{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T12:31:17Z","timestamp":1780489877437,"version":"3.54.1"},"reference-count":85,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,10,4]],"date-time":"2021-10-04T00:00:00Z","timestamp":1633305600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,10,4]],"date-time":"2021-10-04T00:00:00Z","timestamp":1633305600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1007\/s11227-021-04100-z","type":"journal-article","created":{"date-parts":[[2021,10,5]],"date-time":"2021-10-05T03:39:57Z","timestamp":1633405197000},"page":"5712-5743","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Corona virus optimization (CVO): a novel optimization algorithm inspired from the Corona virus pandemic"],"prefix":"10.1007","volume":"78","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0139-5051","authenticated-orcid":false,"given":"Alireza","family":"Salehan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6441-9455","authenticated-orcid":false,"given":"Arash","family":"Deldari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,10,4]]},"reference":[{"key":"4100_CR1","doi-asserted-by":"publisher","unstructured":"Yang XS, Cui Z, Xiao R, Gandomi AH, Karamanoglu M (2013)\u00a0Swarm intelligence and bio-inspired computation: theory and applications. Newnes, Elsevier, London. https:\/\/doi.org\/10.1016\/B978-0-12-405163-8.00020-X","DOI":"10.1016\/B978-0-12-405163-8.00020-X"},{"key":"4100_CR2","doi-asserted-by":"publisher","unstructured":"Yang XS (2014) Nature-inspired optimization algorithms. Academic Press, Elsevier, London. https:\/\/doi.org\/10.1016\/C2013-0-01368-0","DOI":"10.1016\/C2013-0-01368-0"},{"issue":"4","key":"4100_CR3","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1007\/s00158-013-0996-4","volume":"49","author":"GR Zavala","year":"2014","unstructured":"Zavala GR, Nebro AJ, Luna F, Coello CAC (2014) A survey of multi-objective metaheuristics applied to structural optimization. Struct Multidiscip Optim 49(4):537\u2013558. https:\/\/doi.org\/10.1007\/s00158-013-0996-4","journal-title":"Struct Multidiscip Optim"},{"issue":"4","key":"4100_CR4","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1007\/s12559-018-9554-0","volume":"10","author":"D Molina","year":"2018","unstructured":"Molina D, LaTorre A, Herrera F (2018) An insight into bio-inspired and evolutionary algorithms for global optimization: review, analysis, and lessons learnt over a decade of competitions. Cogn Comput 10(4):517\u2013544. https:\/\/doi.org\/10.1007\/s12559-018-9554-0","journal-title":"Cogn Comput"},{"issue":"6","key":"4100_CR5","doi-asserted-by":"publisher","first-page":"881","DOI":"10.1016\/j.comnet.2009.10.024","volume":"54","author":"F Dressler","year":"2010","unstructured":"Dressler F, Akan OB (2010) A survey on bio-inspired networking. Comput Netw 54(6):881\u2013900. https:\/\/doi.org\/10.1016\/j.comnet.2009.10.024","journal-title":"Comput Netw"},{"issue":"8","key":"4100_CR6","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1016\/j.cose.2011.08.009","volume":"30","author":"C Kolias","year":"2011","unstructured":"Kolias C, Kambourakis G, Maragoudakis M (2011) Swarm intelligence in intrusion detection: a survey. Comput Secur 30(8):625\u2013642. https:\/\/doi.org\/10.1016\/j.cose.2011.08.009","journal-title":"Comput Secur"},{"key":"4100_CR7","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/B978-0-12-405163-8.00018-1","volume-title":"Swarm intelligence and bio-inspired computation: theory and applications","author":"S Fong","year":"2013","unstructured":"Fong S (2013) Opportunities and challenges of integrating bio-inspired optimization and data mining algorithms. In: Yang XS, Cui Z, Xiao R, Gandomi AH, Karamanoglu M (eds) Swarm intelligence and bio-inspired computation: theory and applications. Newnes, Elsevier, London, pp 385\u2013402. https:\/\/doi.org\/10.1016\/B978-0-12-405163-8.00018-1"},{"issue":"1","key":"4100_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2791121","volume":"48","author":"B Alsalibi","year":"2015","unstructured":"Alsalibi B, Venkat I, Subramanian K, Lutfi SL, Wilde PD (2015) The impact of bio-inspired approaches toward the advancement of face recognition. ACM Comput Surv (CSUR) 48(1):1\u201333. https:\/\/doi.org\/10.1145\/2791121","journal-title":"ACM Comput Surv (CSUR)"},{"key":"4100_CR9","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.asoc.2015.12.001","volume":"41","author":"A Jose-Garcia","year":"2016","unstructured":"Jose-Garcia A, Gomez-Flores W (2016) Automatic clustering using nature-inspired metaheuristics: a survey. Appl Soft Comput 41:192\u2013213. https:\/\/doi.org\/10.1016\/j.asoc.2015.12.001","journal-title":"Appl Soft Comput"},{"issue":"2","key":"4100_CR10","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1109\/TEVC.2007.896686","volume":"12","author":"Y Del Valle","year":"2008","unstructured":"Del Valle Y, Venayagamoorthy GK, Mohagheghi S, Hernandez JC, Harley RG (2008) Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans Evol Comput 12(2):171\u2013195. https:\/\/doi.org\/10.1109\/TEVC.2007.896686","journal-title":"IEEE Trans Evol Comput"},{"key":"4100_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/B978-0-12-801538-4.00001-X","volume-title":"Bio-inspired computation in telecommunications","author":"XS Yang","year":"2015","unstructured":"Yang XS, Chien SF, Ting TO (2015) Bio-inspired computation and optimization: an overview. In: Yang XS, Chien SF, Ting TO (eds) Bio-inspired computation in telecommunications. Morgan Kaufmann, Elsevier, pp 1\u201321. https:\/\/doi.org\/10.1016\/B978-0-12-801538-4.00001-X"},{"key":"4100_CR12","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1007\/978-3-642-58069-7_38","volume-title":"Robots and biological systems: Towards a new bionics?","author":"G Beni","year":"1993","unstructured":"Beni G, Wang J (1993) Swarm intelligence in cellular robotic systems. In: Dario P, Sandini G, Aebischer P (eds) Robots and biological systems: Towards a new bionics? Springer, Berlin, Heidelberg, pp 703\u2013712. https:\/\/doi.org\/10.1007\/978-3-642-58069-7_38"},{"issue":"2","key":"4100_CR13","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1109\/TITS.2019.2897377","volume":"21","author":"J Del Ser","year":"2019","unstructured":"Del Ser J, Osaba E, Sanchez-Medina JJ, Fister I (2019) Bioinspired computational intelligence and transportation systems: a long road ahead. IEEE Trans Intell Transp Syst 21(2):466\u2013495. https:\/\/doi.org\/10.1109\/TITS.2019.2897377","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"4100_CR14","unstructured":"World Health Organization (2020) Coronavirus. World Health Organization. https:\/\/www.who.int\/health-topics\/coronavirus. Accessed 19 May 2020"},{"key":"4100_CR15","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.1056\/NEJMoa2001316","volume":"382","author":"Q Li","year":"2020","unstructured":"Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, Ren R, Leung KS, Lau EH, Wong JY, Xing X (2020) Early transmission dynamics in Wuhan, China, of novel coronavirus\u2013infected pneumonia. N Engl J Med 382:1199\u20131207. https:\/\/doi.org\/10.1056\/NEJMoa2001316","journal-title":"N Engl J Med"},{"issue":"1","key":"4100_CR16","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1109\/3477.484436","volume":"26","author":"M Dorigo","year":"1996","unstructured":"Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern 26(1):29\u201341","journal-title":"IEEE Trans Syst Man Cybern"},{"issue":"3","key":"4100_CR17","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/s10898-007-9149-x","volume":"39","author":"D Karaboga","year":"2007","unstructured":"Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39(3):459\u2013471. https:\/\/doi.org\/10.1007\/s10898-007-9149-x","journal-title":"J Global Optim"},{"key":"4100_CR18","doi-asserted-by":"publisher","unstructured":"Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: MHS'95, the sixth international IEEE symposium on micro machine and human science, pp 39\u201343. https:\/\/doi.org\/10.1109\/MHS.1995.494215","DOI":"10.1109\/MHS.1995.494215"},{"key":"4100_CR19","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/978-3-642-12538-6_6","volume-title":"Nature-inspired cooperative strategies for optimization (NICSO 2010)","author":"XS Yang","year":"2010","unstructured":"Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Gonzalez JR, Sancho-Royo A, Pelta DA, Cruz C (eds) Nature-inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, Heidelberg, pp 65\u201374. https:\/\/doi.org\/10.1007\/978-3-642-12538-6_6"},{"key":"4100_CR20","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/978-3-642-04944-6_14","volume-title":"Stochastic algorithms: foundations and applications","author":"XS Yang","year":"2009","unstructured":"Yang XS (2009) Firefly algorithms for multimodal optimization. In: Watanabe O, Zeugmann T (eds) Stochastic algorithms: foundations and applications. Springer, Berlin, Heidelberg, pp 169\u2013178. https:\/\/doi.org\/10.1007\/978-3-642-04944-6_14"},{"key":"4100_CR21","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.advengsoft.2017.01.004","volume":"105","author":"S Saremi","year":"2017","unstructured":"Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30\u201347. https:\/\/doi.org\/10.1016\/j.advengsoft.2017.01.004","journal-title":"Adv Eng Softw"},{"key":"4100_CR22","doi-asserted-by":"publisher","unstructured":"Kumar A, Misra RK, Singh D (2015) Butterfly optimizer. In: 2015 IEEE workshop on computational intelligence: theories, applications and future directions (WCI). Kanpur, India, p. 1\u20136. https:\/\/doi.org\/10.1109\/WCI.2015.7495523","DOI":"10.1109\/WCI.2015.7495523"},{"issue":"4","key":"4100_CR23","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1007\/s00521-015-1920-1","volume":"27","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053\u20131073. https:\/\/doi.org\/10.1007\/s00521-015-1920-1","journal-title":"Neural Comput Appl"},{"key":"4100_CR24","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46\u201361. https:\/\/doi.org\/10.1016\/j.advengsoft.2013.12.007","journal-title":"Adv Eng Softw"},{"key":"4100_CR25","doi-asserted-by":"publisher","unstructured":"Shiqin Y, Jianjun J, Guangxing Y (2009) A dolphin partner optimization. In: 2009 WRI global congress on intelligent systems (IEEE). Xiamen, China, pp 124\u2013128. https:\/\/doi.org\/10.1109\/GCIS.2009.464","DOI":"10.1109\/GCIS.2009.464"},{"key":"4100_CR26","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","volume":"95","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51\u201367. https:\/\/doi.org\/10.1016\/j.advengsoft.2016.01.008","journal-title":"Adv Eng Softw"},{"issue":"5","key":"4100_CR27","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1109\/41.538609","volume":"43","author":"KF Man","year":"1996","unstructured":"Man KF, Tang KS, Kwong S (1996) Genetic algorithms: concepts and applications [in engineering design]. IEEE Trans Industr Electron 43(5):519\u2013534. https:\/\/doi.org\/10.1109\/41.538609","journal-title":"IEEE Trans Industr Electron"},{"issue":"4","key":"4100_CR28","doi-asserted-by":"publisher","first-page":"1284","DOI":"10.1016\/j.asoc.2010.05.011","volume":"10","author":"A Farasat","year":"2010","unstructured":"Farasat A, Menhaj MB, Mansouri T, Moghadam MRS (2010) ARO: a new model-free optimization algorithm inspired from asexual reproduction. Appl Soft Comput 10(4):1284\u20131292. https:\/\/doi.org\/10.1016\/j.asoc.2010.05.011","journal-title":"Appl Soft Comput"},{"issue":"4","key":"4100_CR29","doi-asserted-by":"publisher","first-page":"1213","DOI":"10.1016\/j.cnsns.2013.08.027","volume":"19","author":"A Askarzadeh","year":"2014","unstructured":"Askarzadeh A (2014) Bird mating optimizer: an optimization algorithm inspired by bird mating strategies. Commun Nonlinear Sci Numer Simul 19(4):1213\u20131228. https:\/\/doi.org\/10.1016\/j.cnsns.2013.08.027","journal-title":"Commun Nonlinear Sci Numer Simul"},{"key":"4100_CR30","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/739768","author":"S Salcedo-Sanz","year":"2014","unstructured":"Salcedo-Sanz S, Del Ser J, Landa-Torres I, Gil-Lopez S, Portilla-Figueras JA (2014) The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems. Sci World J. https:\/\/doi.org\/10.1155\/2014\/739768","journal-title":"Sci World J"},{"issue":"4","key":"4100_CR31","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn R, Price K (1997) Differential evolution\u2013a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341\u2013359. https:\/\/doi.org\/10.1023\/A:1008202821328","journal-title":"J Global Optim"},{"issue":"3","key":"4100_CR32","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1023\/A:1021207331209","volume":"115","author":"Y Liu","year":"2002","unstructured":"Liu Y, Passino KM (2002) Biomimicry of social foraging bacteria for distributed optimization: models, principles, and emergent behaviors. J Optim Theory Appl 115(3):603\u2013628. https:\/\/doi.org\/10.1023\/A:1021207331209","journal-title":"J Optim Theory Appl"},{"issue":"12","key":"4100_CR33","doi-asserted-by":"publisher","first-page":"4831","DOI":"10.1016\/j.cnsns.2012.05.010","volume":"17","author":"AH Gandomi","year":"2012","unstructured":"Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831\u20134845. https:\/\/doi.org\/10.1016\/j.cnsns.2012.05.010","journal-title":"Commun Nonlinear Sci Numer Simul"},{"key":"4100_CR34","doi-asserted-by":"publisher","unstructured":"Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE congress on evolutionary computation. Singapore, pp 4661\u20134667. https:\/\/doi.org\/10.1109\/CEC.2007.4425083","DOI":"10.1109\/CEC.2007.4425083"},{"key":"4100_CR35","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/978-3-642-21515-5_36","volume-title":"Advances in swarm intelligence (ICSI 2011) lecture notes in computer science","author":"Y Shi","year":"2011","unstructured":"Shi Y (2011) Brain storm optimization algorithm. In: Tan Y, Shi Y, Chai Y, Wang G (eds) Advances in swarm intelligence (ICSI 2011) lecture notes in computer science. Springer, Berlin, Heidelberg, pp 303\u2013309"},{"issue":"4","key":"4100_CR36","doi-asserted-by":"publisher","first-page":"199","DOI":"10.5923\/j.eee.20120204.05","volume":"2","author":"H Shayeghi","year":"2012","unstructured":"Shayeghi H, Dadashpour J (2012) Anarchic society optimization based PID control of an automatic voltage regulator (AVR) system. Electr Electron Eng 2(4):199\u2013207. https:\/\/doi.org\/10.5923\/j.eee.20120204.05","journal-title":"Electr Electron Eng"},{"issue":"1","key":"4100_CR37","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1007\/s00521-016-2379-4","volume":"28","author":"TT Huan","year":"2017","unstructured":"Huan TT, Kulkarni AJ, Kanesan J, Huang CJ, Abraham A (2017) Ideology algorithm: a socio-inspired optimization methodology. Neural Comput Appl 28(1):845\u2013876. https:\/\/doi.org\/10.1007\/s00521-016-2379-4","journal-title":"Neural Comput Appl"},{"issue":"4","key":"4100_CR38","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1016\/j.ecoinf.2006.07.003","volume":"1","author":"AR Mehrabian","year":"2006","unstructured":"Mehrabian AR, Lucas C (2006) A novel numerical optimization algorithm inspired from weed colonization. Eco Inform 1(4):355\u2013366. https:\/\/doi.org\/10.1016\/j.ecoinf.2006.07.003","journal-title":"Eco Inform"},{"key":"4100_CR39","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/627416","author":"M Sulaiman","year":"2014","unstructured":"Sulaiman M, Salhi A, Selamoglu BI, Kirikchi OB (2014) A plant propagation algorithm for constrained engineering optimisation problems. Math Probl Eng. https:\/\/doi.org\/10.1155\/2014\/627416","journal-title":"Math Probl Eng"},{"key":"4100_CR40","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1007\/978-3-642-32894-7_27","volume-title":"Unconventional computation and natural computation (UCNC 2012) lecture notes in computer science","author":"XS Yang","year":"2012","unstructured":"Yang XS (2012) Flower pollination algorithm for global optimization. In: Durand-Lose J, Jonoska N (eds) Unconventional computation and natural computation (UCNC 2012) lecture notes in computer science. Springer, Berlin, Heidelberg, pp 240\u2013249. https:\/\/doi.org\/10.1007\/978-3-642-32894-7_27"},{"key":"4100_CR41","doi-asserted-by":"publisher","unstructured":"Zhao Z, Cui Z, Zeng J, Yue X (2011) Artificial plant optimization algorithm for constrained optimization problems. In: 2011 Second international IEEE conference on innovations in bio-inspired computing and applications. Shenzhen, China, pp. 120\u2013123. https:\/\/doi.org\/10.1109\/IBICA.2011.34","DOI":"10.1109\/IBICA.2011.34"},{"issue":"15","key":"4100_CR42","doi-asserted-by":"publisher","first-page":"6676","DOI":"10.1016\/j.eswa.2014.05.009","volume":"41","author":"M Ghaemi","year":"2014","unstructured":"Ghaemi M, Feizi-Derakhshi MR (2014) Forest optimization algorithm. Expert Syst Appl 41(15):6676\u20136687. https:\/\/doi.org\/10.1016\/j.eswa.2014.05.009","journal-title":"Expert Syst Appl"},{"key":"4100_CR43","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1016\/j.engappai.2018.04.021","volume":"72","author":"A Cheraghalipour","year":"2018","unstructured":"Cheraghalipour A, Hajiaghaei-Keshteli M, Paydar MM (2018) Tree growth algorithm (TGA): a novel approach for solving optimization problems. Eng Appl Artif Intell 72:393\u2013414. https:\/\/doi.org\/10.1016\/j.engappai.2018.04.021","journal-title":"Eng Appl Artif Intell"},{"issue":"4598","key":"4100_CR44","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1126\/science.220.4598.671","volume":"220","author":"S Kirkpatrick","year":"1983","unstructured":"Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671\u2013680. https:\/\/doi.org\/10.1126\/science.220.4598.671","journal-title":"Science"},{"issue":"21","key":"4100_CR45","doi-asserted-by":"publisher","first-page":"5663","DOI":"10.1016\/j.ces.2005.05.028","volume":"60","author":"R Irizarry","year":"2005","unstructured":"Irizarry R (2005) A generalized framework for solving dynamic optimization problems using the artificial chemical process paradigm: applications to particulate processes and discrete dynamic systems. Chem Eng Sci 60(21):5663\u20135681. https:\/\/doi.org\/10.1016\/j.ces.2005.05.028","journal-title":"Chem Eng Sci"},{"issue":"8","key":"4100_CR46","doi-asserted-by":"publisher","first-page":"3185","DOI":"10.1016\/j.eswa.2012.12.032","volume":"40","author":"P Melin","year":"2013","unstructured":"Melin P, Astudillo L, Castillo O, Valdez F, Garcia M (2013) Optimal design of type-2 and type-1 fuzzy tracking controllers for autonomous mobile robots under perturbed torques using a new chemical optimization paradigm. Expert Syst Appl 40(8):3185\u20133195. https:\/\/doi.org\/10.1016\/j.eswa.2012.12.032","journal-title":"Expert Syst Appl"},{"key":"4100_CR47","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.advengsoft.2017.03.014","volume":"110","author":"A Kaveh","year":"2017","unstructured":"Kaveh A, Dadras A (2017) A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw 110:69\u201384. https:\/\/doi.org\/10.1016\/j.advengsoft.2017.03.014","journal-title":"Adv Eng Softw"},{"issue":"3","key":"4100_CR48","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1023\/A:1022452626305","volume":"25","author":"SI Birbil","year":"2003","unstructured":"Birbil SI, Fang SC (2003) An electromagnetism-like mechanism for global optimization. J Global Optim 25(3):263\u2013282. https:\/\/doi.org\/10.1023\/A:1022452626305","journal-title":"J Global Optim"},{"key":"4100_CR49","doi-asserted-by":"publisher","unstructured":"Xie L, Zeng J, Cui Z (2009) General framework of artificial physics optimization algorithm. In: 2009 IEEE world congress on nature and biologically inspired computing (NaBIC). Coimbatore, India, pp 1321\u20131326. https:\/\/doi.org\/10.1109\/NABIC.2009.5393736","DOI":"10.1109\/NABIC.2009.5393736"},{"key":"4100_CR50","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.swevo.2015.07.002","volume":"26","author":"H Abedinpourshotorban","year":"2016","unstructured":"Abedinpourshotorban H, Shamsuddin SM, Beheshti Z, Jawawi DN (2016) Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm. Swarm Evol Comput 26:8\u201322. https:\/\/doi.org\/10.1016\/j.swevo.2015.07.002","journal-title":"Swarm Evol Comput"},{"key":"4100_CR51","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.swevo.2019.03.013","volume":"48","author":"A Yadav","year":"2019","unstructured":"Yadav A (2019) AEFA: artificial electric field algorithm for global optimization. Swarm Evol Comput 48:93\u2013108. https:\/\/doi.org\/10.1016\/j.swevo.2019.03.013","journal-title":"Swarm Evol Comput"},{"key":"4100_CR52","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.jocs.2016.12.010","volume":"19","author":"SHA Kaboli","year":"2017","unstructured":"Kaboli SHA, Selvaraj J, Rahim NA (2017) Rain-fall optimization algorithm: a population based algorithm for solving constrained optimization problems. J Comput Sci 19:31\u201342. https:\/\/doi.org\/10.1016\/j.jocs.2016.12.010","journal-title":"J Comput Sci"},{"key":"4100_CR53","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.ins.2012.08.023","volume":"222","author":"A Hatamlou","year":"2013","unstructured":"Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222:175\u2013184. https:\/\/doi.org\/10.1016\/j.ins.2012.08.023","journal-title":"Inf Sci"},{"issue":"1\u20132","key":"4100_CR54","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1504\/IJCSE.2011.041221","volume":"6","author":"H Shah-Hosseini","year":"2011","unstructured":"Shah-Hosseini H (2011) Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation. Int J Comput Sci Eng 6(1\u20132):132\u2013140. https:\/\/doi.org\/10.1504\/IJCSE.2011.041221","journal-title":"Int J Comput Sci Eng"},{"key":"4100_CR55","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1007\/978-1-4615-1507-4_27","volume-title":"Essays and surveys in metaheuristics operations research\/computer science interfaces series","author":"ED Taillard","year":"2002","unstructured":"Taillard ED, Voss S (2002) POPMUSIC\u2014Partial optimization metaheuristic under special intensification conditions. Essays and surveys in metaheuristics operations research\/computer science interfaces series. Springer, Boston, MA, pp 613\u2013629. https:\/\/doi.org\/10.1007\/978-1-4615-1507-4_27"},{"issue":"36\u201338","key":"4100_CR56","doi-asserted-by":"publisher","first-page":"3902","DOI":"10.1016\/j.cma.2004.09.007","volume":"194","author":"KS Lee","year":"2005","unstructured":"Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194(36\u201338):3902\u20133933. https:\/\/doi.org\/10.1016\/j.cma.2004.09.007","journal-title":"Comput Methods Appl Mech Eng"},{"issue":"1","key":"4100_CR57","first-page":"25","volume":"4","author":"HD Purnomo","year":"2014","unstructured":"Purnomo HD (2014) Soccer game optimization: fundamental concept. Jurnal Sistem Komputer 4(1):25\u201336","journal-title":"Jurnal Sistem Komputer"},{"issue":"1","key":"4100_CR58","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1007\/s10489-013-0512-y","volume":"41","author":"E Osaba","year":"2014","unstructured":"Osaba E, Diaz F, Onieva E (2014) Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts. Appl Intell 41(1):145\u2013166. https:\/\/doi.org\/10.1007\/s10489-013-0512-y","journal-title":"Appl Intell"},{"issue":"4","key":"4100_CR59","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1007\/s40313-016-0242-6","volume":"27","author":"N Razmjooy","year":"2016","unstructured":"Razmjooy N, Khalilpour M, Ramezani M (2016) A new meta-heuristic optimization algorithm inspired by FIFA world cup competitions: theory and its application in PID designing for AVR system. J Control Auto Electr Sys 27(4):419\u2013440. https:\/\/doi.org\/10.1007\/s40313-016-0242-6","journal-title":"J Control Auto Electr Sys"},{"key":"4100_CR60","unstructured":"Juarez JRC, Wang HJ, Lai YC, Liang YC (2009) Virus optimization algorithm (VOA): A novel metaheuristic for solving continuous optimization problems. In: 2009 Asia pacific industrial engineering and management systems conference (APIEMS 2009), pp 2166\u20132174."},{"key":"4100_CR61","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.advengsoft.2015.11.004","volume":"92","author":"MD Li","year":"2016","unstructured":"Li MD, Zhao H, Weng XW, Han T (2016) A novel nature-inspired algorithm for optimization: virus colony search. Adv Eng Softw 92:65\u201388. https:\/\/doi.org\/10.1016\/j.advengsoft.2015.11.004","journal-title":"Adv Eng Softw"},{"key":"4100_CR62","doi-asserted-by":"publisher","unstructured":"Chen TC, Tsai PW, Chu SC, Pan JS (2007) A novel optimization approach: bacterial-GA foraging. In: Second international IEEE conference on innovative computing, information and control (ICICIC 2007). Kumamoto, Japan, pp 391. https:\/\/doi.org\/10.1109\/ICICIC.2007.67","DOI":"10.1109\/ICICIC.2007.67"},{"key":"4100_CR63","doi-asserted-by":"publisher","unstructured":"Chu Y, Mi H, Liao H, Ji Z, Wu QH (2008) A fast bacterial swarming algorithm for high-dimensional function optimization. In: 2008 IEEE congress on evolutionary computation (IEEE world congress on computational intelligence). Hong Kong, China, pp 3135\u20133140. https:\/\/doi.org\/10.1109\/CEC.2008.4631222","DOI":"10.1109\/CEC.2008.4631222"},{"key":"4100_CR64","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/978-3-642-01085-9_2","volume-title":"Foundations of computational intelligence studies in computational intelligence","author":"S Das","year":"2009","unstructured":"Das S, Biswas A, Dasgupta S, Abraham A (2009) Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications. In: Abraham A, Hassanien AE, Siarry P, Engelbrecht A (eds) Foundations of computational intelligence studies in computational intelligence, vol 3. Springer, Berlin, Heidelberg, pp 23\u201355. https:\/\/doi.org\/10.1007\/978-3-642-01085-9_2"},{"key":"4100_CR65","first-page":"501","volume-title":"Emerging intelligent computing technology and applications (ICIC 2012), communications in computer and information science","author":"B Niu","year":"2012","unstructured":"Niu B, Wang H (2012) Bacterial colony optimization: principles and foundations. In: Huang DS, Gupta P, Zhang X, Premaratne P (eds) Emerging intelligent computing technology and applications (ICIC 2012), communications in computer and information science, vol 304. Springer, Berlin, Heidelberg, pp 501\u2013506"},{"key":"4100_CR66","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.asoc.2015.03.003","volume":"31","author":"SA Uymaz","year":"2015","unstructured":"Uymaz SA, Tezel G, Yel E (2015) Artificial algae algorithm (AAA) for nonlinear global optimization. Appl Soft Comput 31:153\u2013171. https:\/\/doi.org\/10.1016\/j.asoc.2015.03.003","journal-title":"Appl Soft Comput"},{"key":"4100_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2020.101104","volume":"46","author":"XS Yang","year":"2020","unstructured":"Yang XS (2020) Nature-inspired optimization algorithms: challenges and open problems. J Comput Sci 46:101104. https:\/\/doi.org\/10.1016\/j.jocs.2020.101104","journal-title":"J Comput Sci"},{"issue":"3","key":"4100_CR68","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1007\/s11831-019-09334-y","volume":"27","author":"KG Dhal","year":"2020","unstructured":"Dhal KG, Das A, Ray S, Galvez J, Das S (2020) Nature-inspired optimization algorithms and their application in multi-thresholding image segmentation. Arch Comput Methods Eng 27(3):855\u2013888. https:\/\/doi.org\/10.1007\/s11831-019-09334-y","journal-title":"Arch Comput Methods Eng"},{"key":"4100_CR69","doi-asserted-by":"publisher","unstructured":"Tzanetos A, Dounias G (2020) A comprehensive survey on the applications of swarm intelligence and bio-inspired evolutionary strategies. In: Tsihrintzis G, Jain L (eds.) Machine learning paradigms. Learning and analytics in intelligent systems (vol 18). Springer, Cham, pp.337\u2013378. https:\/\/doi.org\/10.1007\/978-3-030-49724-8_15","DOI":"10.1007\/978-3-030-49724-8_15"},{"issue":"6","key":"4100_CR70","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1016\/j.cmi.2020.03.026","volume":"26","author":"N Petrosillo","year":"2020","unstructured":"Petrosillo N, Viceconte G, Ergonul O, Ippolito G, Petersen E (2020) COVID-19, SARS and MERS: Are they closely related? Clin Microbiol Infect 26(6):729\u2013734. https:\/\/doi.org\/10.1016\/j.cmi.2020.03.026","journal-title":"Clin Microbiol Infect"},{"issue":"4","key":"4100_CR71","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1137\/S0036144500371907","volume":"42","author":"HW Hethcote","year":"2000","unstructured":"Hethcote HW (2000) The mathematics of infectious diseases. SIAM Rev 42(4):599\u2013653. https:\/\/doi.org\/10.1137\/S0036144500371907","journal-title":"SIAM Rev"},{"key":"4100_CR72","doi-asserted-by":"publisher","DOI":"10.1101\/2020.04.13.20063453","author":"A Kyagulanyi","year":"2020","unstructured":"Kyagulanyi A, Muhanguzi JT, Dembe O, Kirabo S (2020) Risk analysis and prediction for COVID-19 demographics in low resource settings using a python desktop app and excel models. MedRxiv. https:\/\/doi.org\/10.1101\/2020.04.13.20063453","journal-title":"MedRxiv"},{"key":"4100_CR73","doi-asserted-by":"publisher","DOI":"10.1101\/2020.06.12.20130021","author":"KTL Sy","year":"2020","unstructured":"Sy KTL, White LF, Nichols BE (2020) Population density and basic reproductive number of COVID-19 across United States counties. MedRxiv. https:\/\/doi.org\/10.1101\/2020.06.12.20130021","journal-title":"MedRxiv"},{"key":"4100_CR74","doi-asserted-by":"publisher","DOI":"10.1093\/jtm\/taaa021","author":"Y Liu","year":"2020","unstructured":"Liu Y, Gayle AA, Wilder-Smith A, Rocklov J (2020) The reproductive number of COVID-19 is higher compared to SARS coronavirus. J Travel Med. https:\/\/doi.org\/10.1093\/jtm\/taaa021","journal-title":"J Travel Med"},{"key":"4100_CR75","unstructured":"Biswas K, Khaleque A, Sen P (2020) Covid-19 spread: reproduction of data and prediction using a SIR model on Euclidean network. ArXiv preprint"},{"key":"4100_CR76","doi-asserted-by":"publisher","DOI":"10.1101\/2020.03.10.20033803","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, You C, Cai Z, Sun J, Hu W, Zhou XH (2020) Prediction of the COVID-19 outbreak based on a realistic stochastic model. MedRxiv. https:\/\/doi.org\/10.1101\/2020.03.10.20033803","journal-title":"MedRxiv"},{"key":"4100_CR77","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.idm.2020.03.003","volume":"5","author":"Z Liu","year":"2020","unstructured":"Liu Z, Magal P, Seydi O, Webb G (2020) A COVID-19 epidemic model with latency period. Infect Dis Model 5:323\u2013337. https:\/\/doi.org\/10.1016\/j.idm.2020.03.003","journal-title":"Infect Dis Model"},{"issue":"1\u20132","key":"4100_CR78","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/bf02464423","volume":"53","author":"WO Kermack","year":"1927","unstructured":"Kermack WO (1927) McKendrick AG (1991) Contributions to the mathematical theory of epidemics\u2013I. Bull Math Biol 53(1\u20132):33\u201355. https:\/\/doi.org\/10.1007\/bf02464423","journal-title":"Bull Math Biol"},{"key":"4100_CR79","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/s10238-020-00650-3","volume":"21","author":"MP da Silveira","year":"2020","unstructured":"da Silveira MP, da Silva Fagundes KK, Bizuti MR, Starck E, Rossi RC, e Silva DTDR (2020) Physical exercise as a tool to help the immune system against COVID-19: an integrative review of the current literature. Clin Exp Med 21:15\u201328. https:\/\/doi.org\/10.1007\/s10238-020-00650-3","journal-title":"Clin Exp Med"},{"key":"4100_CR80","doi-asserted-by":"publisher","DOI":"10.1016\/j.mehy.2020.109762","volume":"140","author":"F Taghizadeh-Hesary","year":"2020","unstructured":"Taghizadeh-Hesary F, Akbari H (2020) The powerful immune system against powerful COVID-19: a hypothesis. Med Hypotheses 140:109762. https:\/\/doi.org\/10.1016\/j.mehy.2020.109762","journal-title":"Med Hypotheses"},{"key":"4100_CR81","doi-asserted-by":"publisher","DOI":"10.1007\/s10620-020-06693-6","author":"P An","year":"2020","unstructured":"An P, Chen H, Ren H, Su J, Ji M, Kang J, Jiang X, Yang Y, Li J, Lv X, Yin A, Chen D, Chen M, Zhou Z, Dong W, Ding Y, Yu H (2020) Gastrointestinal symptoms onset in COVID-19 patients in Wuhan China. Dig Dis Sci. https:\/\/doi.org\/10.1007\/s10620-020-06693-6","journal-title":"Dig Dis Sci"},{"key":"4100_CR82","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1007\/978-0-387-30164-8_506","volume-title":"Encyclopedia of machine learning","author":"S Craw","year":"2011","unstructured":"Craw S (2011) Manhattan distance. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer, Boston, MA, p 639"},{"key":"4100_CR83","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1016\/j.future.2015.08.006","volume":"56","author":"M Abdullahi","year":"2016","unstructured":"Abdullahi M, Ngadi MA (2016) Symbiotic organism search optimization based task scheduling in cloud computing environment. Futur Gener Comput Syst 56:640\u2013650. https:\/\/doi.org\/10.1016\/j.future.2015.08.006","journal-title":"Futur Gener Comput Syst"},{"issue":"3","key":"4100_CR84","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.omega.2004.10.004","volume":"34","author":"T Bektas","year":"2006","unstructured":"Bektas T (2006) The multiple traveling salesman problem: an overview of formulations and solution procedures. Omega 34(3):209\u2013219. https:\/\/doi.org\/10.1016\/j.omega.2004.10.004","journal-title":"Omega"},{"key":"4100_CR85","unstructured":"Reinelt G (1991) ATT48 from TSPLIB\u2014A traveling salesman problem library. https:\/\/people.sc.fsu.edu\/~jburkardt\/datasets\/tsp\/tsp.htmlAccessed 26 July 2020."}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-021-04100-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-021-04100-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-021-04100-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:36:05Z","timestamp":1699576565000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-021-04100-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,4]]},"references-count":85,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["4100"],"URL":"https:\/\/doi.org\/10.1007\/s11227-021-04100-z","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,4]]},"assertion":[{"value":"20 September 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 October 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}