A binary artificial bee colony algorithm and its performance assessment

•This study proposes artificial bee colony algorithm-based binary optimization method.•The stigmergic behavior is integrated with this algorithm.•The proposed algorithm is applied to solve modern benchmark problems.•The performance of algorithm is compared with many state-of-art algorithms. Artifici...

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Vydané v:Expert systems with applications Ročník 175; s. 114817
Hlavný autor: Kiran, Mustafa Servet
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Elsevier Ltd 01.08.2021
Elsevier BV
Predmet:
Artificial bee colony
Basic converters
Binary optimization
Evolutionary algorithms
Evolutionary computation
Logic circuits
Operations research
Optimization
Optimization algorithms
Performance assessment
Search algorithms
Site selection
Solution space
Stigmergy
Swarm intelligence
Xor logic gate
Artificial bee colony
Xor logic gate
Binary optimization
Stigmergy
ISSN:0957-4174, 1873-6793
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Shrnutí:•This study proposes artificial bee colony algorithm-based binary optimization method.•The stigmergic behavior is integrated with this algorithm.•The proposed algorithm is applied to solve modern benchmark problems.•The performance of algorithm is compared with many state-of-art algorithms. Artificial bee colony algorithm, ABC for short, is a swarm-based optimization algorithm proposed for solving continuous optimization problems. Due to its simple but effective structure, some binary versions of the algorithm have been developed. In this study, we focus on modification of its xor-based binary version, called as binABC. The solution update rule of basic ABC is replaced with a xor logic gate in binABC algorithm, and binABC works on discretely-structured solution space. The rest of components in binABC are the same as with the basic ABC algorithm. In order to improve local search capability and convergence characteristics of binABC, a stigmergic behavior-based update rule for onlooker bees of binABC and extended version of xor-based update rule are proposed in the present study. The developed version of binABC is applied to solve a modern benchmark problem set (CEC2015). To validate the performance of proposed algorithm, a series of comparisons are conducted on this problem set. The proposed algorithm is first compared with the basic ABC and binABC on CEC2015 set. After its performance validation, six binary versions of ABC algorithm are considered for comparison of the algorithms, and a comprehensive comparison among the state-of-art variants of swarm intelligence or evolutionary computation algorithms is conducted on this set of functions. Finally, an uncapacitated facility location problem set, a pure binary optimization problem, is considered for the comparison of the proposed algorithm and binary variants of ABC algorithm. The experimental results and comparisons show that the proposed algorithm is successful and effective in solving binary optimization problems as its basic version in solving continuous optimization problems.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2021.114817