Coronavirus herd immunity optimizer (CHIO)

In this paper, a new nature-inspired human-based optimization algorithm is proposed which is called coronavirus herd immunity optimizer (CHIO). The inspiration of CHIO is originated from the herd immunity concept as a way to tackle coronavirus pandemic (COVID-19). The speed of spreading coronavirus...

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Vydané v:	Neural computing & applications Ročník 33; číslo 10; s. 5011 - 5042
Hlavní autori:	Al-Betar, Mohammed Azmi, Alyasseri, Zaid Abdi Alkareem, Awadallah, Mohammed A., Abu Doush, Iyad
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Springer London 01.05.2021 Springer Nature B.V
Predmet:	Algorithms Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Coronaviruses COVID-19 Data Mining and Knowledge Discovery Disease control Disease transmission Herd immunity Image Processing and Computer Vision Immunity Optimization Optimization algorithms Original Original Article Parameter sensitivity Probability and Statistics in Computer Science Social distancing COVID-19 Herd immunity Metaheuristic Coronavirus Optimization Nature inspired
ISSN:	0941-0643, 1433-3058
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Abstract	In this paper, a new nature-inspired human-based optimization algorithm is proposed which is called coronavirus herd immunity optimizer (CHIO). The inspiration of CHIO is originated from the herd immunity concept as a way to tackle coronavirus pandemic (COVID-19). The speed of spreading coronavirus infection depends on how the infected individuals directly contact with other society members. In order to protect other members of society from the disease, social distancing is suggested by health experts. Herd immunity is a state the population reaches when most of the population is immune which results in the prevention of disease transmission. These concepts are modeled in terms of optimization concepts. CHIO mimics the herd immunity strategy as well as the social distancing concepts. Three types of individual cases are utilized for herd immunity: susceptible, infected, and immuned. This is to determine how the newly generated solution updates its genes with social distancing strategies. CHIO is evaluated using 23 well-known benchmark functions. Initially, the sensitivity of CHIO to its parameters is studied. Thereafter, the comparative evaluation against seven state-of-the-art methods is conducted. The comparative analysis verifies that CHIO is able to yield very competitive results compared to those obtained by other well-established methods. For more validations, three real-world engineering optimization problems extracted from IEEE-CEC 2011 are used. Again, CHIO is proved to be efficient. In conclusion, CHIO is a very powerful optimization algorithm that can be used to tackle many optimization problems across a wide variety of optimization domains.
AbstractList	In this paper, a new nature-inspired human-based optimization algorithm is proposed which is called coronavirus herd immunity optimizer (CHIO). The inspiration of CHIO is originated from the herd immunity concept as a way to tackle coronavirus pandemic (COVID-19). The speed of spreading coronavirus infection depends on how the infected individuals directly contact with other society members. In order to protect other members of society from the disease, social distancing is suggested by health experts. Herd immunity is a state the population reaches when most of the population is immune which results in the prevention of disease transmission. These concepts are modeled in terms of optimization concepts. CHIO mimics the herd immunity strategy as well as the social distancing concepts. Three types of individual cases are utilized for herd immunity: susceptible, infected, and immuned. This is to determine how the newly generated solution updates its genes with social distancing strategies. CHIO is evaluated using 23 well-known benchmark functions. Initially, the sensitivity of CHIO to its parameters is studied. Thereafter, the comparative evaluation against seven state-of-the-art methods is conducted. The comparative analysis verifies that CHIO is able to yield very competitive results compared to those obtained by other well-established methods. For more validations, three real-world engineering optimization problems extracted from IEEE-CEC 2011 are used. Again, CHIO is proved to be efficient. In conclusion, CHIO is a very powerful optimization algorithm that can be used to tackle many optimization problems across a wide variety of optimization domains. In this paper, a new nature-inspired human-based optimization algorithm is proposed which is called coronavirus herd immunity optimizer (CHIO). The inspiration of CHIO is originated from the herd immunity concept as a way to tackle coronavirus pandemic (COVID-19). The speed of spreading coronavirus infection depends on how the infected individuals directly contact with other society members. In order to protect other members of society from the disease, social distancing is suggested by health experts. Herd immunity is a state the population reaches when most of the population is immune which results in the prevention of disease transmission. These concepts are modeled in terms of optimization concepts. CHIO mimics the herd immunity strategy as well as the social distancing concepts. Three types of individual cases are utilized for herd immunity: susceptible, infected, and immuned. This is to determine how the newly generated solution updates its genes with social distancing strategies. CHIO is evaluated using 23 well-known benchmark functions. Initially, the sensitivity of CHIO to its parameters is studied. Thereafter, the comparative evaluation against seven state-of-the-art methods is conducted. The comparative analysis verifies that CHIO is able to yield very competitive results compared to those obtained by other well-established methods. For more validations, three real-world engineering optimization problems extracted from IEEE-CEC 2011 are used. Again, CHIO is proved to be efficient. In conclusion, CHIO is a very powerful optimization algorithm that can be used to tackle many optimization problems across a wide variety of optimization domains.In this paper, a new nature-inspired human-based optimization algorithm is proposed which is called coronavirus herd immunity optimizer (CHIO). The inspiration of CHIO is originated from the herd immunity concept as a way to tackle coronavirus pandemic (COVID-19). The speed of spreading coronavirus infection depends on how the infected individuals directly contact with other society members. In order to protect other members of society from the disease, social distancing is suggested by health experts. Herd immunity is a state the population reaches when most of the population is immune which results in the prevention of disease transmission. These concepts are modeled in terms of optimization concepts. CHIO mimics the herd immunity strategy as well as the social distancing concepts. Three types of individual cases are utilized for herd immunity: susceptible, infected, and immuned. This is to determine how the newly generated solution updates its genes with social distancing strategies. CHIO is evaluated using 23 well-known benchmark functions. Initially, the sensitivity of CHIO to its parameters is studied. Thereafter, the comparative evaluation against seven state-of-the-art methods is conducted. The comparative analysis verifies that CHIO is able to yield very competitive results compared to those obtained by other well-established methods. For more validations, three real-world engineering optimization problems extracted from IEEE-CEC 2011 are used. Again, CHIO is proved to be efficient. In conclusion, CHIO is a very powerful optimization algorithm that can be used to tackle many optimization problems across a wide variety of optimization domains.
Author	Al-Betar, Mohammed Azmi Awadallah, Mohammed A. Abu Doush, Iyad Alyasseri, Zaid Abdi Alkareem
Author_xml	– sequence: 1 givenname: Mohammed Azmi orcidid: 0000-0003-1980-1791 surname: Al-Betar fullname: Al-Betar, Mohammed Azmi email: mohbetar@bau.edu.jo organization: Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, Department of Information Technology - MSAI, College of Engineering and Information Technology Ajman University – sequence: 2 givenname: Zaid Abdi Alkareem orcidid: 0000-0003-4228-9298 surname: Alyasseri fullname: Alyasseri, Zaid Abdi Alkareem organization: Center for Artificial Intelligence, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, ECE Department, Faculty of Engineering, University of Kufa – sequence: 3 givenname: Mohammed A. orcidid: 0000-0002-7815-8946 surname: Awadallah fullname: Awadallah, Mohammed A. organization: Department of Computer Science, Al-Aqsa University – sequence: 4 givenname: Iyad surname: Abu Doush fullname: Abu Doush, Iyad organization: Computer Science Department, American University of Kuwait, Computer Science Department, Yarmouk University
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/32874019$$D View this record in MEDLINE/PubMed
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Keywords	COVID-19 Herd immunity Metaheuristic Coronavirus Optimization Nature inspired
Language	English
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IEEE – reference: AroraSSinghSButterfly optimization algorithm: a novel approach for global optimizationSoft Comput2019233715734 – reference: MirjaliliSGandomiAHMirjaliliSZSaremiSFarisHMirjaliliSMSalp swarm algorithm: a bio-inspired optimizer for engineering design problemsAdv Eng Softw2017114163191 – reference: EskandarHSadollahABahreininejadAHamdiMWater cycle algorithm-a novel metaheuristic optimization method for solving constrained engineering optimization problemsComput Struct2012110151166 – reference: KozaJRKozaJRGenetic programming: on the programming of computers by means of natural selection1992CambridgeMIT Press0850.68161 – reference: FernandoFAdolfoR-OErikCAndrade ÁngelGMarcoP-CFrom ants to whales: metaheuristics for all tastesArtif Intel Rev20192019158 – reference: World Health Organization (2020) Covid-19 uk data – reference: FinePEMHerd immunity: history, theory, practiceEpidemiol Rev1993152265302 – reference: MirjaliliSDragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problemsNeural Comput Appl2016274105310734138447 – reference: SimonDBiogeography-based optimizationIEEE Trans Evol Comput2008126702713 – reference: LaTorre A, Muelas S, Peña J-M (2011) Benchmarking a hybrid de-rhc algorithm on real world problems. In: 2011 IEEE congress of evolutionary computation (CEC), pp 1027–1033. IEEE – reference: Singh HK, Ray T (2011) Performance of a hybrid ea-de-memetic algorithm on cec 2011 real world optimization problems. In: 2011 IEEE congress of evolutionary computation (CEC), pp 1322–1326. IEEE – reference: HashimFAHousseinEHMabroukMSAl-AtabanyWMirjaliliSHenry gas solubility optimization: a novel physics-based algorithmFuture Gener Comput Syst2019101646667 – reference: RibeiroGSHamerGLDialloMKitronUKoAIWeaverSCInfluence of herd immunity in the cyclical nature of arbovirusesCurr Opin Virol202040110 – reference: MirjaliliSSca: a sine cosine algorithm for solving optimization problemsKnowl Based Syst201696120133 – reference: BlumCRoliAMetaheuristics in combinatorial optimization: overview and conceptual comparisonACM Comput Surv2003353268308 – reference: AndersonRMMayRMImmunisation and herd immunityLancet19903358690641645 – reference: RemuzziARemuzziGCovid-19 and italy: what next?Lancet2020395497 – reference: MirjaliliSMirjaliliSMHatamlouAMulti-verse optimizer: a nature-inspired algorithm for global optimizationNeural Comput Appl2016272495513 – reference: RashediENezamabadi-PourHSaryazdiSGsa: a gravitational search algorithmInf Sci200917913223222481177.90378 – reference: Glover F, Laguna M (1998) Tabu search. 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Snippet	In this paper, a new nature-inspired human-based optimization algorithm is proposed which is called coronavirus herd immunity optimizer (CHIO). The inspiration...
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SubjectTerms	Algorithms Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Coronaviruses COVID-19 Data Mining and Knowledge Discovery Disease control Disease transmission Herd immunity Image Processing and Computer Vision Immunity Optimization Optimization algorithms Original Original Article Parameter sensitivity Probability and Statistics in Computer Science Social distancing
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Title	Coronavirus herd immunity optimizer (CHIO)
URI	https://link.springer.com/article/10.1007/s00521-020-05296-6 https://www.ncbi.nlm.nih.gov/pubmed/32874019 https://www.proquest.com/docview/2518854640 https://www.proquest.com/docview/2439627280 https://pubmed.ncbi.nlm.nih.gov/PMC7451802
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