https://orcid.org/0000-0002-6957-2942
![Sample our Mathematics & Statistics journals, sign in here to start your FREE access for 14 days]({"type":"image" , "src" : "/sda/5943/TOC-Subject-Sample-2021-Mathematics-Statistics.jpg"})
ABSTRACT
In this paper, a novel multidisciplinary design optimisation (MDO) algorithm is proposed, which is named as the decomposition-based switching multi-objective whale optimiser (SMWO/D). In particular, a penalty-Tchebycheff value-based decomposition framework is designed to decouple the strongly correlated conflicting objectives, so as to give comprehensive considerations to different disciplinary demands. To overcome the shortcoming of premature in the complicated multi-modal non-linear decision space, two adaptively switchable evolutionary modes are defined to enhance the ability of escaping from local optimum and promote a thorough global search with rich learning strategies. The proposed SMWO/D is evaluated on a series of benchmark functions, and the results show its competitiveness in terms of comprehensive performance as compared with other four popular decomposition-based multi-objective optimisation algorithms (MOAs). In addition, sensitivity analysis is carried out to determine the best parameter configuration of SMWO/D. Finally, in a case study of a real-world turbine disk structural optimisation, the practicality of the proposed SMWO/D is validated, which can effectively handle the multidisciplinary property of this complicated problem, thereby providing valuable experiences in the aero-engine MDO domain.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The data that support the findings of this study are available from the corresponding author, Nianyin Zeng, upon reasonable request.
Additional information
Funding
Notes on contributors
Han Li
Han Li received the bachelor degree in Measurement and Control Technology and Instrumentation from Xiamen University, Xiamen, China, in 2018. He is currently working towards the Ph. D. degree in Measuring and Testing Technologies and Instruments at Xiamen University, Xiamen, China. His research interests include intelligent optimization algorithms and evolutionary transfer optimization techniques.
Haonan Liu
Haonan Liu received the bachelor degree in Electrical Engineering and Automation from Wuhan University, Wuhan, China, in 2021. He is currently pursuing the master degree in Electronic Engineering at Xiamen University, Xiamen, China. His research interests include multi-objective optimization and deep learning techniques.
Chengbo Lan
Chengbo Lan received the bachelor degree in Machinery Design and Manufacturing and Its Automation from Guangdong University of Technology, GuangZhou, China, in 2018. He is currently pursuing the master degree in Electronic Information at Xiamen University, Xiamen, China. His research interests include dynamic multi-objective optimization and transfer learning techniques.
Yiqi Yin
Yiqi Yin received the bachelor degree in Automation from Xiamen University, Xiamen, China, in 2020. She is currently pursuing the master degree in Mechanical Engineering at Xiamen University, Xiamen, China. Her research interests include single objective optimization, multi-objective optimization and optimum structural design.
Peishu Wu
Peishu Wu received the bachelor degree in Measurement and Control Technology and Instrumentation from Tianjin University of Science and Technology, Tianjin, China, in 2020. He is currently pursuing the Ph. D. degree in Measuring and Testing Technologies and Instruments at Xiamen University, Xiamen, China. His research interests include computer vision and deep learning techniques.
Cheng Yan
Cheng Yan was born in Shaanxi, China, in 1992. He received the B.Eng. degree in Flight Vehicle Propulsion Engineering and the Ph. D. degree in Aerospace Propulsion Theory and Engineering from Beihang University in 2014 and 2019, respectively. He is currently an Assistant Professor in the Department of Power Engineering at Xiamen University. He is the author of several technical papers and also a very active reviewer for many international journals and conferences. His current research interests include surrogate model, intelligent optimization algorithm, multidisciplinary design optimization and structural topology optimization.
Nianyin Zeng
Nianyin Zeng was born in Fujian Province, China, in 1986. He received the B.Eng. degree in electrical engineering and automation in 2008 and the Ph. D. degree in electrical engineering in 2013, both from Fuzhou University. From October 2012 to March 2013, he was a RA in the Department of Electrical and Electronic Engineering, the University of Hong Kong. From September 2017 to August 2018, he as an ISEF Fellow founded by the Korea Foundation for Advance Studies and also a Visiting Professor at the Korea Advanced Institute of Science and Technology.
Currently, he is an Associate Professor with the Department of Instrumental & Electrical Engineering of Xiamen University. His current research interests include intelligent data analysis, computational intelligent, time-series modeling and applications. He is the author or co-author of several technical papers and also a very active reviewer for many international journals and conferences.
Dr. Zeng is currently serving as Associate Editors for Neurocomputing, Evolutionary Intelligence, and Frontiers in Medical Technology, and also Editorial Board members for Computers in Biology and Medicine, Biomedical Engineering Online, and Mathematical Problems in Engineering.