Evaluation of performance of adaptive and HybridABC (aABC) algorithm in solution of numerical optimization problems

Abstract

Artificial bee colony (ABC) algorithm is a heuristic optimization algorithm that models food search behavior of the honey bees. It is used to solve many real-world problems and has been successful. In the literature, it is seen that different modifications of ABC algorithm are proposed to obtain more effective results. In this study, adaptive and hybrid ABC (aABC) algorithm which is one of the modifications of ABC algorithm is used. Its performance is evaluated in solving numerical test functions. Unlike standard ABC algorithm, aABC algorithm uses arithmetic crossover and adaptive neighborhood radius in the solution generation mechanism. The applications are performed on 6 numerical test functions. The results are evaluated in terms of solution quality and convergence speed. In addition, Wilcoxon signed rank test is used to examine the significance of the results. The results show that aABC algorithm is more effective than ABC algorithm in solving numerical optimization problems.