Abstract
A mathematical process which is used to search the maxima or minima of an objective function in the valid search space is known as the optimization. A wide range of nature-inspired optimization tactics such as spider monkey optimization, ant colony optimization and particle swarm optimization are used to find the most desirable solution and one of them is artificial bee colony (ABC) algorithm which is a population-based metaheuristic optimization approach. In the proposed work, a modification in gbest-guided ABC (GABC) algorithm is introduced by integrating some properties of Gaussian ABC algorithm. The aim of this paper is to overcome certain impediments of original ABC algorithm such as low speed of convergence, weak exploitation capability and solutions easily trapped by local optima. Experimental results by proposed algorithm are tested on several benchmark functions that show the modified GABC can improve upon ABC and GABC algorithms in most of the experiments.
Published Version
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