Opposition based lévy flight artificial bee colony

Artificial Bee Colony (ABC) is a well known optimization approach to solve nonlinear and complex problems. It is relatively a simple and recent population based probabilistic approach for global optimization. Similar to other population 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. In the solution search equation of ABC due to the large step size the chance of skipping the true solution is high. Therefore, in this paper, to balance the diversity and convergence capability of the ABC, Lévy Flight random walk based local search strategy is proposed and incorporated with ABC along with opposition based learning strategy. The proposed algorithm is named as Opposition Based Lévy Flight ABC. The experiments over 14 un-biased test problems of different complexities and five well known engineering optimization problems show that the proposed algorithm outperforms the basic ABC and its recent variants namely Gbest guided ABC, Best-So-Far ABC, and Modified ABC in most of the experiments.