A multi-objective optimization methodology based on multi-mid-range meta-models for multimodal deterministic/robust problems

It is a global issue to set up a practical, sensitive, and useful model to eradicate or mitigate the coronavirus disease 2019 (COVID-19). Taking Central China’s Hubei Province for example, three models were established. Firstly, a susceptible-probable-infectious-recovered (SPIR) model was proposed to predict the monthly number of confirmed and susceptible cases in each city. Next, an epidemic prefecture clustering model was set up to find proper vaccine delivery sites, according to the distance of each city. Finally, a dynamic material delivery optimization model was established for multiple epidemic prefectures, aiming to speed up vaccine production and storage in each delivery site.

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“…Based on SPIR model, this article establishes a dynamic emergency delivery optimization model for multiple epidemic prefectures [29,30].…”

Section: Dynamic Materials Delivery Optimization Modelmentioning

confidence: 99%

[Retracted] A Multiobjective Optimization Model for Prevention and Control of Coronavirus Disease 2019 in China

Xiao-cheng

Zhao-li

2022

Discrete Dynamics in Nature and Society

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“…32 The latter has an important exploration capability, which makes it suitable for large design spaces. 33 The vista-CCD is used as code and it is a mean line design tool for centrifugal compressors. Given the inputs in Table 1, the program will build the blade geometry and provide essential nondimensional performance parameters, including specific speed and flow coefficient.…”

Section: Description Of the Proposed Pd Optimization Methodsmentioning

confidence: 99%

An efficient methodology to generate optimal inputs for the preliminary design of centrifugal compressor impellers

Bourabia

Khalfallah

Cerdoun

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part E:

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Preliminary design of centrifugal compressors is an emphasized step, which initiates the design process. Even with the use of quick analysis methods such as one-dimensional models, preliminary design still occupies a substantial part of the total design time. Among the factors that can lengthen this time or even cause the design failure is the inappropriate selection of the design input parameters. The present paper proposes a methodology to generate optimal inputs for the preliminary design, which reduces the design time and optimizes its overall performance. This is achieved, firstly, by performing an aerothermodynamic analysis that defines the appropriate input parameters, which will be used by a preliminary design code. This analysis has allowed identifying three pilot parameters (inlet relative Mach number, work input factor, and slip factor) to guide the generation of adequate input parameters. This input parameter generator, which reduces considerably the failure rate, is then exploited efficiently in an optimization process considering the pilot parameters as decision variables. Therefore, the proposed input parameter generator is coupled with a preliminary design code and an optimization algorithm. The proposed input parameter generator was validated on four existing compressors, showing a gain of more than 90% of the design time. Mainly, the proposed optimization has created a preliminary design trade-off having the target requirements and with optimized off-design performance.

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“…The variant FitRBF 14 has many advantages, notably, an effective hybrid optimization of the shape factor; relatively reduced cost with the use of an ill-conditioning model, 50 and the DoE-safeguard option which limits the DoE size by discarding non-important samples during the adaptive refinement. 14…”

Section: Meta-model Selection and Constructionmentioning

confidence: 99%

“…This process is known as uncertainty propagation, and it can be achieved by statistical means such as expectation and variance. 14 In single-objective problems, using the mean and the variance as measures of robustness is widely adopted. This consideration is suitable when transforming the sing-objective problem into a multi-objective problem where the objectives become the mean and the variance.…”

Section: Optimization Algorithmsmentioning

confidence: 99%

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Robust optimization of the aerodynamic design of a helicopter rotor blade based on multi-fidelity meta-models

Eddine

Khalfallah

Cerdoun

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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Robust optimization (RO) of designs becomes indispensable to deal with inherent manufacturing uncertainties. Yet, RO is still too expensive to solve complex problems such as the design of helicopter rotor blades. A promising way to reduce this cost is to use the multi-fidelity meta-model-based optimization (MFMBO), which combines low fidelity models (LFM), high fidelity models (HFM), meta-models, design of experiment (DoE), and optimization techniques. The field of MFMBO is not been completely explored despite the high number of proposed strategies. This paper contributes to the MFMBO cost-effectiveness improvement by proposing a new strategy. The main feature of the latter is the mutual adaptive refinement of the HFM and LFM DoE sets. A new idea is proposed to generate initial nested DoEs, which is implemented using an ordinary and an optimal Latin Hyper Cube method. These DoEs are refined adaptively and mutually using an improved MaxMin-distance criterion. The expensive HFM is used only to define a correction C of the LFM. C is used to adjust LFM evaluations during MBO. Meta-models are constructed using the Radial Basis Functions technique. The strategy is tested with two RO techniques, namely, the Non-Dominated Sorting Genetic Algorithm (NSGA-II) and the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D). The implemented strategy is validated on three different mathematical benchmarks, compared with four different MFMBO strategies and applied on a helicopter rotor blade. This validation shows that the proposed MFMBO is competitive and can produce solutions as accurate as the HFM-based MBO while reducing significantly the overall computation time (up to 40%).

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A multi-objective optimization methodology based on multi-mid-range meta-models for multimodal deterministic/robust problems

Cited by 8 publications

References 50 publications

[Retracted] A Multiobjective Optimization Model for Prevention and Control of Coronavirus Disease 2019 in China

[Retracted] A Multiobjective Optimization Model for Prevention and Control of Coronavirus Disease 2019 in China

An efficient methodology to generate optimal inputs for the preliminary design of centrifugal compressor impellers

Robust optimization of the aerodynamic design of a helicopter rotor blade based on multi-fidelity meta-models

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