An improved TLBO based memetic algorithm for aerodynamic shape optimization

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• 作者：Xinghua Qu^b ; Ran Zhang^b ; Bo Liu^a ; ^{bliu@amss.ac.cn} ; Huifeng Li^b ; ^{lihuifeng@buaa.edu.cn}
• 关键词：Aerodynamic shape optimization ; Teaching-Learning based optimization ; Memetic algorithm
• 刊名：Engineering Applications of Artificial Intelligence
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：57
• 期：Complete
• 页码：1-15
• 全文大小：1999 K
• 卷排序：57

文摘

Aerodynamic shape optimization (ASO) for aircraft is the focus of concern as well as the subject of substantial research issue in aerospace engineering. This paper proposes a novel TLBO (teaching-learning based optimization based) memetic algorithm (TLBO-MA) for optimizing the aerodynamic shape. In the proposed TLBO-MA, an adaptive teaching factor, conservation of information inspired operator and multi-meme learning are incorporated to enhance the searching behavior of standard TLBO. Simulation based on well-known benchmarks and ASO for HTV-2 prototype demonstrates the efficiency of the proposed TLBO-MA.