A Novel Global ABC Algorithm with Self-Perturbing

• Main Authors: Zhou Shuliang, Feng Dongqing, Ding Panpan
• Format: Article
• Language: English
• Published: De Gruyter 2017-09-01
• Series: Journal of Intelligent Systems
• Subjects: abc; improved algorithm; self-perturbing; global optimal solution; self-adaptive; new mutation strategy; bionics
• Online Access: https://doi.org/10.1515/jisys-2016-0060

Artificial bee colony (ABC) is a kind of a metaheuristic population-based algorithms proposed in 2005. Due to its simple parameters and flexibility, the ABC algorithm is applied to engineering problems, algebra problems, and so on. However, its premature convergence and slow convergence speed are inherent shortcomings. Aiming at the shortcomings, a novel global ABC algorithm with self-perturbing (IGABC) is proposed in this paper. On the basis of the original search equation, IGABC adopts a novel self-adaptive search equation, introducing the guidance of the global optimal solution. The search method improves the convergence precision and the global search capacity. An excellent leader can lead the whole team to obtain more success. In order to obtain a better “leader,” IGABC proposes a novel method with global self-perturbing. To avoid falling into the local optimum, this paper designed a new mutation strategy that simulates the natural phenomenon of sick fish being eaten.