An improved Jaya optimization algorithm with Lévy flight

Recent advances in metaheuristics have shown the advantages of using Lévy distribution, which models a kind random walk (named “Lévy flight”) with occasional “big” steps. This characteristic makes flight especially useful for performing large “jumps” that allow search to escape from local optimum and restart different region space. In this paper, we investigate idea by applying Jaya, simple yet effective Swarm Intelligence optimization algorithm recently proposed literature. We perform experiments on CEC 2014 benchmark as well five industrial problems taken 2011 benchmark, compare performance Jaya Algorithm (LJA) against several state-of-the-art algorithms continuous optimization. Our numerical results show that, although both LJA are general less efficient than most advanced largely outperforms original cases, is also highly competitive tested problems.