Differential evolution-based wingsuit flying search for optimization

In the past few years, meta-heuristic algorithms have rapid development. More and more scholars started research in this area. Wingsuit flying search (WFS) is a recently proposed nature-inspired meta-heuristic algorithm which is able to solve complex optimization problems rapidly and effectively. However, due to the number of initial points (individuals) in WFS is small, the algorithm lacks of population diversity. Therefore, we use differential evolution (DE) as a search strategy to improve it. DE is a classical evolutionary algorithm which has fast convergence ability and good convergence results. Thus, we incorporate its learning operator into WFS. This hybrid algorithm enhances the exploration ability via DE on the basis of WFS. Finally, we proved that the proposed algorithm has superior performance in comparison with other state-of-the-art algorithms in terms of effectiveness and robustness based on thirty classical benchmark functions.