Group Social Learning in Artificial Bee Colony Optimization Algorithm Harish Sharma 1 , Abhishek Verma 2 , and Jagdish Chand Bansal 3 1 ABV-Indian Institute of Information Technology and Management, Gwalior harish0107@rediffmail.com 2 ABV-Indian Institute of Information Technology and Management, Gwalior abhishekverma.cs@gmail.com 3 ABV-Indian Institute of Information Technology and Management, Gwalior jcbansal@gmail.com Abstract. Artificial Bee Colony (ABC) optimization algorithm is a power- ful stochastic evolutionary algorithm that is used to find the global optimum solution in search space. In ABC each bee stores candidate solution; and stochastically modifies its candidate over time, based on the best solution found by neighboring bees,and based on the best solution found by the bee itself. When tested over various benchmark function and real life problems, it has performed better than a few evolutionary algorithms and other search heuristics . ABC, like other probabilistic optimization algorithms, has inher- ent drawback of premature convergence or stagnation that leads to loss of exploration and exploitation capability . Therefore, in order to balance be- tween exploration and exploitation capability of ABC a new search strategy is proposed. In the proposed strategy, search process in ABC is performed by smaller group of independent swarms of bees. The experiments with 10 test functions of different complexities show that the proposed strategy has better diversity and faster convergence than the basic ABC. Keywords: Numerical Optimization, Artificial Bee Colony Algorithm, Group Social Learning, Swarm intelligence, Meta-heuristics. 1 Introduction Swarm Intelligence is a meta-heuristic approach in the field of nature inspired techniques that is used to solve optimization problems. It is based on the col- lective behavior of social creatures. Social creatures utilizes their ability of social learning to solve complex tasks. Researchers have analyzed such behav- iors and designed algorithms that can be used to solve nonlinear, nonconvex or combinatorial optimization problems. Previous research [4, 12, 15, 17] have shown that algorithms based on Swarm Intelligence have great potential to K. Deep et al. (Eds.): Proceedings of the International Conference on SocProS 2011, AISC 130, pp. 441–451. springerlink.com c  Springer India 2012