Lbest artificial bee colony using structured swarm

Abstract

Artificial bee colony (ABC) optimization algorithm is a powerful stochastic evolutionary algorithm that is used to find the global optima. In ABC each bee stores the information of candidate solution and stochastically modifies this over time, based on the information provided 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 some evolutionary algorithms and other search heuristics. However ABC, like other probabilistic optimization algorithms, has inherent drawback of premature convergence or stagnation that leads to the loss of exploration and exploitation capability. 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, in order to balance between exploration and exploitation capability of the ABC, a new search strategy is proposed. In the proposed strategy, new solution is generated using the current solution and the best solution. Further, in the proposed search strategy, the swarm of bees is dynamically divided into smaller subgroups and the search process is performed by independent smaller groups of bees. The experiments on 15 test functions of different complexities show that the proposed strategy outperforms the ABC algorithm in most of the experiments. Further, the results of the proposed strategy are compared with the results of recent variants of ABC named Gbest guided ABC (GABC), Best-So-Far ABC (BSFABC) and Modified ABC (MABC).