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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Bibliographic Details
Published in:Cybernetics and systems analysis Vol. 56; no. 3; pp. 449 - 454
Main Authors: Irodov, V. F., Barsuk, R. V., Chornomorets, H. Ya
Format: Journal Article
Language:English
Published: New York Springer US 01.05.2020
Springer
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1060-0396
1573-8337
DOI:10.1007/s10559-020-00260-7