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 |
| Cites_doi | 10.1007/s00500-016-2104-3 10.1371/journal.pone.0112634 10.1016/j.future.2019.02.028 10.1016/j.future.2020.03.055 10.1007/s00500-016-2116-z 10.3390/en13071808 10.1007/s00607-018-0645-2 10.1504/IJCSM.2016.081701 10.1016/j.eswa.2015.08.054 10.1016/j.ins.2016.12.024 10.1016/j.cnsns.2012.06.009 10.1007/s13369-019-03759-0 10.1007/s40009-013-0129-z 10.1109/ACCESS.2021.3052960 10.1002/qre.1591 10.1016/j.swevo.2013.06.001 10.1016/j.jocs.2017.07.009 10.1109/CEC.2019.8789954 10.1007/s00521-015-1923-y 10.1109/ISCID.2013.90 10.1007/s00500-020-05569-1 10.1007/s10462-017-9568-0 10.1109/CEC.2016.7744067 10.1109/CEC.2019.8790273 10.1109/ICEEOT.2016.7755239 10.1007/s10462-016-9463-0 10.1007/s12293-016-0212-3 10.1109/CEC.2016.7743964 10.1109/IAEAC.2017.8054023 10.1142/SO217984920503224 10.1109/TCSII.2020.2990698 10.1109/CEC.2016.7744275 10.1109/CEC.2019.8789903 |
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| References | Gandomi, Yang, Talatahari (CR15) 2013; 18 Lai, Kuate, Liu (CR30) 2020; 67 CR18 Verma, Aggarwal, Patodi (CR20) 2016; 44 Wang, Zhou, Sun (CR14) 2017; 21 CR37 Xia, Gui, He (CR17) 2018; 26 CR13 CR35 Lin, Christine, Michael (CR39) 2015; 31 CR34 Fister, Fister, Yang (CR1) 2013; 13 Zhou, Ding, Ma (CR16) 2019; 101 CR33 Cheung, Ding, Shen (CR23) 2014; 9 Sedighizadeh, Masehian, Sedighizadeh (CR38) 2020; 179 Wahid, Ghazali, Ismail (CR42) 2019; 44 Alsghaier, Akour (CR40) 2020; 31 Xu, Xu, Ge (CR31) 2020; 13 CR4 Mauder, Sandera, Stetina (CR10) 2017; 45 Wang, Wang, Zhou (CR12) 2017; 2 CR6 Wang, Wang, Sun (CR32) 2017; 5 Li, Chen, Wang (CR5) 2020; 111 Wang, Wang, Zhou (CR11) 2017; 382 CR29 Yuan, Zhao, Liu (CR41) 2021; 9 Heidari, Mirjalili, Faris (CR9) 2019; 97 CR28 Nekouie, Yaghoobi (CR2) 2016; 46 Tilahun, Ngnotchouye, Hamadneh (CR3) 2019; 51 CR27 CR26 CR25 Yang, Chen (CR7) 2021; 117 CR24 CR21 Wang, Cui, Sun (CR36) 2017; 21 CR43 Ahmadianfar, Heidari, Gandomi (CR8) 2021; 181 Khajehzadeh, Taha, Eslami (CR22) 2013; 36 Yu (CR19) 2016; 7 2642_CR25 YT Yang (2642_CR7) 2021; 117 2642_CR24 F Wahid (2642_CR42) 2019; 44 AA Heidari (2642_CR9) 2019; 97 2642_CR21 2642_CR43 N Nekouie (2642_CR2) 2016; 46 AH Gandomi (2642_CR15) 2013; 18 X Xia (2642_CR17) 2018; 26 L Zhou (2642_CR16) 2019; 101 H Wang (2642_CR36) 2017; 21 CD Lin (2642_CR39) 2015; 31 JH Yuan (2642_CR41) 2021; 9 2642_CR4 2642_CR27 2642_CR26 2642_CR6 M Khajehzadeh (2642_CR22) 2013; 36 2642_CR29 2642_CR28 D Sedighizadeh (2642_CR38) 2020; 179 2642_CR34 2642_CR33 H Wang (2642_CR12) 2017; 2 2642_CR13 NJ Cheung (2642_CR23) 2014; 9 2642_CR35 H Alsghaier (2642_CR40) 2020; 31 I Ahmadianfar (2642_CR8) 2021; 181 SL Tilahun (2642_CR3) 2019; 51 Q Lai (2642_CR30) 2020; 67 H Wang (2642_CR14) 2017; 21 OP Verma (2642_CR20) 2016; 44 GH Xu (2642_CR31) 2020; 13 S Li (2642_CR5) 2020; 111 T Mauder (2642_CR10) 2017; 45 JI Fister (2642_CR1) 2013; 13 G Yu (2642_CR19) 2016; 7 H Wang (2642_CR32) 2017; 5 2642_CR37 2642_CR18 H Wang (2642_CR11) 2017; 382 |
| References_xml | – volume: 181 start-page: 256 issue: 2 year: 2021 end-page: 269 ident: CR8 article-title: RUN Beyond the metaphor: an efficient optimization algorithm based on Runge Kutta method publication-title: Expert Syst Appl – volume: 21 start-page: 5091 issue: 17 year: 2017 end-page: 5102 ident: CR14 article-title: Firefly algorithm with adaptive control parameters publication-title: Soft Comput doi: 10.1007/s00500-016-2104-3 – volume: 9 start-page: e112634 issue: 11 year: 2014 ident: CR23 article-title: Adaptive firefly algorithm: parameter analysis and its application publication-title: PLoS One doi: 10.1371/journal.pone.0112634 – volume: 97 start-page: 849 issue: 2 year: 2019 end-page: 872 ident: CR9 article-title: Harris hawks optimization: algorithm and applications publication-title: Fut Gener Comput Syst doi: 10.1016/j.future.2019.02.028 – volume: 51 start-page: 445 issue: 3 year: 2019 end-page: 492 ident: CR3 article-title: Continuous versions of firefly algorithm: a review publication-title: Artif Intell – volume: 45 start-page: 347 issue: 4 year: 2017 end-page: 350 ident: CR10 article-title: Optimization of the quality of continuously cast steel slabs using the firefly algorithm publication-title: Mater Technol – ident: CR18 – ident: CR43 – volume: 111 start-page: 300 issue: 2 year: 2020 end-page: 323 ident: CR5 article-title: Slime mould algorithm: a new method for stochastic optimization publication-title: Fut Gener Comput Syst doi: 10.1016/j.future.2020.03.055 – volume: 21 start-page: 5325 issue: 18 year: 2017 end-page: 5339 ident: CR36 article-title: Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism publication-title: Soft Comput doi: 10.1007/s00500-016-2116-z – ident: CR4 – volume: 67 start-page: 1129 issue: 6 year: 2020 end-page: 1133 ident: CR30 article-title: An extremely simple chaotic system with infinitely many coexisting attractors publication-title: IEEE Trans Circ Syst II: Express Briefs – ident: CR37 – ident: CR33 – volume: 13 start-page: 1808 issue: 7 year: 2020 end-page: 1822 ident: CR31 article-title: Distributed event-based control of hierarchical leader-follower networks with time-varying layer-to-layer delays publication-title: Energies doi: 10.3390/en13071808 – volume: 101 start-page: 477 issue: 5 year: 2019 end-page: 493 ident: CR16 article-title: An accurate partially attracted firefly algorithm publication-title: Comput doi: 10.1007/s00607-018-0645-2 – ident: CR35 – ident: CR6 – volume: 7 start-page: 530 issue: 6 year: 2016 end-page: 536 ident: CR19 article-title: An improved firefly algorithm based on probabilistic attraction publication-title: Inter J Comput Sci Math doi: 10.1504/IJCSM.2016.081701 – ident: CR29 – volume: 44 start-page: 168 year: 2016 end-page: 176 ident: CR20 article-title: Opposition and dimensional based modified firefly algorithm publication-title: Expert Sys Appli doi: 10.1016/j.eswa.2015.08.054 – volume: 117 start-page: 215 issue: 3 year: 2021 end-page: 227 ident: CR7 article-title: Hunger games search: visions, conception, implementation, deep analysis, perspectives, and towards performance shifts publication-title: Expert Syst Appl – volume: 382 start-page: 374 year: 2017 end-page: 387 ident: CR11 article-title: Firefly algorithm with neighborhood attraction publication-title: Inf Sci doi: 10.1016/j.ins.2016.12.024 – ident: CR25 – ident: CR27 – volume: 31 start-page: 155 issue: 2 year: 2020 end-page: 167 ident: CR40 article-title: Software fault prediction using whale algorithm with genetics algorithm publication-title: Soft Pra Exper – volume: 18 start-page: 89 issue: 1 year: 2013 end-page: 98 ident: CR15 article-title: Firefly algorithm with chaos publication-title: Commun Nonlinear Sci doi: 10.1016/j.cnsns.2012.06.009 – ident: CR21 – volume: 44 start-page: 4027 issue: 4 year: 2019 end-page: 4047 ident: CR42 article-title: Improved firefly algorithm based on genetic algorithm operators for energy efficiency in smart buildings publication-title: Ara J Sci Eng doi: 10.1007/s13369-019-03759-0 – volume: 5 start-page: 33 issue: 1 year: 2017 end-page: 41 ident: CR32 article-title: Firefly algorithm with random attraction publication-title: Inter J Bio Comput – volume: 36 start-page: 279 issue: 3 year: 2013 end-page: 288 ident: CR22 article-title: A new hybrid firefly algorithm for foundation optimization publication-title: National Acad Sci Lett doi: 10.1007/s40009-013-0129-z – volume: 179 start-page: 194 issue: 2 year: 2020 end-page: 212 ident: CR38 article-title: A new generalized particle swarm optimization algorithm publication-title: Math Comput Sim – ident: CR13 – volume: 9 start-page: 16623 year: 2021 end-page: 16629 ident: CR41 article-title: DMPPT Control of photovoltaic microgrid based on improved sparrow search algorithm publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3052960 – volume: 46 start-page: 267 issue: 2 year: 2016 end-page: 287 ident: CR2 article-title: A new method in multimodal optimization based on firefly algorithm publication-title: Artif Intell – ident: CR34 – volume: 31 start-page: 155 issue: 2 year: 2015 end-page: 167 ident: CR39 article-title: Using genetic algorithms to design experiments: a review publication-title: Qual Reliab Eng Int doi: 10.1002/qre.1591 – volume: 13 start-page: 34 year: 2013 end-page: 46 ident: CR1 article-title: A comprehensive review of firefly algorithms publication-title: Swarm Evol Comput doi: 10.1016/j.swevo.2013.06.001 – volume: 26 start-page: 488 year: 2018 end-page: 500 ident: CR17 article-title: A hybrid optimizer based on firefly algorithm and particle swarm optimization algorithm publication-title: J Comput Sci doi: 10.1016/j.jocs.2017.07.009 – ident: CR28 – ident: CR26 – ident: CR24 – volume: 2 start-page: 128 year: 2017 end-page: 134 ident: CR12 article-title: Dynamic step factor based firefly algorithm for optimization problems publication-title: IEEE Inter Confer Comput Sci Eng – volume: 45 start-page: 347 issue: 4 year: 2017 ident: 2642_CR10 publication-title: Mater Technol – ident: 2642_CR25 doi: 10.1109/CEC.2019.8789954 – ident: 2642_CR13 – ident: 2642_CR4 doi: 10.1007/s00521-015-1923-y – volume: 181 start-page: 256 issue: 2 year: 2021 ident: 2642_CR8 publication-title: Expert Syst Appl – volume: 2 start-page: 128 year: 2017 ident: 2642_CR12 publication-title: IEEE Inter Confer Comput Sci Eng – volume: 117 start-page: 215 issue: 3 year: 2021 ident: 2642_CR7 publication-title: Expert Syst Appl – ident: 2642_CR35 doi: 10.1109/ISCID.2013.90 – volume: 97 start-page: 849 issue: 2 year: 2019 ident: 2642_CR9 publication-title: Fut Gener Comput Syst doi: 10.1016/j.future.2019.02.028 – volume: 382 start-page: 374 year: 2017 ident: 2642_CR11 publication-title: Inf Sci doi: 10.1016/j.ins.2016.12.024 – volume: 13 start-page: 34 year: 2013 ident: 2642_CR1 publication-title: Swarm Evol Comput doi: 10.1016/j.swevo.2013.06.001 – volume: 21 start-page: 5325 issue: 18 year: 2017 ident: 2642_CR36 publication-title: Soft Comput doi: 10.1007/s00500-016-2116-z – volume: 44 start-page: 4027 issue: 4 year: 2019 ident: 2642_CR42 publication-title: Ara J Sci Eng doi: 10.1007/s13369-019-03759-0 – volume: 13 start-page: 1808 issue: 7 year: 2020 ident: 2642_CR31 publication-title: Energies doi: 10.3390/en13071808 – volume: 31 start-page: 155 issue: 2 year: 2020 ident: 2642_CR40 publication-title: Soft Pra Exper – ident: 2642_CR43 doi: 10.1007/s00500-020-05569-1 – volume: 51 start-page: 445 issue: 3 year: 2019 ident: 2642_CR3 publication-title: Artif Intell doi: 10.1007/s10462-017-9568-0 – ident: 2642_CR37 – ident: 2642_CR26 doi: 10.1109/CEC.2016.7744067 – volume: 179 start-page: 194 issue: 2 year: 2020 ident: 2642_CR38 publication-title: Math Comput Sim – volume: 101 start-page: 477 issue: 5 year: 2019 ident: 2642_CR16 publication-title: Comput doi: 10.1007/s00607-018-0645-2 – ident: 2642_CR28 doi: 10.1109/CEC.2019.8790273 – volume: 67 start-page: 1129 issue: 6 year: 2020 ident: 2642_CR30 publication-title: IEEE Trans Circ Syst II: Express Briefs – volume: 7 start-page: 530 issue: 6 year: 2016 ident: 2642_CR19 publication-title: Inter J Comput Sci Math doi: 10.1504/IJCSM.2016.081701 – volume: 5 start-page: 33 issue: 1 year: 2017 ident: 2642_CR32 publication-title: Inter J Bio Comput – ident: 2642_CR34 doi: 10.1109/ICEEOT.2016.7755239 – volume: 31 start-page: 155 issue: 2 year: 2015 ident: 2642_CR39 publication-title: Qual Reliab Eng Int doi: 10.1002/qre.1591 – volume: 46 start-page: 267 issue: 2 year: 2016 ident: 2642_CR2 publication-title: Artif Intell doi: 10.1007/s10462-016-9463-0 – ident: 2642_CR6 doi: 10.1007/s12293-016-0212-3 – volume: 36 start-page: 279 issue: 3 year: 2013 ident: 2642_CR22 publication-title: National Acad Sci Lett doi: 10.1007/s40009-013-0129-z – volume: 21 start-page: 5091 issue: 17 year: 2017 ident: 2642_CR14 publication-title: Soft Comput doi: 10.1007/s00500-016-2104-3 – ident: 2642_CR27 doi: 10.1109/CEC.2016.7743964 – ident: 2642_CR33 doi: 10.1109/IAEAC.2017.8054023 – volume: 18 start-page: 89 issue: 1 year: 2013 ident: 2642_CR15 publication-title: Commun Nonlinear Sci doi: 10.1016/j.cnsns.2012.06.009 – ident: 2642_CR18 doi: 10.1142/SO217984920503224 – volume: 111 start-page: 300 issue: 2 year: 2020 ident: 2642_CR5 publication-title: Fut Gener Comput Syst doi: 10.1016/j.future.2020.03.055 – volume: 26 start-page: 488 year: 2018 ident: 2642_CR17 publication-title: J Comput Sci doi: 10.1016/j.jocs.2017.07.009 – ident: 2642_CR21 doi: 10.1109/TCSII.2020.2990698 – ident: 2642_CR29 doi: 10.1109/CEC.2016.7744275 – volume: 9 start-page: e112634 issue: 11 year: 2014 ident: 2642_CR23 publication-title: PLoS One doi: 10.1371/journal.pone.0112634 – ident: 2642_CR24 doi: 10.1109/CEC.2019.8789903 – volume: 9 start-page: 16623 year: 2021 ident: 2642_CR41 publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3052960 – volume: 44 start-page: 168 year: 2016 ident: 2642_CR20 publication-title: Expert Sys Appli doi: 10.1016/j.eswa.2015.08.054 |
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| Title | A new firefly algorithm with mean condition partial attraction |
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