A self-adaptive artificial bee colony algorithm based on global best for global optimization

Intelligent optimization algorithms based on evolutionary and swarm principles have been widely researched in recent years. The artificial bee colony (ABC) algorithm is an intelligent swarm algorithm for global optimization problems. Previous studies have shown that the ABC algorithm is an efficient, effective, and robust optimization method. However, the solution search equation used in ABC is insufficient, and the strategy for generating candidate solutions results in good exploration ability but poor exploitation performance. Although some complex strategies for generating candidate solutions have recently been developed, the universality and robustness of these new algorithms are still insufficient. This is mainly because only one strategy is adopted in the modified ABC algorithm. In this paper, we propose a self-adaptive ABC algorithm based on the global best candidate (SABC-GB) for global optimization. Experiments are conducted on a set of 25 benchmark functions. To ensure a fair comparison with other algorithms, we employ the same initial population for all algorithms on each benchmark function. Besides, to validate the feasibility of SABC-GB in real-world application, we demonstrate its application to a real clustering problem based on the K-means technique. The results demonstrate that SABC-GB is superior to the other algorithms for solving complex optimization problems. It means that it is a new technique to improve the ABC by introducing self-adaptive mechanism.