{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:54:36Z","timestamp":1777704876669,"version":"3.51.4"},"reference-count":35,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,8,11]]},"abstract":"<jats:p>Firefly algorithm (FA) is one of most important nature-inspired algorithm based on swarm intelligence. Meanwhile, FA uses the full attraction model, which results too many unnecessary movements and reduces the efficiency of searching the optimal solution. To overcome these problems, this paper presents a new job, how the better fireflies move, which is always ignored. The novel algorithm is called multiple swarm strategy firefly algorithm (MSFFA), in which multiple swarm attraction model and status adaptively switch approach are proposed. It is characterized by employing the multiple swarm attraction model, which not only improves the efficiency of searching the optimal solution, but also quickly finds the better fireflies that move in free status. In addition, the novel approach defines that the fireflies followed different rules in different status, and can adaptively switch the status of fireflies between the original status and the free status to balance the exploration and the exploitation. To verify the robustness of MSFFA, it is compared with other improved FA variants on CEC2013. In one case of 30 dimension on 28 test functions, the proposed algorithm is significantly better than FA, DFA, PaFA, MFA, NaFA,and NSRaFA on 24, 23, 23, 17, 15, and 24 functions, respectively. The experimental results prove that MSFFA has obvious advantages over other FA variants.<\/jats:p>","DOI":"10.3233\/jifs-200619","type":"journal-article","created":{"date-parts":[[2021,3,23]],"date-time":"2021-03-23T13:43:22Z","timestamp":1616507002000},"page":"99-112","source":"Crossref","is-referenced-by-count":2,"title":["Enhancing firefly algorithm with multiple swarm strategy"],"prefix":"10.1177","volume":"41","author":[{"given":"Lianglin","family":"Cao","sequence":"first","affiliation":[{"name":"School of Electronics and Engineering, Naval University of Engineering, WuHan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kerong","family":"Ben","sequence":"additional","affiliation":[{"name":"School of Electronics and Engineering, Naval University of Engineering, WuHan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hu","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Jiujiang University, Jiujiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-200619_ref1","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.swevo.2013.06.001","article-title":"A comprehensive review of firefly algorithms","volume":"13","author":"Fister","year":"2013","journal-title":"Swarm Evol Comput"},{"issue":"1","key":"10.3233\/JIFS-200619_ref3","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.jocs.2017.07.010","article-title":"Enhancing differential evolution with random neighbors based strategy","volume":"26","author":"Peng","year":"2018","journal-title":"J Comput Sci"},{"issue":"1","key":"10.3233\/JIFS-200619_ref4","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.amc.2009.03.090","article-title":"A comparative study of artificial bee colony algorithm","volume":"214","author":"Karaboga","year":"2009","journal-title":"Appl Math Comput"},{"issue":"1","key":"10.3233\/JIFS-200619_ref5","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.aei.2004.07.001","article-title":"Ant colony optimization techniques for the vehicle routing problem","volume":"18","author":"Bell","year":"2004","journal-title":"Adv Engineering Inf"},{"issue":"5","key":"10.3233\/JIFS-200619_ref8","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1007\/s00521-018-3612-0","article-title":"Optimized feature selection algorithm based on fireflies with gravitational ant colony algorithm for big data predictive analytics","volume":"31","author":"AlFarraj","year":"2019","journal-title":"J Neural Comput Appl"},{"key":"10.3233\/JIFS-200619_ref9","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/j.asoc.2017.02.009","article-title":"Decomposition-based multi-objective firefly algorithm for RFID network planning with uncertainty","volume":"55","author":"Zhao","year":"2017","journal-title":"Appl Soft Comput"},{"key":"10.3233\/JIFS-200619_ref10","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/j.ins.2017.08.047","article-title":"A return-cost-based binary firefly algorithm for feature selection","volume":"418","author":"Zhang","year":"2017","journal-title":"Inf Sci"},{"key":"10.3233\/JIFS-200619_ref11","doi-asserted-by":"crossref","unstructured":"Apostolopoulos T. and Vlachos A. , Application of the firefly algorithm for solving the economic emissions load dispatch problem, Int J Combin 2011 (2010).","DOI":"10.1155\/2011\/523806"},{"key":"10.3233\/JIFS-200619_ref13","doi-asserted-by":"crossref","first-page":"922","DOI":"10.4028\/www.scientific.net\/AMM.575.922","article-title":"A hybrid discrete firefly algorithm for multi-objective flexible job shop scheduling problems with maintenance activity","volume":"575","author":"Karthikeyanl","year":"2014","journal-title":"Applied Mechanics Materials"},{"key":"10.3233\/JIFS-200619_ref14","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.swevo.2013.06.001","article-title":"A comprehensive review of firefly algorithms","volume":"13","author":"Fister","year":"2013","journal-title":"Swarm Evol Comput"},{"issue":"1","key":"10.3233\/JIFS-200619_ref15","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1504\/IJBIC.2016.074630","article-title":"Firefly algorithm with random attraction","volume":"8","author":"Wang","year":"2016","journal-title":"Int J Bio-Inspired Comput"},{"issue":"3","key":"10.3233\/JIFS-200619_ref16","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1145\/2480741.2480752","article-title":"Exploration and exploitation in evolutionary algorithms: A survey","volume":"45","author":"Crepinsek","year":"2013","journal-title":"ACM Comput Surv"},{"key":"10.3233\/JIFS-200619_ref17","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.jocs.2017.07.010","article-title":"Enhancing differential evolution with random neighbors based strategy","volume":"26","author":"Peng","year":"2018","journal-title":"J Comput Sci"},{"issue":"18","key":"10.3233\/JIFS-200619_ref19","doi-asserted-by":"crossref","first-page":"8723","DOI":"10.1007\/s00500-018-3473-6","article-title":"Best neighbor-guided artificial bee colony algorithm for continuous optimization problems","volume":"23","author":"Peng","year":"2019","journal-title":"Soft Comput"},{"key":"10.3233\/JIFS-200619_ref20","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.ins.2016.12.024","article-title":"Firefly algorithm with neighborhood attraction","volume":"382","author":"Wang","year":"2017","journal-title":"Inf Sci"},{"key":"10.3233\/JIFS-200619_ref21","first-page":"184","article-title":"A switch-mode firefly algorithm for global optimization,54 177\u201354","volume":"6","author":"Huang","year":"2018","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-200619_ref22","first-page":"128","article-title":"Dynamic step factor based firefly algorithm for optimization problems, in","volume":"1","author":"Wang","year":"2017","journal-title":"CSE. EUC,"},{"issue":"1","key":"10.3233\/JIFS-200619_ref23","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.cnsns.2012.06.009","article-title":"Firefly algorithm with chaos","volume":"18","author":"Gandomi","year":"2013","journal-title":"Commun Nonlinear Sci Numer Simul"},{"issue":"8","key":"10.3233\/JIFS-200619_ref24","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1007\/s11265-017-1278-y","article-title":"The firefly algorithm with gaussian disturbance and local search","volume":"90","author":"Lv","year":"2018","journal-title":"J Signal Process Syst"},{"key":"10.3233\/JIFS-200619_ref25","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.amc.2015.11.001","article-title":"A hybrid PSO-GA algorithm for constrained optimization problems","volume":"274","author":"Garg","year":"2016","journal-title":"J Applied Mathematics Computation"},{"key":"10.3233\/JIFS-200619_ref26","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/j.ins.2018.11.041","article-title":"A hybrid GSA-GA algorithm for constrained optimization problems","volume":"478","author":"Garg","year":"2019","journal-title":"J Information Sciences"},{"issue":"34","key":"10.3233\/JIFS-200619_ref27","first-page":"281","article-title":"Problem definitions and evaluation criteria for the cec special session on real-parameter optimization","volume":"201212","author":"Liang","year":"2013","journal-title":"Comput. Int. Labo, Zhengzhou. Uni, Zhengzhou, CN. Nanyang. Techn. Uni, Singapore, Tech. Report.,"},{"issue":"5","key":"10.3233\/JIFS-200619_ref28","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1007\/s00607-018-0645-2","article-title":"An accurate partially attracted firefly algorithm","volume":"101","author":"Zhou","year":"2019","journal-title":"Comput"},{"issue":"6","key":"10.3233\/JIFS-200619_ref29","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1504\/IJCSM.2016.081701","article-title":"An improved firefly algorithm based on probabilistic attraction","volume":"7","author":"Gan","year":"2016","journal-title":"Int J Comput Sci Math"},{"key":"10.3233\/JIFS-200619_ref30","doi-asserted-by":"crossref","first-page":"120189","DOI":"10.1109\/ACCESS.2019.2937136","article-title":"Firefly Algorithm With Luciferase Inhibition Mechanism","volume":"7","author":"Peng","year":"2019","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-200619_ref32","doi-asserted-by":"crossref","unstructured":"Arora S. and Singh S. , The firefly optimization algorithm: convergence analysis and parameter selection, Int J Comput Appli 69(3) (2013).","DOI":"10.5120\/11826-7528"},{"key":"10.3233\/JIFS-200619_ref33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.swevo.2016.12.005","article-title":"A survey of swarm intelligence for dynamic optimization: Algorithms and applications","volume":"33","author":"Mavrovouniotis","year":"2017","journal-title":"Swarm Evolu Computa"},{"issue":"7","key":"10.3233\/JIFS-200619_ref34","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1007\/s00607-015-0456-7","article-title":"Enhancing firefly algorithm using generalized opposition-based learning","volume":"97","author":"Yu","year":"2015","journal-title":"Comput"},{"issue":"1","key":"10.3233\/JIFS-200619_ref35","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1504\/IJICA.2019.100535","article-title":"Gaussian bare-bones firefly algorithm","volume":"10","author":"Peng","year":"2019","journal-title":"Int J Innova Comput Appl"},{"key":"10.3233\/JIFS-200619_ref36","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.asoc.2019.03.010","article-title":"A novel firefly algorithm based on gender difference and its convergence","volume":"80","author":"Wang","year":"2019","journal-title":"Appl Soft Comput"},{"key":"10.3233\/JIFS-200619_ref37","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1016\/j.energy.2017.10.052","article-title":"A novel TVAC-PSO based mutation strategies algorithm for generation scheduling of pumped storage hydrothermal system incorporating solar units","volume":"142","author":"Patwal","year":"2018","journal-title":"Energy"},{"key":"10.3233\/JIFS-200619_ref38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.swevo.2015.05.001","article-title":"An efficient biogeography based optimization algorithm for solving reliability optimization problems","volume":"24","author":"Garg","year":"2015","journal-title":"J Swarm and Evolutionary Computation"},{"issue":"3","key":"10.3233\/JIFS-200619_ref41","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1504\/IJWMC.2017.088529","article-title":"Firefly algorithm with dynamic attractiveness model and its application on wireless sensor networks","volume":"13","author":"Wang","year":"2017","journal-title":"Int J Wire Mob Comput"},{"issue":"18","key":"10.3233\/JIFS-200619_ref42","doi-asserted-by":"crossref","first-page":"5325","DOI":"10.1007\/s00500-016-2116-z","article-title":"Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism","volume":"21","author":"Wang","year":"2017","journal-title":"Soft Comput"},{"issue":"1\u20133","key":"10.3233\/JIFS-200619_ref43","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/S0924-0136(98)00079-X","article-title":"Design optimization of cutting parameters for turning operations based on the Taguchi method","volume":"84","author":"Yang","year":"1998","journal-title":"J Materials Pro Technology"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-200619","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:42:14Z","timestamp":1777455734000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-200619"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,11]]},"references-count":35,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3233\/jifs-200619","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,11]]}}}