SMWO/D: a decomposition-based switching multi-objective whale optimiser for structural optimisation of turbine disk in aero-engines. (English) Zbl 1531.90119
Summary: 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.
Keywords:
multidisciplinary design optimisation (MDO); switching; multi-objective optimisation algorithm (MOA); aero-engine; structural optimisationReferences:
| [1] | Ahmadi, P.; Rahmani, M.; Shahmansoorian, A., LQR based optimal co-design for linear control systems with input and state constraints, International Journal of Systems Science, 54, 5, 1136-1149 (2023) · Zbl 1520.93156 · doi:10.1080/00207721.2023.2168142 |
| [2] | Bao, G.; Ma, L.; Yi, X., Recent advances on cooperative control of heterogeneous multi-agent systems subject to constraints: a survey, Systems Science & Control Engineering, 10, 1, 539-551 (2022) · doi:10.1080/21642583.2022.2074169 |
| [3] | Cai, X.; Li, Y.; Fan, Z.; Zhang, Q., An external archive guided multiobjective evolutionary algorithm based on decomposition for combinatorial optimization, IEEE Transactions on Evolutionary Computation, 19, 4, 508-523 (2014) · doi:10.1109/TEVC.2014.2350995 |
| [4] | Cui, D.; Feng, G.; Zhou, P.; Zhang, Y., Parametric design-based multi-objective optimisation for high-pressure turbine disc, International Journal of Production Research, 55, 17, 4847-4861 (2017) · doi:10.1080/00207543.2016.1259669 |
| [5] | Cui, Q.; Liu, K.; Ji, Z.; Song, W., Sampling-data-based distributed optimisation of second-order multi-agent systems with PI strategy, International Journal of Systems Science, 1-14 (2023) · Zbl 1520.93300 · doi:10.1080/00207721.2023.2173541 |
| [6] | Deb, K., Thiele, L., Laumanns, M., & Zitzler, E. (2002). Scalable multi-objective optimization test problems. In Proceedings of the 2002 congress on evolutionary computation (Vol. 1, pp. 825-830). IEEE. |
| [7] | Dhawale, D.; Kamboj, V.; Anand, P., An effective solution to numerical and multi-disciplinary design optimization problems using chaotic slime mold algorithm, Engineering with Computers, 38, Suppl 4, 2739-2777 (2021) · doi:10.1007/s00366-021-01409-4 |
| [8] | Guo, Y.; Liu, Y.; Wu, Y.; Cao, H.; Mo, D., Design optimization and burst speed prediction of a Ti2AlNb blisk, International Journal of Aerospace Engineering, 2021, 1-12 (2021) · doi:10.1155/2021/3290518 |
| [9] | Hu, D.; Yang, J.; Fei, C.; Wang, R.; Choy, Y., Reliability-based design optimization method of turbine disk with transformed deterministic constraints, Journal of Aerospace Engineering, 30, 1 (2017) · doi:10.1061/(ASCE)AS.1943-5525.0000671 |
| [10] | Hu, J.; Jia, C.; Liu, H.; Yi, X.; Liu, Y., A survey on state estimation of complex dynamical networks, International Journal of Systems Science, 52, 16, 3351-3367 (2021) · doi:10.1080/00207721.2021.1995528 |
| [11] | Huang, L.; Li, H.; Zheng, K.; Tian, K.; Wang, B., Shape optimization method for axisymmetric disks based on mesh deformation and smoothing approaches, Mechanics of Advanced Materials and Structures, 1-24 (2022) · doi:10.1080/15376494.2022.2058658 |
| [12] | Huang, Z.; Zhu, D.; Liu, Y.; Wang, X., Multi-strategy sparrow search algorithm with non-uniform mutation, Systems Science & Control Engineering, 10, 1, 936-954 (2022) · doi:10.1080/21642583.2022.2140723 |
| [13] | Huband, S.; Hingston, P.; Barone, L.; While, L., A review of multiobjective test problems and a scalable test problem toolkit, IEEE Transactions on Evolutionary Computation, 10, 5, 477-506 (2006) · doi:10.1109/TEVC.2005.861417 |
| [14] | Jiang, S.; Yang, S.; Wang, Y.; Liu, X., Scalarizing functions in decomposition-based multiobjective evolutionary algorithms, IEEE Transactions on Evolutionary Computation, 22, 2, 296-313 (2017) · doi:10.1109/TEVC.2017.2707980 |
| [15] | Kou, Z.; Sun, J., Test-based model-free adaptive iterative learning control with strong robustness, International Journal of Systems Science, 1-16 (2023) · Zbl 1520.93150 · doi:10.1080/00207721.2023.2169057 |
| [16] | Li, G.; Ding, S.; Bao, M.; Sun, H., Effect of actively managed thermal-loading in optimal design of an aeroengine turbine disk, International Communications in Heat and Mass Transfer, 81, 257-268 (2017) · doi:10.1016/j.icheatmasstransfer.2016.12.024 |
| [17] | Li, H.; Li, J.; Wu, P.; You, Y.; Zeng, N., A ranking-system-based switching particle swarm optimizer with dynamic learning strategies, Neurocomputing, 494, 356-367 (2022) · doi:10.1016/j.neucom.2022.04.117 |
| [18] | Li, H.; Wu, P.; Zeng, N.; Liu, Y.; Alsaadi, F., A survey on parameter identification, state estimation and data analytics for lateral flow immunoassay: from systems science perspective, International Journal of Systems Science, 53, 16, 3556-3576 (2022) · Zbl 1518.92076 · doi:10.1080/00207721.2022.2083262 |
| [19] | Li, H.; Zhang, Q.; Deng, J., Biased multiobjective optimization and decomposition algorithm, IEEE Transactions on Cybernetics, 47, 1, 52-66 (2016) · doi:10.1109/TCYB.2015.2507366 |
| [20] | Li, L.; Tang, Z.; Li, H.; Gao, W.; Yue, Z.; Xie, G., Convective heat transfer characteristics of twin-web turbine disk with pin fins in the inner cavity, International Journal of Thermal Sciences, 152 (2020) · doi:10.1016/j.ijthermalsci.2020.106303 |
| [21] | Li, W.; Gao, L.; Garg, A.; Xiao, M., Multidisciplinary robust design optimization considering parameter and metamodeling uncertainties, Engineering with Computers, 38, 191-208 (2022) · doi:10.1007/s00366-020-01046-3 |
| [22] | Liang, Z.; Liang, W.; Wang, Z.; Ma, X.; Liu, L.; Zhu, Z., Multiobjective evolutionary multitasking with two-stage adaptive knowledge transfer based on population distribution, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52, 7, 4457-4469 (2021) · doi:10.1109/TSMC.2021.3096220 |
| [23] | Liang, Z.; Xu, X.; Liu, L.; Tu, Y.; Zhu, Z., Evolutionary many-task optimization based on multisource knowledge transfer, IEEE Transactions on Evolutionary Computation, 26, 2, 319-333 (2021) · doi:10.1109/TEVC.2021.3101697 |
| [24] | Liu, Y.; Zhu, N.; Li, M., Solving many-objective optimization problems by a Pareto-based evolutionary algorithm with preprocessing and a penalty mechanism, IEEE Transactions on Cybernetics, 51, 11, 5585-5594 (2020) · doi:10.1109/TCYB.2020.2988896 |
| [25] | Meng, D.; Yang, S.; He, C.; Wang, H.; Lv, Z.; Guo, Y.; Nie, P., Multidisciplinary design optimization of engineering systems under uncertainty: a review, International Journal of Structural Integrity, 13, 4, 565-593 (2022) · doi:10.1108/IJSI-05-2022-0076 |
| [26] | Mirjalili, S.; Lewis, A., The whale optimization algorithm, Advances in Engineering Software, 95, 51-67 (2016) · doi:10.1016/j.advengsoft.2016.01.008 |
| [27] | Pinto, E.; Nepomuceno, E.; Campanharo, A., Individual-based modelling of animal brucellosis spread with the use of complex networks, International Journal of Network Dynamics and Intelligence, 1, 1, 120-129 (2022) · doi:10.53941/ijndi0101011 |
| [28] | Rindi, A.; Meli, E.; Boccini, E.; Iurisci, G.; Corbò, S.; Falomi, S., Static and modal topology optimization of turbomachinery components, Journal of Engineering for Gas Turbines and Power, 138, 11, 9-18. (2016) · doi:10.1115/1.4033512 |
| [29] | Shakiba, F.; Shojaee, M.; Azizi, S.; Zhou, M., Real-time sensing and fault diagnosis for transmission lines, International Journal of Network Dynamics and Intelligence, 1, 1, 36-47 (2022) · doi:10.53941/ijndi0101004 |
| [30] | Song, B.; Miao, H.; Xu, L., Path planning for coal mine robot via improved ant colony optimization algorithm, Systems Science & Control Engineering, 9, 1, 283-289 (2021) · doi:10.1080/21642583.2021.1901158 |
| [31] | Song, J.; Zhang, Y.; Guo, X.; Gao, H.; Wen, W.; Cui, H., Topology and shape optimization of twin-web turbine disk, Structural and Multidisciplinary Optimization, 65, 2, 44 (2022) · doi:10.1007/s00158-021-03147-z |
| [32] | Srinivas, M.; Patnaik, L., Genetic algorithms: a survey, Computer, 27, 6, 17-26 (1994) · doi:10.1109/2.294849 |
| [33] | Su, Y.; Cai, H.; Huang, J., The cooperative output regulation by the distributed observer approach, International Journal of Network Dynamics and Intelligence, 1, 1, 20-35 (2022) · doi:10.53941/ijndi0101003 |
| [34] | Tian, Y.; Cheng, R.; Zhang, X.; Jin, Y., PlatEMO: A MATLAB platform for evolutionary multi-objective optimization [educational forum], IEEE Computational Intelligence Magazine, 12, 4, 73-87 (2017) · doi:10.1109/MCI.2017.2742868 |
| [35] | Wang, R.; Zhang, Q.; Zhang, T., Decomposition-based algorithms using Pareto adaptive scalarizing methods, IEEE Transactions on Evolutionary Computation, 20, 6, 821-837 (2016) · doi:10.1109/TEVC.2016.2521175 |
| [36] | Wang, X.; Sun, Y.; Ding, D., Adaptive dynamic programming for networked control systems under communication constraints: a survey of trends and techniques, International Journal of Network Dynamics and Intelligence, 1, 1, 85-98 (2022) · doi:10.53941/ijndi0101008 |
| [37] | Wang, X.; Zhang, K.; Wang, J.; Jin, Y., An enhanced competitive swarm optimizer with strongly convex sparse operator for large-scale multiobjective optimization, IEEE Transactions on Evolutionary Computation, 26, 5, 859-871 (2021) · doi:10.1109/TEVC.2021.3111209 |
| [38] | Wei, Y.; Wei, X.; Huang, H.; Bi, J.; Zhou, Y.; Du, Y., SSMA: simplified slime mould algorithm for optimization wireless sensor network coverage problem, Systems Science & Control Engineering, 10, 1, 662-685 (2022) · doi:10.1080/21642583.2022.2084650 |
| [39] | Xu, L.; Song, B.; Cao, M., An improved particle swarm optimization algorithm with adaptive weighted delay velocity, Systems Science & Control Engineering, 9, 1, 188-197 (2021) · doi:10.1080/21642583.2021.1891153 |
| [40] | Yan, C.; Hao, W.; Yin, Y.; Zeng, N.; Du, H.; Song, D., Stress optimization of vent holes with different shapes using efficient switching delayed PSO algorithm, Applied Sciences, 12, 11, 5395 (2022) · doi:10.3390/app12115395 |
| [41] | Yan, C.; Yin, Z.; Shen, X.; Mi, D.; Guo, F.; Long, D., Surrogate-based optimization with improved support vector regression for non-circular vent hole on aero-engine turbine disk, Aerospace Science and Technology, 96 (2020) · doi:10.1016/j.ast.2019.105332 |
| [42] | Zeng, N.; Song, D.; Li, H.; You, Y.; Liu, Y.; Alsaadi, F., A competitive mechanism integrated multi-objective whale optimization algorithm with differential evolution, Neurocomputing, 432, 170-182 (2021) · doi:10.1016/j.neucom.2020.12.065 |
| [43] | Zeng, N.; Wang, Z.; Liu, W.; Zhang, H.; Hone, K.; Liu, X., A dynamic neighborhood-based switching particle swarm optimization algorithm, IEEE Transactions on Cybernetics, 52, 9, 9290-9301 (2020) · doi:10.1109/TCYB.2020.3029748 |
| [44] | Zhang, Q.; Li, H., MOEA/D: a multiobjective evolutionary algorithm based on decomposition, IEEE Transactions on Evolutionary Computation, 11, 6, 712-731 (2007) · doi:10.1109/TEVC.2007.892759 |
| [45] | Zhang, Q.; Zhou, Y., Recent advances in non-Gaussian stochastic systems control theory and its applications, International Journal of Network Dynamics and Intelligence, 1, 1, 111-119 (2022) · doi:10.53941/ijndi0101010 |
| [46] | Zitzler, E.; Deb, K.; Thiele, L., Comparison of multiobjective evolutionary algorithms: empirical results, Evolutionary Computation, 8, 2, 173-195 (2000) · doi:10.1162/106365600568202 |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
