Multiobjective Optimization at Evolutionary Search with Binary Choice Relations

A multiobjective optimization problem is considered, in which binary choice relations are used instead of optimized functions. To solve this problem, it is proposed to use an evolutionary random search algorithm, in which the function of choice in the form of a lock is used instead of the choice fun...

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Veröffentlicht in:	Cybernetics and systems analysis Jg. 56; H. 3; S. 449 - 454
Hauptverfasser:	Irodov, V. F., Barsuk, R. V., Chornomorets, H. Ya
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.05.2020 Springer Springer Nature B.V
Schlagworte:	Algorithms Analysis Artificial Intelligence Control Convergence Evolutionary algorithms Genetic algorithms Mathematics Mathematics and Statistics Multiple objective analysis Optimization Processor Architectures Search algorithms Searches and seizures Software Engineering/Programming and Operating Systems Systems Theory multiobjective optimization evolutionary search binary choice relations
ISSN:	1060-0396, 1573-8337
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Abstract	A multiobjective optimization problem is considered, in which binary choice relations are used instead of optimized functions. To solve this problem, it is proposed to use an evolutionary random search algorithm, in which the function of choice in the form of a lock is used instead of the choice function in the form of a preference,. The convergence of the proposed evolutionary algorithms is analyzed, and sufficient conditions for convergence are formulated. The results of the proposed evolutionary search are compared with the results of well-known evolutionary algorithms for one test problem.
AbstractList	A multiobjective optimization problem is considered, in which binary choice relations are used instead of optimized functions. To solve this problem, it is proposed to use an evolutionary random search algorithm, in which the function of choice in the form of a lock is used instead of the choice function in the form of a preference,. The convergence of the proposed evolutionary algorithms is analyzed, and sufficient conditions for convergence are formulated. The results of the proposed evolutionary search are compared with the results of well-known evolutionary algorithms for one test problem. A multiobjective optimization problem is considered, in which binary choice relations are used instead of optimized functions. To solve this problem, it is proposed to use an evolutionary random search algorithm, in which the function of choice in the form of a lock is used instead of the choice function in the form of a preference,. The convergence of the proposed evolutionary algorithms is analyzed, and sufficient conditions for convergence are formulated. The results of the proposed evolutionary search are compared with the results of well-known evolutionary algorithms for one test problem. Keywords: evolutionary search, multiobjective optimization, binary choice relations.
Audience	Academic
Author	Irodov, V. F. Barsuk, R. V. Chornomorets, H. Ya
Author_xml	– sequence: 1 givenname: V. F. surname: Irodov fullname: Irodov, V. F. email: vfirodov@i.ua organization: Prydniprovska State Academy of Civil Engineering and Architecture – sequence: 2 givenname: R. V. surname: Barsuk fullname: Barsuk, R. V. email: Igortrustimater@gmail.com organization: Prydniprovska State Academy of Civil Engineering and Architecture – sequence: 3 givenname: H. Ya surname: Chornomorets fullname: Chornomorets, H. Ya email: ChHYa@i.ua organization: Prydniprovska State Academy of Civil Engineering and Architecture
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References_xml	– reference: YudinDBComputing Methods of Decision Theory [in Russian]1989MoscowNauka – reference: IrodovVFThe construction and convergence of evolutional algorithms of random search for self-organizationSov. J. Autom. Inf. Sci.198720432419518730656.90087 – reference: SagawaMiyakoKusunoNatsukiAguirreHernánTanakaKiyoshiKoishiMasatakaEvolutionary Multiobjective Optimization including Practically Desirable SolutionsAdvances in Operations Research20172017116373173810.1155/2017/9094514 – reference: IrodovVFMaksimenkovVPApplication of an evolutionary program for solving the travelling-salesman problemSov. Autom. Control19811447106858930508.90066 – reference: GiagkiozisIFlemingPJPareto front estimation for decision makingEvolutionary Computation201422465167810.1162/EVCO_a_00128 – reference: H. Ya. Chornomorets and V. F. Irodov, “Applying multiobjective choice in solving analysis and synthesis problems with tubular gas heaters in building structures,” in: Construction. Materials Science. Mechanical Engineering, Ser. Energy, Ecology, Computer Technologies in Construction [in Russian], Issue 84 (2015), pp. 197–202. – reference: AjzezmanMAAleskerovFTAlternative Choice: Fundamentals of the Theory [in Russian]1990MoscowNauka – reference: LemarchandLaurentMasséDamienRebreyendPascalHåkanssonJohanMultiobjective Optimization for Multimode Transportation ProblemsAdvances in Operations Research20182018113381610110.1155/2018/8720643 – reference: V. Irodov, “Self-organization methods for analysis of nonlinear systems with binary choice relations,” System Analysis Modeling Simulation, Vol. 18–19, 203–206 (1995). – reference: V. F. Irodov and Yu. V. Khatskevych, “Convergence of evolutionary algorithms for optimal solution with binary choice relations,” in: Construction. Materials Science. Mechanical Engineering, Ser. Energy, Ecology, Computer Technologies in Construction [in Russian], Issue 98 (2017), pp. 91– 96. – reference: ZitzlerEDebKThieleLComparison of multiobjective evolutionary algorithms: Empirical resultsEvolutionary Computation20008217319510.1162/106365600568202 – volume: 2018 start-page: 1 year: 2018 ident: 260_CR3 publication-title: Advances in Operations Research doi: 10.1155/2018/8720643 – ident: 260_CR10 – volume: 8 start-page: 173 issue: 2 year: 2000 ident: 260_CR11 publication-title: Evolutionary Computation doi: 10.1162/106365600568202 – volume-title: Computing Methods of Decision Theory [in Russian] year: 1989 ident: 260_CR2 – volume: 2017 start-page: 1 year: 2017 ident: 260_CR4 publication-title: Advances in Operations Research doi: 10.1155/2017/9094514 – volume: 14 start-page: 7 issue: 4 year: 1981 ident: 260_CR6 publication-title: Sov. Autom. Control – ident: 260_CR9 – ident: 260_CR8 – volume: 20 start-page: 32 issue: 4 year: 1987 ident: 260_CR7 publication-title: Sov. J. Autom. Inf. Sci. – volume: 22 start-page: 651 issue: 4 year: 2014 ident: 260_CR5 publication-title: Evolutionary Computation doi: 10.1162/EVCO_a_00128 – volume-title: Alternative Choice: Fundamentals of the Theory [in Russian] year: 1990 ident: 260_CR1
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SubjectTerms	Algorithms Analysis Artificial Intelligence Control Convergence Evolutionary algorithms Genetic algorithms Mathematics Mathematics and Statistics Multiple objective analysis Optimization Processor Architectures Search algorithms Searches and seizures Software Engineering/Programming and Operating Systems Systems Theory
Title	Multiobjective Optimization at Evolutionary Search with Binary Choice Relations
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