Improved artificial bee colony algorithm with differential evolution for the numerical optimisation problems

Abstract

Evolutionary algorithms (EAs) have been widely used in recent years. Artificial bee colony (ABC) algorithm is an EA for numerical optimisation problems. Recently, more and more researchers show interest in ABC algorithm. Previous studies have shown that the ABC algorithm is an efficient, effective and robust evolutionary optimisation method. However, the convergence rate of ABC algorithm still does not meet our requirements and it is necessary to optimise the ABC algorithm. In this paper, several local search operations are embedded into the ABC algorithm. This modification enables the algorithm to get a better balance between the convergence rate and the robustness. Thus it can be possible to increase the convergence speed of the ABC algorithm and thereby obtain an acceptable solution. Such an improvement can be advantageous in many real-world problems. This paper focuses on the performance of improving artificial bee colony algorithm with differential strategy on the numerical optimisation problems. The proposed algorithm has been tested on 18 benchmark functions from relevant literature. The experiment results indicated that the performance of the improved ABC algorithm is better than that of the original ABC algorithm and some other classical algorithms.