BinBRO: Binary Battle Royale Optimizer algorithm

Stochastic methods attempt to solve problems that cannot be solved by deterministic methods with reasonable time complexity. Optimization algorithms benefit from stochastic methods; however, they do not guarantee to obtain the optimal solution. Many optimization algorithms have been proposed for sol...

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Vydáno v:Expert systems with applications Ročník 195; s. 116599
Hlavní autoři: (Rahkar Farshi), Taymaz Akan, Agahian, Saeid, Dehkharghani, Rahim
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 01.06.2022
Elsevier BV
Témata:
Battle Royale Optimization algorithm
Benchmarks
Datasets
Discrete optimization
Genetic algorithms
Operations research
Optimization
Optimization algorithms
Particle swarm optimization
Problem solving
Search algorithms
Site selection
Discrete optimization
Optimization
Battle Royale Optimization algorithm
ISSN:0957-4174, 1873-6793
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Shrnutí:Stochastic methods attempt to solve problems that cannot be solved by deterministic methods with reasonable time complexity. Optimization algorithms benefit from stochastic methods; however, they do not guarantee to obtain the optimal solution. Many optimization algorithms have been proposed for solving problems with continuous nature; nevertheless, they are unable to solve discrete or binary problems. Adaptation and use of continuous optimization algorithms for solving discrete problems have gained growing popularity in recent decades. In this paper, the binary version of a recently proposed optimization algorithm, Battle Royale Optimization, which we named BinBRO, has been proposed. The proposed algorithm has been applied to two benchmark datasets: the uncapacitated facility location problem, and the maximum-cut graph problem, and has been compared with 6 other binary optimization algorithms, namely, Particle Swarm Optimization, different versions of Genetic Algorithm, and different versions of Artificial Bee Colony algorithm. The BinBRO-based algorithms could rank first among those algorithms when applying on all benchmark datasets of both problems, UFLP and Max-Cut. •We proposed a binary version of Battle Royale Optimization algorithm named BinBRO.•BinBRO performs as good as or better than existing binary optimization algorithms.•BinBRO provides a good balance between exploration and exploitation.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2022.116599