Enhancing Artificial Bee Colony Algorithm with Dynamic Best Neighbor-guided Search Strategy

Artificial bee colony (ABC) algorithm is a relatively new bio inspired optimization technique which has attracted a lot of attention for its competitive performance. However, in the basic ABC, the solution search equation performs well in exploration but poorly in exploitation, which may cause the problem of slow convergence rate. To tackle this issue, we propose a dynamic best neighbor-guided search strategy to enhance the performance of ABC. In the proposed strategy, a dynamic neighborhood with variable size is first constructed with respect to different evolution stage of the algorithm. After that, the best food source of the neighborhood is selected to guide search instead of only using the global best food source or some elite food sources, which aims to achieve a better balance between the exploration and exploitation. In addition, we design an improved global neighborhood search operator with better robustness to further enhance the performance of ABC. In the experiments, 50 different benchmark functions are used to verify our approach, including the CEC2013 benchmark test suite. The experimental results compared with other four recently well-established ABC variants show that our approach can significantly enhance the performance of ABC.