Colony search optimization algorithm using global optimization
Abstract This paper proposes a novel metaheuristic optimizer, named Colony Search Optimization Algorithm (CSOA). The algorithm mimics the social behavior of early humans. Early humans expanded their settlements in search of more livable places to live. In CSOA, the worst solution is used to escape f...
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Gespeichert in:
| Autor*in: |
Wen, Heng [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: The journal of supercomputing - Springer US, 1987, 78(2021), 5 vom: 22. Okt., Seite 6567-6611 |
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| Übergeordnetes Werk: |
volume:78 ; year:2021 ; number:5 ; day:22 ; month:10 ; pages:6567-6611 |
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| DOI / URN: |
10.1007/s11227-021-04127-2 |
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OLC2078292338 |
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| 520 | |a Abstract This paper proposes a novel metaheuristic optimizer, named Colony Search Optimization Algorithm (CSOA). The algorithm mimics the social behavior of early humans. Early humans expanded their settlements in search of more livable places to live. In CSOA, the worst solution is used to escape from local optima. And the number of these redundant solutions’ updates is reduced to improve the performance of the algorithm. CSOA is tested with 26 mathematical optimization problems and 4 classical engineering optimization problems. The optimization results are compared with those of various optimization algorithms. The experimental results show that the CSOA is able to provide very competitive results on most of the tested problems. Then, a new effective method is provided for solving optimization problems. | ||
| 650 | 4 | |a Heuristic algorithm | |
| 650 | 4 | |a Meta-heuristic algorithm | |
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| 700 | 1 | |a Wang, Su Xin |4 aut | |
| 700 | 1 | |a Lu, Fu Qiang |4 aut | |
| 700 | 1 | |a Feng, Ming |4 aut | |
| 700 | 1 | |a Wang, Lei Zhen |4 aut | |
| 700 | 1 | |a Xiong, Jun Kai |4 aut | |
| 700 | 1 | |a Si, Ma Cong |4 aut | |
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10.1007/s11227-021-04127-2 doi (DE-627)OLC2078292338 (DE-He213)s11227-021-04127-2-p DE-627 ger DE-627 rakwb eng 004 620 VZ Wen, Heng verfasserin (orcid)0000-0003-3322-6447 aut Colony search optimization algorithm using global optimization 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract This paper proposes a novel metaheuristic optimizer, named Colony Search Optimization Algorithm (CSOA). The algorithm mimics the social behavior of early humans. Early humans expanded their settlements in search of more livable places to live. In CSOA, the worst solution is used to escape from local optima. And the number of these redundant solutions’ updates is reduced to improve the performance of the algorithm. CSOA is tested with 26 mathematical optimization problems and 4 classical engineering optimization problems. The optimization results are compared with those of various optimization algorithms. The experimental results show that the CSOA is able to provide very competitive results on most of the tested problems. Then, a new effective method is provided for solving optimization problems. Heuristic algorithm Meta-heuristic algorithm Nature-inspired algorithm Constrained optimization CSOA Wang, Su Xin aut Lu, Fu Qiang aut Feng, Ming aut Wang, Lei Zhen aut Xiong, Jun Kai aut Si, Ma Cong aut Enthalten in The journal of supercomputing Springer US, 1987 78(2021), 5 vom: 22. Okt., Seite 6567-6611 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:78 year:2021 number:5 day:22 month:10 pages:6567-6611 https://doi.org/10.1007/s11227-021-04127-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 78 2021 5 22 10 6567-6611 |
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10.1007/s11227-021-04127-2 doi (DE-627)OLC2078292338 (DE-He213)s11227-021-04127-2-p DE-627 ger DE-627 rakwb eng 004 620 VZ Wen, Heng verfasserin (orcid)0000-0003-3322-6447 aut Colony search optimization algorithm using global optimization 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract This paper proposes a novel metaheuristic optimizer, named Colony Search Optimization Algorithm (CSOA). The algorithm mimics the social behavior of early humans. Early humans expanded their settlements in search of more livable places to live. In CSOA, the worst solution is used to escape from local optima. And the number of these redundant solutions’ updates is reduced to improve the performance of the algorithm. CSOA is tested with 26 mathematical optimization problems and 4 classical engineering optimization problems. The optimization results are compared with those of various optimization algorithms. The experimental results show that the CSOA is able to provide very competitive results on most of the tested problems. Then, a new effective method is provided for solving optimization problems. Heuristic algorithm Meta-heuristic algorithm Nature-inspired algorithm Constrained optimization CSOA Wang, Su Xin aut Lu, Fu Qiang aut Feng, Ming aut Wang, Lei Zhen aut Xiong, Jun Kai aut Si, Ma Cong aut Enthalten in The journal of supercomputing Springer US, 1987 78(2021), 5 vom: 22. Okt., Seite 6567-6611 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:78 year:2021 number:5 day:22 month:10 pages:6567-6611 https://doi.org/10.1007/s11227-021-04127-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 78 2021 5 22 10 6567-6611 |
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Abstract This paper proposes a novel metaheuristic optimizer, named Colony Search Optimization Algorithm (CSOA). The algorithm mimics the social behavior of early humans. Early humans expanded their settlements in search of more livable places to live. In CSOA, the worst solution is used to escape from local optima. And the number of these redundant solutions’ updates is reduced to improve the performance of the algorithm. CSOA is tested with 26 mathematical optimization problems and 4 classical engineering optimization problems. The optimization results are compared with those of various optimization algorithms. The experimental results show that the CSOA is able to provide very competitive results on most of the tested problems. Then, a new effective method is provided for solving optimization problems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Abstract This paper proposes a novel metaheuristic optimizer, named Colony Search Optimization Algorithm (CSOA). The algorithm mimics the social behavior of early humans. Early humans expanded their settlements in search of more livable places to live. In CSOA, the worst solution is used to escape from local optima. And the number of these redundant solutions’ updates is reduced to improve the performance of the algorithm. CSOA is tested with 26 mathematical optimization problems and 4 classical engineering optimization problems. The optimization results are compared with those of various optimization algorithms. The experimental results show that the CSOA is able to provide very competitive results on most of the tested problems. Then, a new effective method is provided for solving optimization problems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Abstract This paper proposes a novel metaheuristic optimizer, named Colony Search Optimization Algorithm (CSOA). The algorithm mimics the social behavior of early humans. Early humans expanded their settlements in search of more livable places to live. In CSOA, the worst solution is used to escape from local optima. And the number of these redundant solutions’ updates is reduced to improve the performance of the algorithm. CSOA is tested with 26 mathematical optimization problems and 4 classical engineering optimization problems. The optimization results are compared with those of various optimization algorithms. The experimental results show that the CSOA is able to provide very competitive results on most of the tested problems. Then, a new effective method is provided for solving optimization problems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Colony search optimization algorithm using global optimization |
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Wang, Su Xin Lu, Fu Qiang Feng, Ming Wang, Lei Zhen Xiong, Jun Kai Si, Ma Cong |
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Wang, Su Xin Lu, Fu Qiang Feng, Ming Wang, Lei Zhen Xiong, Jun Kai Si, Ma Cong |
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10.1007/s11227-021-04127-2 |
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