Some studies on differential evolution variants for application to nuclear reactor core design

Sacco, Wagner F.; Meneses, Anderson Alvarenga de Moura; Henderson, Nélio

doi:10.1016/j.pnucene.2012.10.003

Cited by 18 publications

(4 citation statements)

References 22 publications

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“…More recently, Sacco et al 8 applied DE optimization to a nuclear core design problem. They compared the performance of two DE extensions: trigonometric mutation and opposition-based learning.…”

Section: Related Workmentioning

confidence: 99%

Empirical Comparison of Differential Evolution Variants for Industrial Controller Design

Wong¹,

Bigras²,

Duchaîne³

et al. 2016

IJCIS

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To be cost-effective, most commercial off-the-shelf industrial controllers have low system order and a predefined internal structure. When operating in an industrial environment, the system output is often specified by a reference model, and the control system must closely match the model's response. In this context, a valid controller design solution must satisfy the application specifications, fit the controller's configuration and meet a model matching criterion. This paper proposes a method of solving the design problem using bilinear matrix inequality formulation, and the use of Differential Evolution (DE) algorithms to solve the resulting optimization problem. The performance of the proposed method is demonstrated by comparing a set of ten DE variants. Extensive statistical analysis shows that the variants and are effective in terms of mean best objective function value, average number of function evaluations, and objective function value progression.

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Section: Related Workmentioning

confidence: 99%

Empirical Comparison of Differential Evolution Variants for Industrial Controller Design

Wong¹,

Bigras²,

Duchaîne³

et al. 2016

IJCIS

View full text Add to dashboard Cite

show abstract

“…In addition, the implementation of DE only needs a few lines of code in any standard programming language, which makes it easy to realize for engineers of different optimization fields. Over the past two decades, DE and other intelligent algorithms have gained the promising performance in solving numerous practical engineering problems, such as, chemical engineering [2][3][4][5], electrical engineering [6][7][8][9], scheduling optimization [10,11], image processing [12][13][14][15], and structural optimization of neural network [16,17]. e convergence of DE and its variants is proved in the literature [18].…”

Section: Introductionmentioning

confidence: 99%

Differential Evolution with Autonomous Selection of Mutation Strategies and Control Parameters and Its Application

Wang

Cao

et al. 2022

Complexity

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The existing numerous adaptive variants of differential evolution (DE) have been improved the search ability of classic DE to certain extent. Nevertheless, those variants of DE do not obtain the promising performance in solving black box problems with unknown features, which is mainly because the adaptive rules of those variants are designed according to their designers’ cognition on the problem features. To enhance the optimization ability of DE in optimizing black box problems with unknown features, a differential evolution with autonomous selection of mutation strategies and control parameters (ASDE) is proposed in this paper, inspired by autonomous decision-making mechanism of reinforcement learning. In ASDE, a historical experience archive with population features is utilized to preserve accumulated historical experience of the combination of mutation strategies and control parameters. Furthermore, the accumulated historical experience can be autonomously mapped into rules repository, and the individuals can choose the combination of mutation strategies and control parameters according to those rules. Additionally, an updating and utilization mechanism of the historical experience is designed to assure that the historical experience can be effectively accumulated and utilized efficiently. Compared with some state-of-the-art intelligence algorithms on 15 functions of CEC2015, 28 functions of CEC2017, and parameter extraction problems of the photovoltaic model, ASDE has the advantages of solution accuracy, convergence speed, and robustness in solving black box problems with unknown features.

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“…A series of design criteria need to be taken into consideration in the fuel design process. Moreover, to achieve the accident tolerant capability, additional requirements are expected to arise in the development of ATF fuel systems, which makes the ATF fuel element design an intrinsic multi‐objective optimization problem 12,13 . Thus, it is imperative to implement the multi‐objective optimization method in the design of ATF fuel elements 14,15 .…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, to achieve the accident tolerant capability, additional requirements are expected to arise in the development of ATF fuel systems, which makes the ATF fuel element design an intrinsic multi-objective optimization problem. 12,13 Thus, it is imperative to implement the multi-objective optimization method in the design of ATF fuel elements. 14,15 However, as we mentioned above, most of the research focuses on the performance evaluation of U 3 Si 2 -FeCrAl ATF fuel system or trying to predict its in-core behaviors with a more accurate simulation method, few works considered the optimization design of the ATF fuel element.…”

mentioning

confidence: 99%

Multi‐objective optimization design of U ₃ Si ₂ –FeCrAl accident tolerant fuel elements based on Gaussian process and genetic algorithm

Chen

Zhou

et al. 2022

Intl J of Energy Research

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Summary Among various accident tolerant fuel (ATF) concepts, U3Si2–FeCrAl fuel systems are considered as ones of the most potential fuel for the next generation light water reactor. In this work, a multi‐objective optimization framework based on multi‐objective genetic algorithm (GA) and Gaussian process (GP) was established for the optimal design of U3Si2–FeCrAl ATF fuel element. The coupling of GA and surrogate GP model could take advantage of the high fidelity of multi‐physics modeling and the global searching capability of GA, therefore allowing to perform a relatively fast and accurate multi‐objective optimization design for the U3Si2–FeCrAl ATF fuel element. Maximum fuel temperature, maximum cladding Von–Mises stress, and maximum plenum pressure are considered as the three objective functions for the optimization problem in this study. According to the Pareto approximation surface obtained in the optimization process, a lowest maximum fuel temperature of 871 K, a lowest maximum cladding stress of 87.9 MPa, and a lowest plenum pressure of 3.1 MPa for the U3Si2–FeCrAl ATF fuel element could be achieved based on different design parameters. The multi‐objective optimization framework developed in this work could perform as an efficient tool for the multi‐objective optimization design of the ATF fuel element.

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Some studies on differential evolution variants for application to nuclear reactor core design

Cited by 18 publications

References 22 publications

Empirical Comparison of Differential Evolution Variants for Industrial Controller Design

Empirical Comparison of Differential Evolution Variants for Industrial Controller Design

Differential Evolution with Autonomous Selection of Mutation Strategies and Control Parameters and Its Application

Multi‐objective optimization design of U ₃ Si ₂ –FeCrAl accident tolerant fuel elements based on Gaussian process and genetic algorithm

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Some studies on differential evolution variants for application to nuclear reactor core design

Cited by 18 publications

References 22 publications

Empirical Comparison of Differential Evolution Variants for Industrial Controller Design

Empirical Comparison of Differential Evolution Variants for Industrial Controller Design

Differential Evolution with Autonomous Selection of Mutation Strategies and Control Parameters and Its Application

Multi‐objective optimization design of U 3 Si 2 –FeCrAl accident tolerant fuel elements based on Gaussian process and genetic algorithm

Contact Info

Product

Resources

About

Multi‐objective optimization design of U ₃ Si ₂ –FeCrAl accident tolerant fuel elements based on Gaussian process and genetic algorithm