2020
DOI: 10.1109/access.2020.2981196
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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 gettin… Show more

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Cited by 50 publications
(26 citation statements)
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“…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.…”
Section: The Proposed Naoamentioning
confidence: 99%
“…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.…”
Section: The Proposed Naoamentioning
confidence: 99%
“…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.…”
Section: Comparison Of Cgwo With Other Meta-heuristic Algorithmsmentioning
confidence: 99%
“…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.…”
Section: A Experimental Setupmentioning
confidence: 99%
“…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.…”
Section: Introductionmentioning
confidence: 99%