Wingsuit Flying Search—A Novel Global Optimization Algorithm

Abstract

In this paper, a novel global optimization algorithm-Wingsuit Flying Search (WFS) is introduced. It is inspired by the popular extreme sport-wingsuit flying. The algorithm mimics the intention of a flier to land at the lowest possible point of the Earth surface within their range, i.e., a global minimum of the search space. This is achieved by probing the search space at each iteration with a carefully picked population of points. Iterative update of the population corresponds to the flier progressively getting a sharper image of the surface, thus shifting the focus to lower regions. The algorithm is described in detail, including the mathematical background and the pseudocode. It is validated using a variety of classical and CEC 2020 benchmark functions under a number of search space dimensionalities. The validation includes the comparison of WFS to several nature-inspired popular metaheuristic algorithms, including the winners of CEC 2017 competition. The numerical results indicate that WFS algorithm provides considerable performance improvements (mean solution values, standard deviation of solution values, runtime and convergence rate) with respect to other methods. The main advantages of this algorithm are that it is practically parameter-free, apart from the population size and maximal number of iterations. Moreover, it is considerably ''lean'' and easy to implement.

Advanced arithmetic optimization algorithm for solving mechanical engineering design problems

“…In our proposed improvements for the AOA, the optimization process starts with initializing the candidate solutions using the beta distribution. This was chosen because so many authors have used many other distributions besides the random number to generate the initial population, with varying levels of success [17][18][19][20]. The candidate solutions are improved after every iteration according to the optimization rules.…”

Comparative Study on Single and Multiple Chaotic Maps Incorporated Grey Wolf Optimization Algorithms

“…In this section, we compare CGWOs with other metaheuristic algorithms, including PSO [96], sine cosine algorithm (SCA) [97], and wingsuit flying search algorithm (WFS) [98]. PSO and SCA are population-based metaheuristic algorithms, among which PSO simulates bird flocking and animal social behaviors and SCA uses sine and cosine functions to optimize the problem.…”

A Novel Distributed Gravitational Search Algorithm With Multi-Layered Information Interaction

“…Each algorithm repeatedly runs 51 times on each function. We analyze and compare the following algorithms: MGSA, MDBSO [83], CGWO [84], WFS [85], SCA [86], GSA [9], HGSA [46], DGSA [70] and DNLGSA [82]. Table 2 shows parameters of each algorithm.…”

“…Moreover, in comparison with DGSA, MGSA has the better performance, demonstrating the effectiveness of the proposed method. Besides GSA variants, four competitive algorithms including MDBSO [83], CGWO [84], WFS [85] and SCA [86] are adopted to compare with MGSA on CEC2017 benchmark functions and static economic dispatch problem. Results verify that MGSA is the best.…”