A grey artificial bee colony algorithm

作者：Xiang, Wan-li^*; Li, Yin-zhen; Meng, Xue-lei; Zhang, Chun-min; An, Mei-qing

来源：Applied Soft Computing, 2017, 60: 1-17.

DOI：10.1016/j.asoc.2017.06.015

• Artificial bee colony algorithm
• Grey relational analysis
• Opposition based learning
• Chaotic initialization
• Numerical optimization

Artificial bee colony (ABC) algorithm is a very popular population-based algorithm. Unfortunately, there exists a shortcoming of slow convergence rate, which partly results from random choices of neighbor individuals regarding its solution search equation. A novel scheme for the choice of neighbors is introduced based on grey relational degrees between a current individual and its neighbors to overcome the insufficiency. Then, the chosen neighbor is used to guide the search process. Additionally, inspired by differential evolution, a solution search equation called ABC/rand/2 is employed to balance the previous exploitation and a new perturbation scheme is also employed. What is more, solution search equations using information of the best individual, an opposition-based learning method and a chaotic initialization technique are also integrated into the proposed algorithm called grey artificial bee colony algorithm (GABC for short). Subsequently, the effectiveness and efficiency of GABC are validated on a test suite composed of fifty-seven benchmark functions. Furthermore, it is also compared with a few state-of-the-artalgorithms. The related experimental results show the effectiveness and superiority of GABC.

• 出版日期2017-11
• 单位兰州交通大学