Alpine skiing optimization: A new bio-inspired optimization algorithm. (August 2022)

• Record Type: Journal Article
• Authors: Yuan, Yongliang; Ren, Jianji; Wang, Shuo; Wang, Zhenxi; Mu, Xiaokai; Zhao, Wu
• Is Part Of: Advances in engineering software. Volume 170(2022)
• Publication Date: 2022-08
• Subjects: Swarm intelligence -- Alpine skiing optimization algorithm -- Physical stamina -- Constrained optimization -- Auto drum fashioned brake; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2022.103158
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: A mathematical model is proposed to simulate the behaviors of skiers competing for the championship. A novel optimization algorithm is proposed using the mathematical model. The proposed alpine skiing optimization (ASO) algorithm is tested on twenty-three unconstrained benchmark functions and four engineering design problems. The results indicate that ASO can be used as a state-of-the-art optimization algorithm to solve engineering optimization problems. ASO is applied to ensure the parameter of an auto drum fashioned brake. The braking efficiency factor can be improved 28.446% compared with the initial design. Results reveal that ASO is appreciated for complex engineering optimization problem due to its high efficiency, strong reliability and robust exploration performances. Abstract: A novel swarm intelligence optimization algorithm is proposed, which is named alpine skiing optimization (ASO). The main inspiration of the ASO originated from the behaviors of skiers competing for the championship. In the ASO, physical stamina and sprint are two essential factors for skiers to win the tournament, which are similar to the two stages of exploration and exploitation. The skiers revealed the behaviour of winning the tournament according to the static sliding and dynamic sliding. This work simulates this behaviour from a mathematical perspective and develops the ASO algorithm. The performance of the ASO algorithm is investigated, through a comparison with many competitive optimization algorithms and four constrained engineering problems. The statistical results validate that the ASO can provide competitive results compared to other state-of-the-art optimization algorithms. Furthermore, ASO is applied to optimize the parameter of an auto drum fashioned brake engineering problem. The objective function is chosen to maximize the braking efficiency coefficient. Results show that the braking efficiency factor is improved by 28.446% compared with the initial design.