The Wingsuit Flying Search (WFS) algorithm is a newly developed global meta-heuristic algorithm. It is efficient and easy to implement, requiring no parameter tuning apart from the population size and the maximum number of iterations. Recently, WFS has been developed based on applying t-way strategies, where t represents the interaction strength. Despite the encouraging results, WFS's search strategy leans more toward local optima due to the narrowing of the boundary search space and the increased value of the search sharpness. Hybridising two or more algorithms enhances search performance by effectively balancing the strengths and mitigating the weaknesses of each method. Thus, this paper proposes a new hybrid Lévy Flight with Wingsuit Flying Search (WFS) algorithm called Enhanced Wingsuit Flying Search Algorithm (EWFS). EWFS uses a control mechanism to identify the best dynamic solution during runtime. The Lévy Flight motion helps the solution escape from local optima and improves the searching process when it gets stuck. Comparison between EWFS and WFS uses the benchmarking configuration of CA(N; 2, 5⁷), while the comparison with other metaheuristic algorithms is based on the following covering array configurations: CA(N; t, 3p), CA(N; t, v7), CA(N; 2, 2p), and CA(N; t, 210). The experimental result shows that EWFS is statistically better regarding test suite size reduction than the recent t-way strategies. It also offers improved results of 65% over the original WFS and resolves the issues of excessive exploitation and getting stuck in local minima or maxima.
Rose et al. (Wed,) studied this question.