack problems obtained using BPSO, GBPSO and CGBPSO. Binary PSO (dpeaa)DE-He213 Knapsack Problems (dpeaa)DE-He213 Multi Knapsack Problems (dpeaa)DE-He213 Gompertz function (dpeaa)DE-He213 chaos (dpeaa)DE-He213 Pant, Millie aut Deep, Kusum aut Enthalten in Applied mathematics Berlin [u.a.] : Editorial commitee of applied mathematics, 1993 36(2021), 4 vom: Okt., Seite 611-630 (DE-627)527579777 (DE-600)2277401-4 1993-0445 nnns volume:36 year:2021 number:4 month:10 pages:611-630 https://dx.doi.org/10.1007/s11766-021-4583-y 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_121 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_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 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_2700 GBV_ILN_2817 GBV_ILN_4012 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_4277 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_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 36 2021 4 10 611-630
allfieldsGer	10.1007/s11766-021-4583-y doi (DE-627)SPR050379445 (SPR)s11766-021-4583-y-e DE-627 ger DE-627 rakwb eng Chauhan, Pinkey verfasserin aut Gompertz PSO variants for Knapsack and Multi-Knapsack Problems 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Editorial Committee of Applied Mathematics 2021 Abstract Particle Swarm Optimization, a potential swarm intelligence heuristic, has been recognized as a global optimizer for solving various continuous as well as discrete optimization problems. Encourged by the performance of Gompertz PSO on a set of continuous problems, this works extends the application of Gompertz PSO for solving binary optimization problems. Moreover, a new chaotic variant of Gompertz PSO namely Chaotic Gompertz Binary Particle Swarm Optimization (CGBPSO) has also been proposed. The new variant is further analysed for solving binary optimization problems. The new chaotic variant embeds the notion of chaos into GBPSO in later stages of searching process to avoid stagnation phenomena. The efficiency of both the Binary PSO variants has been tested on different sets of Knapsack Problems (KPs): 0–1 Knapsack Problem (0-1 KP) and Multidimensional Knapsack Problems (MKP). The concluding remarks have made on the basis of detailed analysis of results, which comprises the comparison of results for Knapsack and Multidimensional Knapsack problems obtained using BPSO, GBPSO and CGBPSO. Binary PSO (dpeaa)DE-He213 Knapsack Problems (dpeaa)DE-He213 Multi Knapsack Problems (dpeaa)DE-He213 Gompertz function (dpeaa)DE-He213 chaos (dpeaa)DE-He213 Pant, Millie aut Deep, Kusum aut Enthalten in Applied mathematics Berlin [u.a.] : Editorial commitee of applied mathematics, 1993 36(2021), 4 vom: Okt., Seite 611-630 (DE-627)527579777 (DE-600)2277401-4 1993-0445 nnns volume:36 year:2021 number:4 month:10 pages:611-630 https://dx.doi.org/10.1007/s11766-021-4583-y 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_121 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_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 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_2700 GBV_ILN_2817 GBV_ILN_4012 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_4277 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_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 36 2021 4 10 611-630
allfieldsSound	10.1007/s11766-021-4583-y doi (DE-627)SPR050379445 (SPR)s11766-021-4583-y-e DE-627 ger DE-627 rakwb eng Chauhan, Pinkey verfasserin aut Gompertz PSO variants for Knapsack and Multi-Knapsack Problems 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Editorial Committee of Applied Mathematics 2021 Abstract Particle Swarm Optimization, a potential swarm intelligence heuristic, has been recognized as a global optimizer for solving various continuous as well as discrete optimization problems. Encourged by the performance of Gompertz PSO on a set of continuous problems, this works extends the application of Gompertz PSO for solving binary optimization problems. Moreover, a new chaotic variant of Gompertz PSO namely Chaotic Gompertz Binary Particle Swarm Optimization (CGBPSO) has also been proposed. The new variant is further analysed for solving binary optimization problems. The new chaotic variant embeds the notion of chaos into GBPSO in later stages of searching process to avoid stagnation phenomena. The efficiency of both the Binary PSO variants has been tested on different sets of Knapsack Problems (KPs): 0–1 Knapsack Problem (0-1 KP) and Multidimensional Knapsack Problems (MKP). The concluding remarks have made on the basis of detailed analysis of results, which comprises the comparison of results for Knapsack and Multidimensional Knapsack problems obtained using BPSO, GBPSO and CGBPSO. Binary PSO (dpeaa)DE-He213 Knapsack Problems (dpeaa)DE-He213 Multi Knapsack Problems (dpeaa)DE-He213 Gompertz function (dpeaa)DE-He213 chaos (dpeaa)DE-He213 Pant, Millie aut Deep, Kusum aut Enthalten in Applied mathematics Berlin [u.a.] : Editorial commitee of applied mathematics, 1993 36(2021), 4 vom: Okt., Seite 611-630 (DE-627)527579777 (DE-600)2277401-4 1993-0445 nnns volume:36 year:2021 number:4 month:10 pages:611-630 https://dx.doi.org/10.1007/s11766-021-4583-y 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_121 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_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 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_2700 GBV_ILN_2817 GBV_ILN_4012 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_4277 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_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 36 2021 4 10 611-630
language	English
source	Enthalten in Applied mathematics 36(2021), 4 vom: Okt., Seite 611-630 volume:36 year:2021 number:4 month:10 pages:611-630
sourceStr	Enthalten in Applied mathematics 36(2021), 4 vom: Okt., Seite 611-630 volume:36 year:2021 number:4 month:10 pages:611-630
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Binary PSO Knapsack Problems Multi Knapsack Problems Gompertz function chaos
isfreeaccess_bool	false
container_title	Applied mathematics
authorswithroles_txt_mv	Chauhan, Pinkey @@aut@@ Pant, Millie @@aut@@ Deep, Kusum @@aut@@
publishDateDaySort_date	2021-10-01T00:00:00Z
hierarchy_top_id	527579777
id	SPR050379445
language_de	englisch
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author	Chauhan, Pinkey
spellingShingle	Chauhan, Pinkey misc Binary PSO misc Knapsack Problems misc Multi Knapsack Problems misc Gompertz function misc chaos Gompertz PSO variants for Knapsack and Multi-Knapsack Problems
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topic_title	Gompertz PSO variants for Knapsack and Multi-Knapsack Problems Binary PSO (dpeaa)DE-He213 Knapsack Problems (dpeaa)DE-He213 Multi Knapsack Problems (dpeaa)DE-He213 Gompertz function (dpeaa)DE-He213 chaos (dpeaa)DE-He213
topic	misc Binary PSO misc Knapsack Problems misc Multi Knapsack Problems misc Gompertz function misc chaos
topic_unstemmed	misc Binary PSO misc Knapsack Problems misc Multi Knapsack Problems misc Gompertz function misc chaos
topic_browse	misc Binary PSO misc Knapsack Problems misc Multi Knapsack Problems misc Gompertz function misc chaos
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title	Gompertz PSO variants for Knapsack and Multi-Knapsack Problems
ctrlnum	(DE-627)SPR050379445 (SPR)s11766-021-4583-y-e
title_full	Gompertz PSO variants for Knapsack and Multi-Knapsack Problems
author_sort	Chauhan, Pinkey
journal	Applied mathematics
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author_browse	Chauhan, Pinkey Pant, Millie Deep, Kusum
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doi_str_mv	10.1007/s11766-021-4583-y
title_sort	gompertz pso variants for knapsack and multi-knapsack problems
title_auth	Gompertz PSO variants for Knapsack and Multi-Knapsack Problems
abstract	Abstract Particle Swarm Optimization, a potential swarm intelligence heuristic, has been recognized as a global optimizer for solving various continuous as well as discrete optimization problems. Encourged by the performance of Gompertz PSO on a set of continuous problems, this works extends the application of Gompertz PSO for solving binary optimization problems. Moreover, a new chaotic variant of Gompertz PSO namely Chaotic Gompertz Binary Particle Swarm Optimization (CGBPSO) has also been proposed. The new variant is further analysed for solving binary optimization problems. The new chaotic variant embeds the notion of chaos into GBPSO in later stages of searching process to avoid stagnation phenomena. The efficiency of both the Binary PSO variants has been tested on different sets of Knapsack Problems (KPs): 0–1 Knapsack Problem (0-1 KP) and Multidimensional Knapsack Problems (MKP). The concluding remarks have made on the basis of detailed analysis of results, which comprises the comparison of results for Knapsack and Multidimensional Knapsack problems obtained using BPSO, GBPSO and CGBPSO. © Editorial Committee of Applied Mathematics 2021
abstractGer	Abstract Particle Swarm Optimization, a potential swarm intelligence heuristic, has been recognized as a global optimizer for solving various continuous as well as discrete optimization problems. Encourged by the performance of Gompertz PSO on a set of continuous problems, this works extends the application of Gompertz PSO for solving binary optimization problems. Moreover, a new chaotic variant of Gompertz PSO namely Chaotic Gompertz Binary Particle Swarm Optimization (CGBPSO) has also been proposed. The new variant is further analysed for solving binary optimization problems. The new chaotic variant embeds the notion of chaos into GBPSO in later stages of searching process to avoid stagnation phenomena. The efficiency of both the Binary PSO variants has been tested on different sets of Knapsack Problems (KPs): 0–1 Knapsack Problem (0-1 KP) and Multidimensional Knapsack Problems (MKP). The concluding remarks have made on the basis of detailed analysis of results, which comprises the comparison of results for Knapsack and Multidimensional Knapsack problems obtained using BPSO, GBPSO and CGBPSO. © Editorial Committee of Applied Mathematics 2021
abstract_unstemmed	Abstract Particle Swarm Optimization, a potential swarm intelligence heuristic, has been recognized as a global optimizer for solving various continuous as well as discrete optimization problems. Encourged by the performance of Gompertz PSO on a set of continuous problems, this works extends the application of Gompertz PSO for solving binary optimization problems. Moreover, a new chaotic variant of Gompertz PSO namely Chaotic Gompertz Binary Particle Swarm Optimization (CGBPSO) has also been proposed. The new variant is further analysed for solving binary optimization problems. The new chaotic variant embeds the notion of chaos into GBPSO in later stages of searching process to avoid stagnation phenomena. The efficiency of both the Binary PSO variants has been tested on different sets of Knapsack Problems (KPs): 0–1 Knapsack Problem (0-1 KP) and Multidimensional Knapsack Problems (MKP). The concluding remarks have made on the basis of detailed analysis of results, which comprises the comparison of results for Knapsack and Multidimensional Knapsack problems obtained using BPSO, GBPSO and CGBPSO. © Editorial Committee of Applied Mathematics 2021
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title_short	Gompertz PSO variants for Knapsack and Multi-Knapsack Problems
url	https://dx.doi.org/10.1007/s11766-021-4583-y
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author2	Pant, Millie Deep, Kusum
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doi_str	10.1007/s11766-021-4583-y
up_date	2024-07-03T15:09:53.555Z
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Encourged by the performance of Gompertz PSO on a set of continuous problems, this works extends the application of Gompertz PSO for solving binary optimization problems. Moreover, a new chaotic variant of Gompertz PSO namely Chaotic Gompertz Binary Particle Swarm Optimization (CGBPSO) has also been proposed. The new variant is further analysed for solving binary optimization problems. The new chaotic variant embeds the notion of chaos into GBPSO in later stages of searching process to avoid stagnation phenomena. The efficiency of both the Binary PSO variants has been tested on different sets of Knapsack Problems (KPs): 0–1 Knapsack Problem (0-1 KP) and Multidimensional Knapsack Problems (MKP). 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Gompertz PSO variants for Knapsack and Multi-Knapsack Problems

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