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.
