A modified BLISCO method and its combination with variable fidelity metamodel for engineering design

Jiang, Ping; Zhou, Qi; Shao, Xinyu; Ren, Long; Zhou, Hui

doi:10.1108/ec-06-2015-0164

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…To help readers understand these MDO architecture more clearly, Table 2 summarizes the characteristics and applications of the common architectures mentioned above. In recent years, a variety of hybrid framework strategies, such as MDF-CSSO, BLISS-CO, and BLISS-MDF, have increasingly attracted attention due to their unique advantages that are not present under single frameworks [60].…”

Section: Classification Of Mdo Architecturesmentioning

confidence: 99%

A Review of Uncertainty-Based Multidisciplinary Design Optimization Methods Based on Intelligent Strategies

Wang,

Fan,

Qiang

2023

Symmetry

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The design of aerospace systems is recognized as a complex interdisciplinary process. Many studies have shown that the exchange of information among multiple disciplines often results in strong coupling and nonlinearity characteristics in system optimization. Meanwhile, inevitable multi-source uncertainty factors continuously accumulate during the optimization process, greatly compromising the system’s robustness and reliability. In this context, uncertainty-based multidisciplinary design optimization (UMDO) has emerged and has been preliminarily applied in aerospace practices. However, it still encounters major challenges, including the complexity of multidisciplinary analysis modeling, and organizational and computational complexities of uncertainty analysis and optimization. Extensive research has been conducted recently to address these issues, particularly uncertainty analysis and artificial intelligence strategies. The former further enriches the UMDO technique, while the latter makes outstanding contributions to addressing the computational complexity of UMDO. With the aim of providing an overview of currently available methods, this paper summarizes existing state-of-the art UMDO technologies, with a special focus on relevant intelligent optimization strategies.

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Section: Classification Of Mdo Architecturesmentioning

confidence: 99%

A Review of Uncertainty-Based Multidisciplinary Design Optimization Methods Based on Intelligent Strategies

Wang,

Fan,

Qiang

2023

Symmetry

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“…Compared with NRCO, NRMCO achieves a more efficient optimization process in terms of the total number of iterations. Furthermore, Bi-level integrated system (BLISCO: Zhao and Cui, 2011), and modified BLISCO (Jiang et al, 2016) are applied to this problem. The comparisons with these methods, in terms of both accuracy and computational efficiency, indicate that NRMCO is more accurate and computationally more efficient for this problem.…”

Section: Analytical Optimization Problemmentioning

confidence: 99%

Multidisciplinary collaborative optimization based on relaxation method for solving complex problems

Chagraoui

Soula

2020

Concurrent Engineering

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The purpose of the present work is to improve the performance of the standard collaborative optimization (CO) approach based on an existing dynamic relaxation method. This approach may be weakened by starting design points. First, a New Relaxation (NR) method is proposed to solve the difficulties in convergence and low accuracy of CO. The new method is based on the existing dynamic relaxation method and it is achieved by changing the system-level consistency equality constraints into relaxation inequality constraints. Then, a Modified Collaborative Optimization (MCO) approach is proposed to eliminate the impact of the information inconsistency between the system-level and the discipline-level on the feasibility of optimal solutions. In the MCO approach, the impact of the inconsistency is treated by transforming the discipline-level constrained optimization problems into an unconstrained optimization problem using an exact penalty function. Based on the NR method, the performance of the MCO approach carried out by solving two multidisciplinary optimization problems. The obtained results show that the MCO approach has improved the convergence of CO significantly. These results prove that the present MCO succeeds in getting feasible solutions while the CO fails to provide feasible solutions with the used starting design points.

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“…It is a common practice that some preexisting variables are not allowed to be adjusted due to the manufacturing technology restrictions (e.g. when optimizing the hull form parameters of a small water-plane area twin hull catamaran, the rated output of the engineering is set to be a fixed value, while there are variations in the engineering power (Jiang et al , 2016b) or material properties [e.g. mass density is usually a constant value during optimization design, but there are variations in the mass density (Li et al , 2009)].…”

Section: Background Definitions and Terminologymentioning

confidence: 99%

An on-line Kriging metamodel assisted robust optimization approach under interval uncertainty

Zhou

Jiang

Shao

et al. 2017

Self Cite

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Purpose Uncertainty is inevitable in real-world engineering optimization. With an outer-inner optimization structure, most previous robust optimization (RO) approaches under interval uncertainty can become computationally intractable because the inner level must perform robust evaluation for each design alternative delivered from the outer level. This paper aims to propose an on-line Kriging metamodel-assisted variable adjustment robust optimization (OLK-VARO) to ease the computational burden of previous VARO approach. Design/methodology/approach In OLK-VARO, Kriging metamodels are constructed for replacing robust evaluations of the design alternative delivered from the outer level, reducing the nested optimization structure of previous VARO approach into a single loop optimization structure. An on-line updating mechanism is introduced in OLK-VARO to exploit the obtained data from previous iterations. Findings One nonlinear numerical example and two engineering cases have been used to demonstrate the applicability and efficiency of the proposed OLK-VARO approach. Results illustrate that OLK-VARO is able to obtain comparable robust optimums as to that obtained by previous VARO, while at the same time significantly reducing computational cost. Practical implications The proposed approach exhibits great capability for practical engineering design optimization problems under interval uncertainty. Originality/value The main contribution of this paper lies in the following: an OLK-VARO approach under interval uncertainty is proposed, which can significantly ease the computational burden of previous VARO approach.

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A modified BLISCO method and its combination with variable fidelity metamodel for engineering design

Cited by 8 publications

References 26 publications

A Review of Uncertainty-Based Multidisciplinary Design Optimization Methods Based on Intelligent Strategies

A Review of Uncertainty-Based Multidisciplinary Design Optimization Methods Based on Intelligent Strategies

Multidisciplinary collaborative optimization based on relaxation method for solving complex problems

An on-line Kriging metamodel assisted robust optimization approach under interval uncertainty

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