Neighborhood Learning for Artificial Bee Colony Algorithm: A Mini-survey

Artificial bee colony (ABC) algorithm is a representative paradigm of swarm intelligence optimization (SIO) algorithms, which has received much attention in the field of global optimization for its good performance yet simple structure. However, there still exists a drawback for ABC that it owns strong exploration but weak exploitation, resulting in slow convergence speed and low convergence accuracy. To solve this drawback, in recent years, the neighborhood learning mechanism has emerged as an effective method, becoming a hot research topic in the community of ABC. However, there has been no surveys on it, even a short one. Considering the appeal of the neighborhood learning mechanism, we are motivated to provide a mini-survey to highlight some key aspects about it, including 1) how to construct a neighborhood topology? 2) how to select the learning exemplar? and 3) what are the advantages and disadvantages? In this mini-survey, some related neighborhood-based ABC variants are reviewed to reveal the key aspects. Furthermore, some interesting future research directions are also given to encourage deeper related works.