A Novel Hybrid Meta-Heuristic Algorithm Based on the Cross-Entropy Method and Firefly Algorithm for Global Optimization
Global optimization, especially on a large scale, is challenging to solve due to its nonlinearity and multimodality. In this paper, in order to enhance the global searching ability of the firefly algorithm (FA) inspired by bionics, a novel hybrid meta-heuristic algorithm is proposed by embedding the...
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| Published in | Entropy (Basel, Switzerland) Vol. 21; no. 5; p. 494 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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Basel
MDPI AG
14.05.2019
MDPI |
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| Online Access | Get full text |
| ISSN | 1099-4300 1099-4300 |
| DOI | 10.3390/e21050494 |
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| Abstract | Global optimization, especially on a large scale, is challenging to solve due to its nonlinearity and multimodality. In this paper, in order to enhance the global searching ability of the firefly algorithm (FA) inspired by bionics, a novel hybrid meta-heuristic algorithm is proposed by embedding the cross-entropy (CE) method into the firefly algorithm. With adaptive smoothing and co-evolution, the proposed method fully absorbs the ergodicity, adaptability and robustness of the cross-entropy method. The new hybrid algorithm achieves an effective balance between exploration and exploitation to avoid falling into a local optimum, enhance its global searching ability, and improve its convergence rate. The results of numeral experiments show that the new hybrid algorithm possesses more powerful global search capacity, higher optimization precision, and stronger robustness. |
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| AbstractList | Global optimization, especially on a large scale, is challenging to solve due to its nonlinearity and multimodality. In this paper, in order to enhance the global searching ability of the firefly algorithm (FA) inspired by bionics, a novel hybrid meta-heuristic algorithm is proposed by embedding the cross-entropy (CE) method into the firefly algorithm. With adaptive smoothing and co-evolution, the proposed method fully absorbs the ergodicity, adaptability and robustness of the cross-entropy method. The new hybrid algorithm achieves an effective balance between exploration and exploitation to avoid falling into a local optimum, enhance its global searching ability, and improve its convergence rate. The results of numeral experiments show that the new hybrid algorithm possesses more powerful global search capacity, higher optimization precision, and stronger robustness. Global optimization, especially on a large scale, is challenging to solve due to its nonlinearity and multimodality. In this paper, in order to enhance the global searching ability of the firefly algorithm (FA) inspired by bionics, a novel hybrid meta-heuristic algorithm is proposed by embedding the cross-entropy (CE) method into the firefly algorithm. With adaptive smoothing and co-evolution, the proposed method fully absorbs the ergodicity, adaptability and robustness of the cross-entropy method. The new hybrid algorithm achieves an effective balance between exploration and exploitation to avoid falling into a local optimum, enhance its global searching ability, and improve its convergence rate. The results of numeral experiments show that the new hybrid algorithm possesses more powerful global search capacity, higher optimization precision, and stronger robustness.Global optimization, especially on a large scale, is challenging to solve due to its nonlinearity and multimodality. In this paper, in order to enhance the global searching ability of the firefly algorithm (FA) inspired by bionics, a novel hybrid meta-heuristic algorithm is proposed by embedding the cross-entropy (CE) method into the firefly algorithm. With adaptive smoothing and co-evolution, the proposed method fully absorbs the ergodicity, adaptability and robustness of the cross-entropy method. The new hybrid algorithm achieves an effective balance between exploration and exploitation to avoid falling into a local optimum, enhance its global searching ability, and improve its convergence rate. The results of numeral experiments show that the new hybrid algorithm possesses more powerful global search capacity, higher optimization precision, and stronger robustness. |
| Author | Zhou, Benda Liu, Pei Li, Guocheng Le, Chengyi |
| AuthorAffiliation | 2 Institute of Financial Risk Intelligent Control and Prevention, West Anhui University, Lu’an 237012, China 1 School of Finance and Mathematics, West Anhui University, Lu’an 237012, China 4 School of Economic & Management, East China Jiaotong University, Nanchang 330013, China 3 College of Computer Science, Sichuan University, Chengdu 610065, China |
| AuthorAffiliation_xml | – name: 3 College of Computer Science, Sichuan University, Chengdu 610065, China – name: 1 School of Finance and Mathematics, West Anhui University, Lu’an 237012, China – name: 4 School of Economic & Management, East China Jiaotong University, Nanchang 330013, China – name: 2 Institute of Financial Risk Intelligent Control and Prevention, West Anhui University, Lu’an 237012, China |
| Author_xml | – sequence: 1 givenname: Guocheng surname: Li fullname: Li, Guocheng – sequence: 2 givenname: Pei surname: Liu fullname: Liu, Pei – sequence: 3 givenname: Chengyi surname: Le fullname: Le, Chengyi – sequence: 4 givenname: Benda surname: Zhou fullname: Zhou, Benda |
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| SubjectTerms | Adaptive algorithms Bionics co-evolution cross-entropy method Design engineering Entropy (Information theory) Evolution Evolutionary algorithms firefly algorithm Genetic algorithms Global optimization Heuristic Heuristic methods Industrial design Light meta-heuristic Methods Optimization algorithms Probability distribution Robustness Searching |
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| Title | A Novel Hybrid Meta-Heuristic Algorithm Based on the Cross-Entropy Method and Firefly Algorithm for Global Optimization |
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