A new firefly algorithm with mean condition partial attraction
As compared with other optimization algorithms (e.g., genetic algorithm, ant colony algorithm, and particle swarm algorithm), FA is relatively simple to be realized. It does not require strict continuous and differentiable conditions, requires less prior knowledge. However, it still cannot effective...
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| Published in: | Applied intelligence (Dordrecht, Netherlands) Vol. 52; no. 4; pp. 4418 - 4431 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
Springer US
01.03.2022
Springer Nature B.V |
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| ISSN: | 0924-669X, 1573-7497 |
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| Abstract | As compared with other optimization algorithms (e.g., genetic algorithm, ant colony algorithm, and particle swarm algorithm), FA is relatively simple to be realized. It does not require strict continuous and differentiable conditions, requires less prior knowledge. However, it still cannot effectively avoid slow convergence and poor stability. To optimize FA for the attraction model, a new FA with mean condition partial attraction is proposed (mcFA) in this paper. McFA, characterized by fast computing power, high precision, and easy implementation, is capable of remedying the defect that the FA is easy to converge slowly. As opposed to standard FA, mcFA has determined excellent model parameter values, and the mean condition partial attraction model is more suitable for different dimensional solutions than the full attraction model. Lastly, as verified by the theoretical and experimental results, mcFA outperforms other algorithms on most of the test functions. Moreover, the mean condition partial attraction model is shown to yield better solutions than the full attraction model. |
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| AbstractList | As compared with other optimization algorithms (e.g., genetic algorithm, ant colony algorithm, and particle swarm algorithm), FA is relatively simple to be realized. It does not require strict continuous and differentiable conditions, requires less prior knowledge. However, it still cannot effectively avoid slow convergence and poor stability. To optimize FA for the attraction model, a new FA with mean condition partial attraction is proposed (mcFA) in this paper. McFA, characterized by fast computing power, high precision, and easy implementation, is capable of remedying the defect that the FA is easy to converge slowly. As opposed to standard FA, mcFA has determined excellent model parameter values, and the mean condition partial attraction model is more suitable for different dimensional solutions than the full attraction model. Lastly, as verified by the theoretical and experimental results, mcFA outperforms other algorithms on most of the test functions. Moreover, the mean condition partial attraction model is shown to yield better solutions than the full attraction model. |
| Author | Lai, Qiang Xu, Guang-Hui Zhang, Ting-Wei |
| Author_xml | – sequence: 1 givenname: Guang-Hui surname: Xu fullname: Xu, Guang-Hui organization: Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, School of Electrical and Electronics Engineering, Hubei University of Technology – sequence: 2 givenname: Ting-Wei surname: Zhang fullname: Zhang, Ting-Wei organization: Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, School of Electrical and Electronics Engineering, Hubei University of Technology – sequence: 3 givenname: Qiang orcidid: 0000-0002-7703-9793 surname: Lai fullname: Lai, Qiang email: laiqiang87@126.com organization: School of Electrical and Automation Engineering, East China Jiaotong University |
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| CitedBy_id | crossref_primary_10_1177_16878132221085125 crossref_primary_10_1016_j_ins_2022_11_164 crossref_primary_10_1016_j_asoc_2023_110158 crossref_primary_10_7717_peerj_cs_956 crossref_primary_10_1109_ACCESS_2022_3224924 crossref_primary_10_1007_s11227_021_04031_9 crossref_primary_10_1016_j_neucom_2022_05_100 crossref_primary_10_1007_s10489_021_02982_3 crossref_primary_10_3390_math9212705 crossref_primary_10_3390_su14095668 |
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| Title | A new firefly algorithm with mean condition partial attraction |
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