{"title":"Improved Snow Ablation Optimizer with Heat Transfer and Condensation Strategy for Global Optimization Problem","authors":"Heming Jia, Fangkai You, Di Wu, Honghua Rao, Hangqu Wu, Laith Abualigah","doi":"10.1093/jcde/qwad096","DOIUrl":null,"url":null,"abstract":"Abstract The Snow Ablation Optimizer (SAO) is a new metaheuristic algorithm proposed in April 2023. It simulates the phenomenon of snow sublimation and melting in nature and has a good optimization effect. The SAO proposes a new two-population mechanism. By introducing Brownian motion to simulate the random motion of gas molecules in space. However, as the temperature factor changes, most water molecules are converted into water vapor. Which breaks the balance between exploration and exploitation, and reduces the optimization ability of the algorithm in the later stage. Especially in the face of high-dimensional problems, it is easy to fall into local optimal. In order to improve the efficiency of the algorithm, this paper proposes an improved Snow Ablation Optimizer with Heat Transfer and Condensation Strategy(SAOHTC). Firstly, this article proposes a heat transfer strategy. Utilizes gas molecules to transfer heat from high to low temperatures and move their positions from low to high temperatures. Causing individuals with lower fitness in the population to move towards individuals with higher fitness, thereby improving the optimization efficiency of the original algorithm. Secondly, a condensation strategy is proposed. Which can transform water vapor into water by simulating condensation in nature, improve the deficiency of the original two-population mechanism. improve the convergence speed. Finally, to verify the performance of SAOHTC. In this paper, two benchmark experiments of IEEE CEC2014 and IEEE CEC2017 and five engineering problems are used to test the superior performance of SAOHTC.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.8000,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Design and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/jcde/qwad096","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract The Snow Ablation Optimizer (SAO) is a new metaheuristic algorithm proposed in April 2023. It simulates the phenomenon of snow sublimation and melting in nature and has a good optimization effect. The SAO proposes a new two-population mechanism. By introducing Brownian motion to simulate the random motion of gas molecules in space. However, as the temperature factor changes, most water molecules are converted into water vapor. Which breaks the balance between exploration and exploitation, and reduces the optimization ability of the algorithm in the later stage. Especially in the face of high-dimensional problems, it is easy to fall into local optimal. In order to improve the efficiency of the algorithm, this paper proposes an improved Snow Ablation Optimizer with Heat Transfer and Condensation Strategy(SAOHTC). Firstly, this article proposes a heat transfer strategy. Utilizes gas molecules to transfer heat from high to low temperatures and move their positions from low to high temperatures. Causing individuals with lower fitness in the population to move towards individuals with higher fitness, thereby improving the optimization efficiency of the original algorithm. Secondly, a condensation strategy is proposed. Which can transform water vapor into water by simulating condensation in nature, improve the deficiency of the original two-population mechanism. improve the convergence speed. Finally, to verify the performance of SAOHTC. In this paper, two benchmark experiments of IEEE CEC2014 and IEEE CEC2017 and five engineering problems are used to test the superior performance of SAOHTC.
期刊介绍:
Journal of Computational Design and Engineering is an international journal that aims to provide academia and industry with a venue for rapid publication of research papers reporting innovative computational methods and applications to achieve a major breakthrough, practical improvements, and bold new research directions within a wide range of design and engineering:
• Theory and its progress in computational advancement for design and engineering
• Development of computational framework to support large scale design and engineering
• Interaction issues among human, designed artifacts, and systems
• Knowledge-intensive technologies for intelligent and sustainable systems
• Emerging technology and convergence of technology fields presented with convincing design examples
• Educational issues for academia, practitioners, and future generation
• Proposal on new research directions as well as survey and retrospectives on mature field.