Open AccessAn Understanding of Artificial Bee Colony Algorithm from the Perspective of Computation and Applied Mathematics: A Comparative Study
Author(s) -
Ali Hassan Mohammed,
Asmahan Abed Yasir
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1362/1/012132
Subject(s) - artificial bee colony algorithm , perspective (graphical) , differential evolution , population , cultural algorithm , particle swarm optimization , algorithm , swarm intelligence , computer science , meta optimization , genetic algorithm , computation , mathematical optimization , evolutionary computation , artificial intelligence , mathematics , demography , sociology
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.