An improved Jaya optimization algorithm with Lévy flight

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