An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems
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Artificial bee colony (ABC) algorithm developed by Karaboga is a nature inspired metaheuristic based on honey bee foraging behavior. It was successfully applied to continuous unconstrained optimization problems and later it was extended to constrained design problems as well. This paper introduces an upgraded artificial bee colony (UABC) algorithm for constrained optimization problems. Our UABC algorithm enhances fine-tuning characteristics of the modification rate parameter and employs modified scout bee phase of the ABC algorithm. This upgraded algorithm has been implemented and tested on standard engineering benchmark problems and the performance was compared to the performance of the latest Akay and Karaboga’s ABC algorithm. Our numerical results show that the proposed UABC algorithm produces better or equal best and average solutions in less evaluations in all cases.
KeywordsArtificial bee colony (ABC) Constrained optimization Swarm intelligence Nature inspired metaheuristics
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