SMWO/D: a decomposition-based switching multi-objective whale optimiser for structural optimisation of Turbine disk in aero-engines

Han Li, Haonan Liu, Chengbo Lan, Yiqi Yin, Peishu Wu, Cheng Yan and Nianyin Zeng

International Journal of Systems Science, 2023, vol. 54, issue 8, 1713-1728

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.

Date: 2023
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DOI: 10.1080/00207721.2023.2209873

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