Robust optimization with convex model considering bounded constraints on performance variation

Hu, Naigang; Duan, Baoyan; Cao, Hongjun; Zong, Yali

doi:10.1007/s00158-016-1647-3

Cited by 17 publications

(5 citation statements)

References 28 publications

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“…The design parameters change during the refinement process, and the objective refinement is realized by their changes. The satisfaction of performance is subjectively determined by the expectations of designers and users, so the measurement index of robustness is not unique, which means that robust-ness analysis is a necessary link in the process of information system design and operation [28]. For stable systems, the real part of eigenvalues is greater than zero, so only PΔ W c ð∞Þ needs to be considered.…”

Section: Solution Of Failure Probability Of Built-in Informationmentioning

confidence: 99%

Robust Refinement of Built-in Network Information System Based on Nonparametric Density Estimation

Mei¹,

Wang

2022

Journal of Sensors

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In the research process of robustness refinement solution of built-in information systems for electronic networks, there are too many factors related to robustness in the current system robustness design, and different factors have different influences on robustness. In stochastic programming problems, uncertain variables usually obey a certain probability distribution, but in real decision-making, these determined distributions are often unknown or we only know part of the information of the distributions, and distributed robust refinement solution is just an effective solution to solve uncertain problems. The robustness measurement solution of information systems for electronic networks is analyzed. The robust refinement of information system is deeply studied by nonparametric density estimation solution, which is based on the strict robustness requirements put forward by users. Based on the research results of interdependent network theory and aiming at “improving the robustness of electronic information system,” this paper makes an in-depth study on the robustness refinement strategy of power information system. The comparison between the companies that adopted the robust refinement of built-in information systems for electronic networks based on nonparametric density estimation and the companies that did not adopt it shows that the refinement rate of the companies that adopted it in the first three years was 82%, while that of the companies that did not adopt it was only 57%, and the overall misjudgment rate was 43%. Therefore, it is proved that using the proposed reliability refinement solution to optimize, the embedded system can improve the service life, modeling accuracy, and availability of the system and has certain practicability.

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Section: Solution Of Failure Probability Of Built-in Informationmentioning

confidence: 99%

Robust Refinement of Built-in Network Information System Based on Nonparametric Density Estimation

Mei¹,

Wang

2022

Journal of Sensors

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“…Wang和Qing [35] 提出一种区间运算和仿射运算混合方法求解区间鲁棒性优化问题. Hu等人 [36] 提出了一个新的鲁棒性指标来表示约束的变动量, 并将区间鲁棒性优化问题转化为确定性优化问题进行求解. Cheng等人 [37] 提出了一种区间约束违反程度正则化(NVDIC)方法, 对不同的设计向量进行分类 .…”

Section: 因此在不确定性领域发展了一种非概率不确定unclassified

An interval robust optimization method for uncertain structures

Jiang¹,

Long²,

Zhang³

et al. 2019

Sci. Sin.-Tech.

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“…In fact, this method can be considered as a meta-modal (surrogateassisted model) which suffers from the drawbacks mentioned above. Another similar interpolation-based meta-models were proposed in [39,40,41,42].…”

Section: Related Work and Problem Backgroundmentioning

confidence: 99%

Confidence-based robust optimisation using multi-objective meta-heuristics

Mirjalili

Lewis

Dong

2018

Swarm and Evolutionary Computation

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Robust optimisation refers to the process of finding optimal solutions that have the lowest sensitivity to possible perturbations. In a multi-objective search space the robust optimal solutions should have the least dispersion on all of the objectives, making it a more challenging problem than in a single-objective search space. This paper establishes a novel and cheap technique for finding robust optimal solutions called confidence-based robust multi-objective optimisation. This approach uses a novel, modified Pareto dominance operator to differentiate search agents of meta-heuristics based on both level of robustness and confidence. The proposed confidence-based Pareto dominance allows us to design different confidence-based robust optimisation variants of meta-heuristics based on different methods. As a case study, robust Multi-Objective Particle Swarm Optimisation is equipped with the proposed operator to produce Confidence-based Robust Multi-Objective Particle Swarm Optimisation. A set of specific test functions and performance indicators is employed for benchmarking the Confidence-based Robust Multi-Objective Particle Swarm Optimisation.The results show that the proposed method is able to confidently and reliably find robust optimal solutions without significant extra computational burden. The paper also considers finding robust Pareto optimal front for a marine propeller design problem to demonstrate the applicability of the approach proposed in solving computationally expensive real-world problems with unknown true robust Pareto optimal fronts.

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Robust optimization with convex model considering bounded constraints on performance variation

Cited by 17 publications

References 28 publications

Robust Refinement of Built-in Network Information System Based on Nonparametric Density Estimation

Robust Refinement of Built-in Network Information System Based on Nonparametric Density Estimation

An interval robust optimization method for uncertain structures

Confidence-based robust optimisation using multi-objective meta-heuristics

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