Power law-based local search in artificial bee colony
by Harish Sharma; Jagdish Chand Bansal; K.V. Arya
International Journal of Artificial Intelligence and Soft Computing (IJAISC), Vol. 4, No. 2/3, 2014

Abstract: Artificial bee colony (ABC) optimisation algorithm is relatively a simple and recent population-based probabilistic approach for global optimisation. ABC has been outperformed over some nature inspired algorithms (NIAs) when tested over benchmark as well as real world optimisation problems. 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. In the solution search equation of ABC, there is an enough chance to skip the true solution due to large step sizes. In order to balance the diversity and convergence capability of the ABC, in this paper, a power law-based local search strategy is proposed and integrated with ABC. The proposed strategy is named as power law-based local search in ABC (PLABC). In the PLABC, new solutions are generated around the best solution and it helps to enhance the exploitation capability of ABC. Further, to improve the exploration capability, numbers of scout bees are increased. The experiments on 24 test problems of different complexities show that the proposed strategy outperforms the basic ABC and recent variants of ABC, namely, Gbest guided ABC (GABC), best-so-far ABC (BSFABC) and modified ABC in most of the experiments.

Online publication date: Sat, 28-Jun-2014

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