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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Veröffentlicht in:Journal of physics. Conference series Jg. 1362; H. 1; S. 12132 - 12134
Hauptverfasser: Mohammed, Ali Hassan, Yasir, Asmahan Abed
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bristol IOP Publishing 01.11.2019
Schlagworte:
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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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1362/1/012132