Shrinking hyper-sphere based Artificial Bee Colony algorithm

Abstract

Artificial Bee Colony (ABC) is a popular swarm intelligence based approach used to solve nonlinear and complex optimization problems. It is a simple to implement and swarm based probabilistic algorithm. As the case of other swarm based algorithms, ABC is also computationally expensive due to its slow nature of search process. The solution search equation of ABC is significantly influenced by a random quantity which helps in exploration at the cost of exploitation of the search space. Therefore, to balance the exploration and exploitation characteristics of the ABC, Shrinking Hyper-Sphere based local search approach is developed and hybridized with in the ABC solution search process. The proposed algorithm is named as Shrinking Hyper-Sphere based ABC (SHABC). The experiments over 14 well known benchmark functions of complex in nature, show that the SHABC algorithm perform better than the original version of ABC and its latest variant, namely Modified ABC (MABC) in most of the experiments.