Weighted superposition attraction algorithm for binary optimization problems
Abstract Weighted superposition attraction algorithm (WSA) is a new generation population-based metaheuristic algorithm, which has been recently proposed to solve various optimization problems. Inspired by the superposition of particles principle in physics, individuals of WSA generate a superpositi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Baykasoğlu, Adil [verfasserIn] Ozsoydan, Fehmi Burcin [verfasserIn] Senol, M. Emre [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
Weighted superposition attraction algorithm |
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| Übergeordnetes Werk: |
Enthalten in: Operational research - Berlin : Springer, 2001, 20(2018), 4 vom: 17. Sept., Seite 2555-2581 |
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| Übergeordnetes Werk: |
volume:20 ; year:2018 ; number:4 ; day:17 ; month:09 ; pages:2555-2581 |
| Links: |
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| DOI / URN: |
10.1007/s12351-018-0427-9 |
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| Katalog-ID: |
SPR041260856 |
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| 520 | |a Abstract Weighted superposition attraction algorithm (WSA) is a new generation population-based metaheuristic algorithm, which has been recently proposed to solve various optimization problems. Inspired by the superposition of particles principle in physics, individuals of WSA generate a superposition, which leads other agents (solution vectors). Alternatively, based on the quality of the generated superposition, individuals occasionally tend to perform random walks. Although WSA is proven to be successful in both real-valued and some dynamic optimization problems, the performance of this new algorithm needs to be examined also in stationary binary optimization problems, which is the main motivation of the present study. Accordingly, WSA is first designed for stationary binary spaces. In this modification, WSA does not require any transfer functions to convert real numbers to binary, whereas such functions are commonly used in numerous approximation algorithms. Moreover, a step sizing function, which encourages population diversity at earlier iterations while intensifying the search towards the end, is adopted in the proposed WSA. Thus, premature convergence and local optima problems are attempted to be avoided. In this context, the contribution of the present study is twofold: first, WSA is modified for stationary binary optimization problems, secondarily, it is further enhanced by the proposed step sizing function. The performance of the modified WSA is examined by using three well-known binary optimization problems, including uncapacitated facility location problem, 0–1 knapsack problem and a natural extension of it, the set union knapsack problem. As demonstrated by the comprehensive experimental study, results point out the efficiency of the proposed WSA modification in binary optimization problems. | ||
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| 650 | 4 | |a Binary optimization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Uncapacitated facility location problem |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a 0–1 Knapsack problem |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Set union knapsack problem |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Ozsoydan, Fehmi Burcin |e verfasserin |4 aut | |
| 700 | 1 | |a Senol, M. Emre |e verfasserin |4 aut | |
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10.1007/s12351-018-0427-9 doi (DE-627)SPR041260856 (SPR)s12351-018-0427-9-e DE-627 ger DE-627 rakwb eng 650 ASE 330 ASE Baykasoğlu, Adil verfasserin aut Weighted superposition attraction algorithm for binary optimization problems 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Weighted superposition attraction algorithm (WSA) is a new generation population-based metaheuristic algorithm, which has been recently proposed to solve various optimization problems. Inspired by the superposition of particles principle in physics, individuals of WSA generate a superposition, which leads other agents (solution vectors). Alternatively, based on the quality of the generated superposition, individuals occasionally tend to perform random walks. Although WSA is proven to be successful in both real-valued and some dynamic optimization problems, the performance of this new algorithm needs to be examined also in stationary binary optimization problems, which is the main motivation of the present study. Accordingly, WSA is first designed for stationary binary spaces. In this modification, WSA does not require any transfer functions to convert real numbers to binary, whereas such functions are commonly used in numerous approximation algorithms. Moreover, a step sizing function, which encourages population diversity at earlier iterations while intensifying the search towards the end, is adopted in the proposed WSA. Thus, premature convergence and local optima problems are attempted to be avoided. In this context, the contribution of the present study is twofold: first, WSA is modified for stationary binary optimization problems, secondarily, it is further enhanced by the proposed step sizing function. The performance of the modified WSA is examined by using three well-known binary optimization problems, including uncapacitated facility location problem, 0–1 knapsack problem and a natural extension of it, the set union knapsack problem. As demonstrated by the comprehensive experimental study, results point out the efficiency of the proposed WSA modification in binary optimization problems. Weighted superposition attraction algorithm (dpeaa)DE-He213 Binary optimization (dpeaa)DE-He213 Uncapacitated facility location problem (dpeaa)DE-He213 0–1 Knapsack problem (dpeaa)DE-He213 Set union knapsack problem (dpeaa)DE-He213 Ozsoydan, Fehmi Burcin verfasserin aut Senol, M. Emre verfasserin aut Enthalten in Operational research Berlin : Springer, 2001 20(2018), 4 vom: 17. Sept., Seite 2555-2581 (DE-627)566014270 (DE-600)2425760-6 1866-1505 nnns volume:20 year:2018 number:4 day:17 month:09 pages:2555-2581 https://dx.doi.org/10.1007/s12351-018-0427-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-WIW SSG-OLC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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 20 2018 4 17 09 2555-2581 |
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10.1007/s12351-018-0427-9 doi (DE-627)SPR041260856 (SPR)s12351-018-0427-9-e DE-627 ger DE-627 rakwb eng 650 ASE 330 ASE Baykasoğlu, Adil verfasserin aut Weighted superposition attraction algorithm for binary optimization problems 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Weighted superposition attraction algorithm (WSA) is a new generation population-based metaheuristic algorithm, which has been recently proposed to solve various optimization problems. Inspired by the superposition of particles principle in physics, individuals of WSA generate a superposition, which leads other agents (solution vectors). Alternatively, based on the quality of the generated superposition, individuals occasionally tend to perform random walks. Although WSA is proven to be successful in both real-valued and some dynamic optimization problems, the performance of this new algorithm needs to be examined also in stationary binary optimization problems, which is the main motivation of the present study. Accordingly, WSA is first designed for stationary binary spaces. In this modification, WSA does not require any transfer functions to convert real numbers to binary, whereas such functions are commonly used in numerous approximation algorithms. Moreover, a step sizing function, which encourages population diversity at earlier iterations while intensifying the search towards the end, is adopted in the proposed WSA. Thus, premature convergence and local optima problems are attempted to be avoided. In this context, the contribution of the present study is twofold: first, WSA is modified for stationary binary optimization problems, secondarily, it is further enhanced by the proposed step sizing function. The performance of the modified WSA is examined by using three well-known binary optimization problems, including uncapacitated facility location problem, 0–1 knapsack problem and a natural extension of it, the set union knapsack problem. As demonstrated by the comprehensive experimental study, results point out the efficiency of the proposed WSA modification in binary optimization problems. Weighted superposition attraction algorithm (dpeaa)DE-He213 Binary optimization (dpeaa)DE-He213 Uncapacitated facility location problem (dpeaa)DE-He213 0–1 Knapsack problem (dpeaa)DE-He213 Set union knapsack problem (dpeaa)DE-He213 Ozsoydan, Fehmi Burcin verfasserin aut Senol, M. Emre verfasserin aut Enthalten in Operational research Berlin : Springer, 2001 20(2018), 4 vom: 17. Sept., Seite 2555-2581 (DE-627)566014270 (DE-600)2425760-6 1866-1505 nnns volume:20 year:2018 number:4 day:17 month:09 pages:2555-2581 https://dx.doi.org/10.1007/s12351-018-0427-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-WIW SSG-OLC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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 20 2018 4 17 09 2555-2581 |
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10.1007/s12351-018-0427-9 doi (DE-627)SPR041260856 (SPR)s12351-018-0427-9-e DE-627 ger DE-627 rakwb eng 650 ASE 330 ASE Baykasoğlu, Adil verfasserin aut Weighted superposition attraction algorithm for binary optimization problems 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Weighted superposition attraction algorithm (WSA) is a new generation population-based metaheuristic algorithm, which has been recently proposed to solve various optimization problems. Inspired by the superposition of particles principle in physics, individuals of WSA generate a superposition, which leads other agents (solution vectors). Alternatively, based on the quality of the generated superposition, individuals occasionally tend to perform random walks. Although WSA is proven to be successful in both real-valued and some dynamic optimization problems, the performance of this new algorithm needs to be examined also in stationary binary optimization problems, which is the main motivation of the present study. Accordingly, WSA is first designed for stationary binary spaces. In this modification, WSA does not require any transfer functions to convert real numbers to binary, whereas such functions are commonly used in numerous approximation algorithms. Moreover, a step sizing function, which encourages population diversity at earlier iterations while intensifying the search towards the end, is adopted in the proposed WSA. Thus, premature convergence and local optima problems are attempted to be avoided. In this context, the contribution of the present study is twofold: first, WSA is modified for stationary binary optimization problems, secondarily, it is further enhanced by the proposed step sizing function. The performance of the modified WSA is examined by using three well-known binary optimization problems, including uncapacitated facility location problem, 0–1 knapsack problem and a natural extension of it, the set union knapsack problem. As demonstrated by the comprehensive experimental study, results point out the efficiency of the proposed WSA modification in binary optimization problems. Weighted superposition attraction algorithm (dpeaa)DE-He213 Binary optimization (dpeaa)DE-He213 Uncapacitated facility location problem (dpeaa)DE-He213 0–1 Knapsack problem (dpeaa)DE-He213 Set union knapsack problem (dpeaa)DE-He213 Ozsoydan, Fehmi Burcin verfasserin aut Senol, M. Emre verfasserin aut Enthalten in Operational research Berlin : Springer, 2001 20(2018), 4 vom: 17. Sept., Seite 2555-2581 (DE-627)566014270 (DE-600)2425760-6 1866-1505 nnns volume:20 year:2018 number:4 day:17 month:09 pages:2555-2581 https://dx.doi.org/10.1007/s12351-018-0427-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-WIW SSG-OLC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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 20 2018 4 17 09 2555-2581 |
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10.1007/s12351-018-0427-9 doi (DE-627)SPR041260856 (SPR)s12351-018-0427-9-e DE-627 ger DE-627 rakwb eng 650 ASE 330 ASE Baykasoğlu, Adil verfasserin aut Weighted superposition attraction algorithm for binary optimization problems 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Weighted superposition attraction algorithm (WSA) is a new generation population-based metaheuristic algorithm, which has been recently proposed to solve various optimization problems. Inspired by the superposition of particles principle in physics, individuals of WSA generate a superposition, which leads other agents (solution vectors). Alternatively, based on the quality of the generated superposition, individuals occasionally tend to perform random walks. Although WSA is proven to be successful in both real-valued and some dynamic optimization problems, the performance of this new algorithm needs to be examined also in stationary binary optimization problems, which is the main motivation of the present study. Accordingly, WSA is first designed for stationary binary spaces. In this modification, WSA does not require any transfer functions to convert real numbers to binary, whereas such functions are commonly used in numerous approximation algorithms. Moreover, a step sizing function, which encourages population diversity at earlier iterations while intensifying the search towards the end, is adopted in the proposed WSA. Thus, premature convergence and local optima problems are attempted to be avoided. In this context, the contribution of the present study is twofold: first, WSA is modified for stationary binary optimization problems, secondarily, it is further enhanced by the proposed step sizing function. The performance of the modified WSA is examined by using three well-known binary optimization problems, including uncapacitated facility location problem, 0–1 knapsack problem and a natural extension of it, the set union knapsack problem. As demonstrated by the comprehensive experimental study, results point out the efficiency of the proposed WSA modification in binary optimization problems. Weighted superposition attraction algorithm (dpeaa)DE-He213 Binary optimization (dpeaa)DE-He213 Uncapacitated facility location problem (dpeaa)DE-He213 0–1 Knapsack problem (dpeaa)DE-He213 Set union knapsack problem (dpeaa)DE-He213 Ozsoydan, Fehmi Burcin verfasserin aut Senol, M. Emre verfasserin aut Enthalten in Operational research Berlin : Springer, 2001 20(2018), 4 vom: 17. Sept., Seite 2555-2581 (DE-627)566014270 (DE-600)2425760-6 1866-1505 nnns volume:20 year:2018 number:4 day:17 month:09 pages:2555-2581 https://dx.doi.org/10.1007/s12351-018-0427-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-WIW SSG-OLC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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 20 2018 4 17 09 2555-2581 |
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10.1007/s12351-018-0427-9 doi (DE-627)SPR041260856 (SPR)s12351-018-0427-9-e DE-627 ger DE-627 rakwb eng 650 ASE 330 ASE Baykasoğlu, Adil verfasserin aut Weighted superposition attraction algorithm for binary optimization problems 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Weighted superposition attraction algorithm (WSA) is a new generation population-based metaheuristic algorithm, which has been recently proposed to solve various optimization problems. Inspired by the superposition of particles principle in physics, individuals of WSA generate a superposition, which leads other agents (solution vectors). Alternatively, based on the quality of the generated superposition, individuals occasionally tend to perform random walks. Although WSA is proven to be successful in both real-valued and some dynamic optimization problems, the performance of this new algorithm needs to be examined also in stationary binary optimization problems, which is the main motivation of the present study. Accordingly, WSA is first designed for stationary binary spaces. In this modification, WSA does not require any transfer functions to convert real numbers to binary, whereas such functions are commonly used in numerous approximation algorithms. Moreover, a step sizing function, which encourages population diversity at earlier iterations while intensifying the search towards the end, is adopted in the proposed WSA. Thus, premature convergence and local optima problems are attempted to be avoided. In this context, the contribution of the present study is twofold: first, WSA is modified for stationary binary optimization problems, secondarily, it is further enhanced by the proposed step sizing function. The performance of the modified WSA is examined by using three well-known binary optimization problems, including uncapacitated facility location problem, 0–1 knapsack problem and a natural extension of it, the set union knapsack problem. As demonstrated by the comprehensive experimental study, results point out the efficiency of the proposed WSA modification in binary optimization problems. Weighted superposition attraction algorithm (dpeaa)DE-He213 Binary optimization (dpeaa)DE-He213 Uncapacitated facility location problem (dpeaa)DE-He213 0–1 Knapsack problem (dpeaa)DE-He213 Set union knapsack problem (dpeaa)DE-He213 Ozsoydan, Fehmi Burcin verfasserin aut Senol, M. Emre verfasserin aut Enthalten in Operational research Berlin : Springer, 2001 20(2018), 4 vom: 17. Sept., Seite 2555-2581 (DE-627)566014270 (DE-600)2425760-6 1866-1505 nnns volume:20 year:2018 number:4 day:17 month:09 pages:2555-2581 https://dx.doi.org/10.1007/s12351-018-0427-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-WIW SSG-OLC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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 20 2018 4 17 09 2555-2581 |
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Baykasoğlu, Adil @@aut@@ Ozsoydan, Fehmi Burcin @@aut@@ Senol, M. Emre @@aut@@ |
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Inspired by the superposition of particles principle in physics, individuals of WSA generate a superposition, which leads other agents (solution vectors). Alternatively, based on the quality of the generated superposition, individuals occasionally tend to perform random walks. Although WSA is proven to be successful in both real-valued and some dynamic optimization problems, the performance of this new algorithm needs to be examined also in stationary binary optimization problems, which is the main motivation of the present study. Accordingly, WSA is first designed for stationary binary spaces. In this modification, WSA does not require any transfer functions to convert real numbers to binary, whereas such functions are commonly used in numerous approximation algorithms. Moreover, a step sizing function, which encourages population diversity at earlier iterations while intensifying the search towards the end, is adopted in the proposed WSA. Thus, premature convergence and local optima problems are attempted to be avoided. In this context, the contribution of the present study is twofold: first, WSA is modified for stationary binary optimization problems, secondarily, it is further enhanced by the proposed step sizing function. The performance of the modified WSA is examined by using three well-known binary optimization problems, including uncapacitated facility location problem, 0–1 knapsack problem and a natural extension of it, the set union knapsack problem. 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Baykasoğlu, Adil |
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Baykasoğlu, Adil ddc 650 ddc 330 misc Weighted superposition attraction algorithm misc Binary optimization misc Uncapacitated facility location problem misc 0–1 Knapsack problem misc Set union knapsack problem Weighted superposition attraction algorithm for binary optimization problems |
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650 ASE 330 ASE Weighted superposition attraction algorithm for binary optimization problems Weighted superposition attraction algorithm (dpeaa)DE-He213 Binary optimization (dpeaa)DE-He213 Uncapacitated facility location problem (dpeaa)DE-He213 0–1 Knapsack problem (dpeaa)DE-He213 Set union knapsack problem (dpeaa)DE-He213 |
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Weighted superposition attraction algorithm for binary optimization problems |
| abstract |
Abstract Weighted superposition attraction algorithm (WSA) is a new generation population-based metaheuristic algorithm, which has been recently proposed to solve various optimization problems. Inspired by the superposition of particles principle in physics, individuals of WSA generate a superposition, which leads other agents (solution vectors). Alternatively, based on the quality of the generated superposition, individuals occasionally tend to perform random walks. Although WSA is proven to be successful in both real-valued and some dynamic optimization problems, the performance of this new algorithm needs to be examined also in stationary binary optimization problems, which is the main motivation of the present study. Accordingly, WSA is first designed for stationary binary spaces. In this modification, WSA does not require any transfer functions to convert real numbers to binary, whereas such functions are commonly used in numerous approximation algorithms. Moreover, a step sizing function, which encourages population diversity at earlier iterations while intensifying the search towards the end, is adopted in the proposed WSA. Thus, premature convergence and local optima problems are attempted to be avoided. In this context, the contribution of the present study is twofold: first, WSA is modified for stationary binary optimization problems, secondarily, it is further enhanced by the proposed step sizing function. The performance of the modified WSA is examined by using three well-known binary optimization problems, including uncapacitated facility location problem, 0–1 knapsack problem and a natural extension of it, the set union knapsack problem. As demonstrated by the comprehensive experimental study, results point out the efficiency of the proposed WSA modification in binary optimization problems. |
| abstractGer |
Abstract Weighted superposition attraction algorithm (WSA) is a new generation population-based metaheuristic algorithm, which has been recently proposed to solve various optimization problems. Inspired by the superposition of particles principle in physics, individuals of WSA generate a superposition, which leads other agents (solution vectors). Alternatively, based on the quality of the generated superposition, individuals occasionally tend to perform random walks. Although WSA is proven to be successful in both real-valued and some dynamic optimization problems, the performance of this new algorithm needs to be examined also in stationary binary optimization problems, which is the main motivation of the present study. Accordingly, WSA is first designed for stationary binary spaces. In this modification, WSA does not require any transfer functions to convert real numbers to binary, whereas such functions are commonly used in numerous approximation algorithms. Moreover, a step sizing function, which encourages population diversity at earlier iterations while intensifying the search towards the end, is adopted in the proposed WSA. Thus, premature convergence and local optima problems are attempted to be avoided. In this context, the contribution of the present study is twofold: first, WSA is modified for stationary binary optimization problems, secondarily, it is further enhanced by the proposed step sizing function. The performance of the modified WSA is examined by using three well-known binary optimization problems, including uncapacitated facility location problem, 0–1 knapsack problem and a natural extension of it, the set union knapsack problem. As demonstrated by the comprehensive experimental study, results point out the efficiency of the proposed WSA modification in binary optimization problems. |
| abstract_unstemmed |
Abstract Weighted superposition attraction algorithm (WSA) is a new generation population-based metaheuristic algorithm, which has been recently proposed to solve various optimization problems. Inspired by the superposition of particles principle in physics, individuals of WSA generate a superposition, which leads other agents (solution vectors). Alternatively, based on the quality of the generated superposition, individuals occasionally tend to perform random walks. Although WSA is proven to be successful in both real-valued and some dynamic optimization problems, the performance of this new algorithm needs to be examined also in stationary binary optimization problems, which is the main motivation of the present study. Accordingly, WSA is first designed for stationary binary spaces. In this modification, WSA does not require any transfer functions to convert real numbers to binary, whereas such functions are commonly used in numerous approximation algorithms. Moreover, a step sizing function, which encourages population diversity at earlier iterations while intensifying the search towards the end, is adopted in the proposed WSA. Thus, premature convergence and local optima problems are attempted to be avoided. In this context, the contribution of the present study is twofold: first, WSA is modified for stationary binary optimization problems, secondarily, it is further enhanced by the proposed step sizing function. The performance of the modified WSA is examined by using three well-known binary optimization problems, including uncapacitated facility location problem, 0–1 knapsack problem and a natural extension of it, the set union knapsack problem. As demonstrated by the comprehensive experimental study, results point out the efficiency of the proposed WSA modification in binary optimization problems. |
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| title_short |
Weighted superposition attraction algorithm for binary optimization problems |
| url |
https://dx.doi.org/10.1007/s12351-018-0427-9 |
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| author2 |
Ozsoydan, Fehmi Burcin Senol, M. Emre |
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| doi_str |
10.1007/s12351-018-0427-9 |
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2024-07-03T21:07:16.827Z |
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| score |
7.3991556 |