Group Social Learning in Artificial Bee Colony Optimization Algorithm

Abstract

Artificial Bee Colony (ABC) optimization algorithm is a powerful 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 inherent drawback of premature convergence or stagnation that leads to loss of exploration and exploitation capability . Therefore, in order to balance between 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.