An Understanding of Artificial Bee Colony Algorithm from the Perspective of Computation and Applied Mathematics: A Comparative Study

In the recent past, one of the swarm-based algorithms that have been introduced is Artificial Be Colony (ABC) algorithms. The role of ABC lies in the stimulation of honeybee swarms' intelligent foraging behavior. This study applied the ABC algorithm toward large numerical test function optimiza...

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Vydáno v:Journal of physics. Conference series Ročník 1362; číslo 1; s. 12132 - 12134
Hlavní autoři: Mohammed, Ali Hassan, Yasir, Asmahan Abed
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bristol IOP Publishing 01.11.2019
Témata:
Algorithms
Applications of mathematics
Applied mathematics
Comparative studies
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Particle swarm optimization
Physics
Search algorithms
ISSN:1742-6588, 1742-6596
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Abstract In the recent past, one of the swarm-based algorithms that have been introduced is Artificial Be Colony (ABC) algorithms. The role of ABC lies in the stimulation of honeybee swarms' intelligent foraging behavior. This study applied the ABC algorithm toward large numerical test function optimization. Also, the results were compared with those that had been reported by experimental studies employing evolution strategies, differential evolution algorithm, particle swarm optimization algorithm, and genetic algorithm. From the findings, the study established that ABC exhibits superior performance compared to population-based algorithms, with other situations also witnessing the algorithm's performance likened to or similar to the population-based algorithms. The factor that explained the superiority of the ABC algorithm was that it employs fewer control parameters.
AbstractList In the recent past, one of the swarm-based algorithms that have been introduced is Artificial Be Colony (ABC) algorithms. The role of ABC lies in the stimulation of honeybee swarms’ intelligent foraging behavior. This study applied the ABC algorithm toward large numerical test function optimization. Also, the results were compared with those that had been reported by experimental studies employing evolution strategies, differential evolution algorithm, particle swarm optimization algorithm, and genetic algorithm. From the findings, the study established that ABC exhibits superior performance compared to population-based algorithms, with other situations also witnessing the algorithm’s performance likened to or similar to the population-based algorithms. The factor that explained the superiority of the ABC algorithm was that it employs fewer control parameters.
Author Mohammed, Ali Hassan
Yasir, Asmahan Abed
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  givenname: Asmahan Abed
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  organization: Dep. of Mathematics / Faculty of Education for Girls / University of Kufa / Iraq
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Cites_doi 10.1023/A:1015652810815
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SubjectTerms Algorithms
Applications of mathematics
Applied mathematics
Comparative studies
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Particle swarm optimization
Physics
Search algorithms
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