Discrete Optimization Algorithm Based on Probability Distribution with Transformation of Target Values

Problems of search optimization in a discrete space, particularly, in a binary space where a variable can take only two values, are of great practical importance. This paper proposes a new population-based discrete optimization algorithm that uses probability distributions of variables. The distribu...

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Veröffentlicht in:Programming and computer software Jg. 50; H. 6; S. 445 - 456
1. Verfasser: Sarin, K. S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Moscow Pleiades Publishing 01.12.2024
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Computer Science
Design
Efficiency
Embedded systems
Genetic algorithms
Integer programming
Methods
Operating Systems
Optimization
Optimization algorithms
Probability
Software Engineering
Software Engineering/Programming and Operating Systems
discrete optimization
binary optimization
stochastic algorithms
metaheuristics
optimization methods
and probability distribution
ISSN:0361-7688, 1608-3261
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Zusammenfassung:Problems of search optimization in a discrete space, particularly, in a binary space where a variable can take only two values, are of great practical importance. This paper proposes a new population-based discrete optimization algorithm that uses probability distributions of variables. The distributions determine the probability of taking one or another discrete value and are generated by transforming target values of solutions into their weight coefficients. The performance of the algorithm is evaluated using unimodal and multimodal test functions with binary variables. The experimental results demonstrate the high efficiency of the proposed algorithm in terms of convergence rate and stability.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0361-7688
1608-3261
DOI:10.1134/S0361768824700312