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Mass Ionized Particle optimization algorithm Applied to Optimal FEA-Based Design of Electric Machine

IEEE TRANSACTIONS ON MAGNETICS 2016年第3期52卷

作者： Han, Wonseok Tran, Trung Tin Kim, Jong-Wook Kim, Yong-Jae Jung, Sang-Yong Sungkyunkwan Univ Sch Elect & Engn Suwon 440746 South Korea Dong A Univ Dept Elect Engn Busan 604714 South Korea Chosun Univ Dept Elect Engn Kwangju 501759 South Korea

A finite-element analysis-based optimal design of an electric machine takes considerable time for its objective evaluation and has many local minima. Thus, selecting an appropriate global convergence optimization with fast convergence speed is necessary in the optimal design of an electric machine. In this paper, a novel global search optimization algorithm, mass ionized particle optimization (MIPO), is newly proposed. The MIPO is the population-based algorithm, which reflects the interactive force between the ionized particles. The global convergence and the convergence speed are validated by comparison with the particle swarm optimization, which have already been proved for its global convergence when applied to a well-known Goldstein-Price function as a benchmark function. In addition, the algorithm has been applied to the optimal design of an interior permanent magnet synchronous machine aiming for its torque ripple reduction.

关键词： Electric machine finite-element analysis (FEA) optimization algorithm population-based algorithm

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Provably Convergent Multifidelity optimization algorithm Not Requiring High-Fidelity Derivatives

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AIAA JOURNAL 2012年第5期50卷 1079-1089页

作者： March, Andrew Willcox, Karen MIT Dept Aeronaut & Astronaut Cambridge MA 02139 USA

This paper presents a provably convergent multifidelity optimization algorithm for unconstrained problems that does not require high-fidelity gradients. The method uses a radial basis function interpolation to capture the error between a high-fidelity function and a low-fidelity function. The error interpolation is added to the low-fidelity function to create a surrogate model of the high-fidelity function in the neighborhood of a trust region. When appropriately distributed spatial calibration points are used, the low-fidelity function and radial basis function interpolation generate a fully linear model. This condition is sufficient to prove convergence in a trust region framework. In the case when there are multiple lower-fidelity models, the predictions of all calibrated lower-fidelity models can be combined with a maximum likelihood estimator constructed using kriging variance estimates from the radial basis function models. This procedure allows for flexibility in sampling lower-fidelity functions, does not alter the convergence proof of the optimization algorithm, and is shown to be robust to poor low-fidelity information. The algorithm is compared with a single-fidelity quasi-Newton algorithm and two first-order consistent multifidelity trust region algorithms. For simple functions the quasi-Newton algorithm uses slightly fewer high-fidelity function evaluations;however, for more complex supersonic airfoil design problems it uses significantly more. In all cases tested, our radial basis function calibration approach uses fewer high-fidelity function evaluations when compared with first-order consistent trust region schemes.

关键词： optimization algorithm Newton's Method Supersonic Airfoils Lift Coefficient Shock Expansion Theory Cart3D Gaussian Process Derivative Free Method Drag Coefficient Singular Value Decomposition

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Dynamic Machine Learning Global optimization algorithm and Its Application to Aerodynamics

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JOURNAL OF PROPULSION AND POWER 2023年第4期39卷 524-539页

作者： Zhang, Zi-Qing Li, Pei-Jing Li, Qing-Kuo Dong, Xu Lu, Xin-Gen Zhang, Yan-Feng Chinese Acad Sci Inst Engn Thermophys Key Lab Light Duty Gas Turbine Beijing 100080 Peoples R China Univ Chinese Acad Sci Sch Engn Sci Beijing 100049 Peoples R China Chinese Acad Sci Inst Engn Thermophys Distributed Generat Renewable Energy Lab Beijing 100080 Peoples R China Chinese Acad Sci Inst Engn Thermophys Key Lab Light Duty Gas Turbine Beijing 100080 Peoples R China

A dynamic extreme gradient boosting (XGBoost) and MaxLIPO trust region parallel global optimization algorithm is proposed in this paper, and it is applied to the turbomachinery blade aerodynamic optimization coupled with an in-house graphics processing unit (GPU) heterogeneous accelerated compressible flow solver, AeroWhale. The algorithm combines an accurate machine learning regression model, an efficient nongradient optimization method with no hyperparameters, a dynamic update regression strategy, and double convergence criteria to achieve high optimization accuracy and efficiency. The optimization results on the test function indicate that the number of objective function calls is less than 2% of that required by a traditional genetic algorithm, which greatly reduces the optimization time. The dynamic XGBoost model ensures that the regression model accuracy near the optimum is relatively high, which is attributed to the update strategy. The error between the optimal value identified by the proposed algorithm and the theoretical value is only 0.52% after several objective function calls. Finally, the aerodynamic optimization algorithm is applied to the LS89 high-pressure turbine, and the total pressure loss is reduced by 13.16%. The sensitivity of each optimization feature to the objective function is determined, showing that the blade suction surface control point near the trailing edge has the greatest impact on aerodynamic performance.

关键词： optimization algorithm Support Vector Machine Aerodynamic Characteristics High Pressure Turbine Turbomachinery Computational Fluid Dynamics XGBoost MaxLIPO GPU High Performance Computing

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A novel Master-Slave optimization algorithm for generating an optimal release policy in case of reservoir operation

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JOURNAL OF HYDROLOGY 2019年 577卷

作者： Turgut, Mert Sinan Turgut, Oguz Emrah Afan, Haitham Abdulmohsin El-Shafie, Ahmed Ege Univ Fac Engn Mech Engn Dept TR-35040 Izmir Turkey Izmir Bakircay Univ Fac Engn Mech Engn Dept TR-35665 Izmir Turkey Univ Malaya Fac Engn Civil Engn Dept Kuala Lumpur 50603 Malaysia

Dams and reservoirs provide decision-makers and managers with appropriate control on the available water resources, allowing the implementation of various strategies for the most efficient usage of the available water resources. In areas where water supply exhibits significant temporal variation when compared with the demand, the challenge is to bridge the gap and achieve an optimal match between the water supply and demand patterns. Therefore, the release of water from reservoirs should be controlled to ensure that the operation rule for the available water storage in the reservoir is optimized to satisfy the future water demands. This level of optimal control can only be achieved using an efficient optimization algorithm to optimally derive the operation rule for such a complex water system. Herein, two main methods have been considered to tackle this water resource management problem. First, three different optimization algorithms, namely particle swarm optimization, differential evolution, and whale optimization algorithm, have been applied. In addition, two different optimization algorithms, namely crow search algorithm and master-slave algorithm, have been introduced to generate an optimal rule for water release policy. Further, the proposed optimization algorithms have been applied to one of the most critical dam and reservoir water systems, namely the Aswan High Dam (AHD), which controls almost 95% of Egypt's water resources. The current operation of AHD using the existing optimization rules resulted in a mismatch between the water supply and water demand. In other words, the water availability could be higher than the water demand during a certain period, whereas it could be less than the water demand during another period. The results denoted that the master-slave algorithm outperforms the remaining algorithms and generates an optimization rule that minimizes the mismatch between the water supply and water demand.

关键词： Dam and reservoir operation optimization algorithm Aswan High Dam Crow search algorithm Master-slave algorithm

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Research on Voltage Compensation Methods and optimization algorithm for Insulated Core Transformer High-Voltage Power Supply

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ENERGIES 2024年第3期17卷

作者： Yang, Lei Liu, Xialing Yang, Jun China Elect Technol Grp Corp Res Inst 14 Nanjing 210013 Peoples R China NARI Grp Corp State Grid Elect Power Res Inst Nanjing 211106 Peoples R China Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China

The insulated core transformer (ICT) power supply is widely employed in electron beam accelerators (EBAs) due to its high power, heightened efficiency, and stable operation. However, the segmented-core structure of the ICT power supply increases magnetic leakage, which leads to it adversely affecting the consistency of the output voltages in the rectifier stages. Currently, numerous studies focus on stage voltage compensation, including turns compensation, capacitor compensation, dummy primary winding compensation, and full-parameter compensation. This paper presents a unified simulation model and an improved gradient-based genetic algorithm, which can also optimize the parameters of the four compensation methods. Based on this, the performance of the power supply using the four compensation methods under different ICT energy levels and power supply requirements is studied, and the selection suggestions are given. This work fills the gap in the performance comparison and application research of various compensation methods.

关键词： electron irradiation accelerator insulated core transformer voltage compensation optimization algorithm

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Dynamic fitness-distance balance-based artificial rabbits optimization algorithm to solve optimal power flow problem

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 240卷

作者： Bakir, Huseyin Dogus Univ Vocat Sch Dept Elect & Automat TR-34775 Istanbul Turkiye

Artificial rabbits optimization (ARO) is a swarm intelligence-based algorithm inspired by the survival strategies of rabbits. Although ARO has a good convergence rate, it is prone to get stuck in the local optima and converge prematurely. To overcome this, the present paper redesigns the exploration operator of the ARO algorithm with the roulette fitness-distance balance (RFDB) and dynamic fitness-distance balance (dFDB) strategies. In this context, three different versions of the fitness-distance balance-based artificial rabbits optimization (FDBARO) algorithm are developed. The performance of the original ARO and FDBARO versions (FDBARO-1, FDBARO-2, and FDBARO-3) are evaluated on CEC 2017 and CEC 2020 benchmark functions. The obtained results are analyzed with the Wilcoxon and Friedman statistical tests. Statistical and convergence analysis results showed that the FDBARO-3 algorithm designed with the dFDB selection method can explore the search space more successfully compared to other algorithms. This version was named the dynamic FDBARO (dFDBARO) algorithm. Moreover, the practicability of the proposed dFDBARO is highlighted by the solution of the optimal power flow (OPF) problem formulated with renewable energy sources (RESs) and flexible alternating current transmission system (FACTS) devices considering fixed and uncertain load demands. Experimental results showed that the proposed dFDBARO is a competitive algorithm for solving global optimization and constrained OPF problems. The source code of the dFDBARO algorithm is available at https://***/matlabcentral/filee xchange/154845-dfdbaro-an-enhanced-metaheuristic-algorithm.

关键词： A novel enhanced artificial rabbits optimization algorithm Metaheuristic algorithm design Roulette and dynamic fitness -distance balance selection methods Optimal power flow Renewable energy sources

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Multi-strategy improved sand cat optimization algorithm-based workflow scheduling mechanism for heterogeneous edge computing environment

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2024年 43卷

作者： Jayalakshmi, P. Ramesh, S. S. Subashka SRM Inst Sci & Technol Ramapuram Dept Comp Sci & Engn Chennai Tamil Nadu India

Edge computing is one of the predominant technologies which facilitates the option of bringing out the computing resources closer to the location of the end users when they are utilized by them. This facility offered by edge computing technology need to reduce the utilization of network bandwidth and response time with respect to the user's workflow. In this paper, Multi-Strategy Improved Sand Cat Swarm Optimisation algorithm (MSISCSOA)-based workflow scheduling mechanism is proposed for handling the challenges of workflow scheduling in cloud-edge computing environment. The core objective of this MSISCSOA-based workflow scheduling algorithm targets on minimizing the execution latency and energy consumption to facilitate timely and on-demand end users' satisfaction of resources. This MSISCSOA scheme is adopted with the improvement introduced using random variation and elite collaborative strategies, such that well-balanced the trade-off between exploration and exploitation is achieved. This improvement is introduced over Sand Cat optimization algorithm (SCOA) using the merits of dynamic random search and joint opposite selection strategies that accelerates the convergence of the algorithm with increased global optimization and searching efficiency. It specifically improved SCOA using random variation for escaping from the local point of optimality. It also used well distributed pareto fronts and population evolution multi-strategy that aids in searching solutions with maximized diversity. The simulation experiments conducted using the datasets of Montage, Cybershake, LIGO and SIPHT an average confirmed minimized execution latency of 21.38 % and energy consumptions of 19.56 %, better than the baseline Ant Colony optimization algorithm-Based Workflow Scheduling (IACOAWS), Quadratic Penalty Function-based Particle Swarm optimization algorithm (QPF-PSOA), Biogeography optimization (BBO) algorithm based Multi-Objective Task Scheduling (BBOAMOTS) and Different Evolution-ba

关键词： Cloud-edge computing Workflow scheduling Multi-strategies improved sand cat optimization algorithm Execution latency Energy consumptions Pareto optimal solutions

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Performance evaluation of nanotubular halloysites from weathered pegmatites in removing heavy metals from water through novel artificial intelligence-based models and human-based optimization algorithm

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CHEMOSPHERE 2021年 282卷 131012页

作者： Bui Hoang Bac Hoang Nguyen Nguyen Thi Thanh Thao Le Thi Duyen Vo Thi Hanh Nguyen Tien Dung Luong Quang Khang Do Manh An Hanoi Univ Min & Geol Fac Geosci & Geoengn Dept Explorat Geol Duc Thang Ward 18 Vien St Hanoi Vietnam Hanoi Univ Min & Geol Ctr Excellence Anal & Expt Duc Thang Ward 18 Vien St Hanoi Vietnam Hanoi Univ Min & Geol Min Fac Dept Surface Min Duc Thang Ward 18 Vien St Hanoi Vietnam Hanoi Univ Min & Geol Ctr Min Electromech Res Duc Thang Ward 18 Vien St Hanoi Vietnam Hanoi Univ Min & Geol Fac Basic Sci Dept Chem Duc Thang Ward 18 Vien St Hanoi Vietnam

The efforts of this study aimed to evaluate the feasibility of the nanotubular halloysites in weathered pegmatites (NaHWP) for removing heavy metals (i.e., Cd2+, Pb2+) from water. Furthermore, two novel intelligent models, such as teaching-learning-based optimization (TLBO)-artificial neural network (ANN), and TLBO-support vector regression (SVR), named as TLBO-ANN and TLBO-SVR models, respectively, were proposed to predict the Cd2+ and Pb2+ absorption efficiencies from water using the NaHWP absorbent. Databases used, including 53 experiments for Pb2+ absorption and 56 experiments for Cd2+ absorption from water, under the catalysis of different conditions, such as initial concentration of Pb2+ and Cd2+, solution pH, adsorbent weight, and contact time. Subsequently, the TLBO-ANN and TLBO-SVR models were developed and applied to predict the efficiencies of Cd2+ and Pb2+ absorption from water, aiming to evaluate the role as well as the effects of different conditions on the absorption efficiencies using the NaHWP absorbent. The standalone ANN and SVM models were also taken into consideration and compared with the proposed hybrid models (i.e., TLBO-ANN and TLBO-SVR). The results showed that the NaHWP detected in a Kaolin mine (Vietnam) with 70% nanotubular halloysites is a potential adsorbent for water treatment to eliminate heavy metals from water. The two novel hybrid models proposed, i.e., TLBO-ANN and TLBO-SVR, also yielded the dominant performances and accuracies in predicting the Cd2+ and Pb2+ absorption efficiencies from water, i.e., RMSE = 1.190 and 1.102, R-2 = 0.951 and 0.957, VAF = 94.436 and 95.028 for the TLBO-ANN and TLBO-SVR models, respectively, in predicting the Pb2+ absorption efficiency from water;RMSE = 3.084 and 3.442, R-2 = 0.971 and 0.965, VAF = 96.499 and 96.415 for the TLBO-ANN and TLBOSVR models, respectively, in predicting the Cd2+ absorption efficiency from water. Furthermore, the validation results also demonstrated these findings in practic

关键词： Heavy metals Water treatment Nanotubular halloysites Weathered pegmatites Artificial intelligence optimization algorithm

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Boosted sooty tern optimization algorithm for global optimization and feature selection

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 213卷

作者： Houssein, Essam H. Oliva, Diego Celik, Emre Emam, Marwa M. Ghoniem, Rania M. Minia Univ Fac Comp & Informat Al Minya Egypt Univ Guadalajara CUCEI Dept Innovac Basada Informat & Conocimiento Guadalajara Jalisco Mexico Duzce Univ Dept Elect & Elect Engn Fac Engn Duzce Turkiye Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol POB 84428 Riyadh 11671 Saudi Arabia Mansoura Univ Dept Comp Mansoura Egypt

Feature selection (FS) represents an optimization problem that aims to simplify and improve the quality of highly dimensional datasets through selecting prominent features and eliminating redundant and irrelevant data to classify results better. The goals of FS comprise dimensionality reduction and enhancing the classifica-tion accuracy in general, accompanied by great significance in different fields like data mining applications, pattern classification, and data analysis. Using powerful optimization algorithms is crucial to obtaining the best subsets of information in FS. Different metaheuristics, such as the Sooty Tern optimization algorithm (STOA), help to optimize the FS problem. However, such kind of techniques tends to converge in sub-optimal solutions. To overcome this problem in the STOA, an improved version called mSTOA is introduced. It employs the balancing exploration/exploitation strategy, self-adaptive of the control parameters strategy, and population reduction strategy. The proposed approach is proposed for solving the FS problem, but also it has been validated over benchmark optimization problems from the CEC 2020. To assess the performance of the mSTOA, it has also been tested with different algorithms. The experiments in terms of FS provide qualitative and quantitative evidence of the capabilities of the mSTOA for extracting the optimal subset of features. Besides, statistical analyses and no-parametric tests were also conducted to validate the result obtained by the mSTOA in optimization.

关键词： Sooty Tern optimization algorithm (STOA) Feature selection Dimensionality reduction Metaheuristics optimization algorithm

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Characterization of polyvinyl alcohol hydrogels as tissue-engineered cartilage scaffolds using a coupled finite element-optimization algorithm

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JOURNAL OF BIOMECHANICS 2020年 99卷 109525-109525页

作者： Nazouri, Mohadeseh Seifzadeh, Alireza Masaeli, Elahe Islamic Azad Univ Najafabad Branch Dept Med Engn Najafabad Iran ACECR Royan Inst Biotechnol Cell Sci Res Ctr Dept Cellular Biotechnol Esfahan Iran Islamic Azad Univ Khomeinishahr Branch Dept Med Engn Khomeinishahr Iran

Mechanical strength along with high biocompatibility and water absorbing are among main characteristics of a desirable scaffold for cartilage tissue engineering. Having these properties, polyvinyl alcohol (PVA) can be a good option for constructing cartilage tissue engineering scaffolds. In this study, PVA hydrogel was produced by freeze-thaw crosslinking method, and its mechanical properties such as viscoelastic and hyperplastic behavior, which cannot be obtained analytically, was investigated with a coupled finite element (FE)-optimization algorithm and stress relaxation experimental data. To obtain isotropic hyper-viscoelastic constitutive parameters of PVA scaffolds, the Mooney-Rivlin and Neo-Hooke strain energy functions, in which shear and bulk moduli varies with time, were applied. Results showed that predicted mechanical responses of scaffolds by the Mooney-Rivlin model better fitted stress-relaxation experiments than those obtained by Neo-Hooke one. Also, the properties obtained from the finite element model, such as the bulk and the shear moduli, showed that, after successful in vitro and in vivo experiments, PVA hydrogel may be introduced as a cartilage substitute for future tissue engineering therapies. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Cartilage Polyvinyl alcohol optimization algorithm Finite Element

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