Enhanced Global-Best Artificial Bee Colony Optimization Algorithm

Abstract

Artificial Bee Colony (ABC) optimization algorithm has captured much attention of researchers from various fields, in recent times. Moreover, various comparative studies clearly reports robust convergence of ABC algorithm than other bio-inspired optimization algorithms. Nevertheless, like other optimization algorithms, ABC suffers from slower convergence and tendency towards local optima trappings. Therefore, various amendments have been proposed to avertthe flaws of ABC algorithm. Nonetheless, the variants are either computationally intensive or could not avert the flaws of the algorithms. Hence, this research work proposes an efficient variant of ABC algorithm. The proposed variant capitalizes on the global-best food-source. The proposed variant has been compared with various existing variants of ABC algorithm on a few benchmark functions. Significance of the proposed variant has also been analyzed statistically. Results show the best convergence of the proposed variant among all the compared optimization algorithms on all benchmark functions.