Chaotic grey wolf optimization

Grey wolf optimization algorithm (GWO) is a recently proposed meta-heuristics and has shown promising performance in solving complex function optimization and engineering problems. To further enrich the search dynamics of GWO, the chaotic local search (CLS) mechanism is incorporated into GWO to enhance the search by taking the properties of ergodicity and randomness of chaotic maps. Twelve different kinds of chaotic maps are investigated to give some insights into the influence of CLS on GWO. Experimental results based on 29 widely used benchmark functions suggest that CLS indeed enables GWO to possess better performance in terms of solution accuracy, solution distribution, and convergence property. Summarized results also reveal that the performance of the resultant chaotic grey wolf optimization (CGWO) algorithm is effected not only by the characteristics of the embedded chaotic map, but also by the landscape of the solved problems.