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An Improved Nonlinear Active Disturbance Rejection Controller via Sine Function and whale optimization algorithm for Permanent Magnet Synchronous Motors Speed Control

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2025年第4期20卷 548-559页

作者： Wang, Longda Liu, Gang Xu, Chuanfang Dalian Jiaotong Univ Sch Elect Engn Dalian 116028 Peoples R China Inner Mongolia Minzu Univ Coll Engn Tongliao 028000 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

Permanent magnet synchronous motors (PMSMs) speed control has gained wide application in various fields. Specifically, there is a disadvantage that nonlinear functions in the conventional active disturbance rejection controller (ADRC) is non-differentiable at the piecewise points. Thus, an improved nonlinear active disturbance rejection controller (NLADRC) for permanent magnet synchronous motor speed control via sine function and whale optimization algorithm (WOA), abbreviated as NLADRC-sin-IWOA, is proposed to overcome this drawback. Considering the unsatisfactory control effect caused by the poor active disturbance resisting ability of the traditional PMSM controllers, this paper proposes an improved NLADRC for PMSM, that reconstructs a novel differentiable and smooth nonlinear function, the novel nonlinear function grounded on primitive function by the function of inverse hyperbolic, sine, square functions, and with difference fitting approach;and designs an improved whale optimization algorithm via convergence factor nonlinear decreasing, Gaussian variation and adaptive cross strategies. The experimental results findings show that the improved NLADRC-sin-IWOA has the advantages of response fast, small steady-state error and tiny overshoot. (c) 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

关键词： permanent magnet synchronous motor nonlinear active disturbance rejection controller whale optimization algorithm nonlinear function Gaussian mutation

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Minimum Zone Evaluation of Cylindricity Error Based on the Improved whale optimization algorithm

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2025年第4期26卷 859-875页

作者： Zheng, Peng He, Qingze Lyu, Xingchen Li, Jicun Li, Yan Zhengzhou Univ Sch Mech & Power Engn Zhengzhou 450001 Henan Peoples R China

The shape error of shaft-type mechanical components directly influences the quality, fitting precision and lifespan of the components. Among these, cylindricity error is a critical indicator for evaluating the precision of shaft-type components. Based on the new generation geometric product specifications & verification (GPS), this paper researched GPS inspection operation operators, determined extraction and fitting schemes, and constructed a mathematical model for cylindricity error evaluation. Consequently, a method for cylindricity error evaluation based on an improved whale optimization algorithm was proposed. The proposed algorithm introduced chaotic mapping and nonlinear parameters during population initialization to enhance solution quality. Subsequently, adaptive weighting coefficients were incorporated during spiral position updates to improve the algorithm's local search capability. Finally, Levy flight strategies were introduced during random search to enhance the algorithm's global search capability. This study conducted experimental validation and analysis by performing numerous comparative experiments on different extraction point numbers, cross-section numbers, evaluation criteria, and algorithms. The experimental results indicated that the proposed method for cylindricity error evaluation demonstrated significant improvements in both accuracy and efficiency compared to genetic algorithms, least squares methods, and others.

关键词： whale optimization algorithm Cylindricity error Geometric product specifications & verification Inspection operation operator

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Early Quality Prediction of Complex Double-Walled Hollow Turbine Blades Based on Improved whale optimization algorithm

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2025年第1期25卷 011003页

作者： Dong, Yiwei Gong, Yuhan Bo, Xu Tan, Zhiyong Xiamen Univ Sch Aerosp Engn 422 Siming Rd Xiamen 361005 Fujian Peoples R China AECC Commercial Aircraft Engine Co Ltd 3998 Lianhua South Rd Shanghai 200241 Peoples R China

The precision in forming complex double-walled hollow turbine blades significantly influences their cooling efficiency, making the selection of appropriate casting process parameters critical for achieving fine-casting blade formation. However, the high cost associated with real blade casting necessitates strategies to enhance product formation rates and mitigate cost losses stemming from the overshoot phenomenon. We propose a machine learning (ML) data-driven framework leveraging an enhanced whale optimization algorithm (WOA) to estimate product formation under diverse process conditions to address this challenge. Complex double-walled hollow turbine blades serve as a representative case within our proposed framework. We constructed a database using simulation data, employed feature engineering to identify crucial features and streamline inputs, and utilized a whale optimization algorithm-back-propagation neural network (WOA-BP) as the foundational ML model. To enhance WOA-BP's performance, we introduce an optimization algorithm, the improved chaos whale optimization-back-propagation (ICWOA-BP), incorporating cubic chaotic mapping adaptation. Experimental evaluation of ICWOA-BP demonstrated an average mean absolute error of 0.001995 mm, reflecting a 36.21% reduction in prediction error compared to conventional models, as well as two well-known optimization algorithms (particle swarm optimization (PSO), quantum-based avian navigation optimizer algorithm (QANA)). Consequently, ICWOA-BP emerges as an effective tool for early prediction of dimensional quality in complex double-walled hollow turbine blades.

关键词： early quality prediction double-walled hollow turbine blade cubic mapping whale optimization algorithm data-driven engineering machine learning for engineering applications physics-based simulations

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Near-Global-Optimal Resource Allocation for Uplink SCMA Systems Based on whale optimization algorithm

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第2期24卷 1089-1103页

作者： Soltanpour, Meysam Zhang, Haixia Shandong Key Lab Intelligent Commun Sensing Comp I Jinan 250061 Shandong Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Shandong Peoples R China

This paper investigates a joint resource allocation (RA) algorithm to maximize the average sum rate (ASR) of the sparse code multiple access (SCMA) random model-based networks. The ASR optimization problem turns out to be non-convex mixed-integer nonlinear programming (MINLP) problem which is challenging to address. The previous works strongly depend on the upper/lower bound of ASR, successive convex approximation (SCA) methods, initial assignment definition, post-processing procedures, and generating/training the data sets, thus cannot be applied. Given the above challenges, considering the ability of the whale optimization algorithm (WOA) in solving the non-convex MINLP NP-hard problems, we first construct the resource element assignment (REA) matrix utilizing the binary WOA (BWOA) through the reshaping the pattern of the arrangement of non-zero elements (PANs) and converting the structural constraints. Then we enhance the continuous WOA (CWOA) to mine the power allocation (PA) coefficients by using an acceleration factor that improves the exploration and exploitation phases. We also develop a Kuhn-Munkres (KM)-based algorithm as an REA benchmark which needs no for post-processing requirements. Finally, our proposed algorithms are compared with the state-of-the-art works in the literature. Results show that SCMA CWOA-BWOA algorithm (CBWOA) provides more robust optimality in massive connectivity situations with a larger number of users. Besides, the CBWOA method guarantees the structural constraints by increasing the number of search agents (NSAs) through the exploitation phase.

关键词： Resource management Uplink Interference whale optimization algorithms Vectors Clustering algorithms Training Measurement Industrial Internet of Things Wireless networks SCMA whale optimization algorithm non-convex MINLP resource allocation metaheuristic

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A novel product shape design method integrating Kansei engineering and whale optimization algorithm

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ADVANCED ENGINEERING INFORMATICS 2024年 62卷

作者： Zhao, Xiang Sharudin, Sharul Azim Lv, Han-Lu City Univ Malaysia Kuala Lumpur 46100 Malaysia West Univ Appl Sci Tengchong 679100 Peoples R China Daejin Univ Pochon 11159 South Korea

The focus of consumer desire transitions from product functionality to emotional resonance in experience economy era, wherein emotional needs of users increasingly become a critical factor in product design. However, traditional approaches to product shape design often rely heavily on the designer's intuition and experience, sometimes neglecting to incorporate emotional and humanistic elements into the product's shape, thus resulting in inconsistencies in design results and quality. To address this challenge, this study introduces a novel method for emotionally driven product shape design that integrates Kansei engineering and the whale optimization algorithm (WOA). This approach aims to fulfill consumer emotional demands related to product form and enhance overall user satisfaction. Firstly, the process utilized Python web crawlers to collect online product review texts and product images from e-commerce platforms. Next, Latent Dirichlet Allocation (LDA) and Analytic Hierarchy Process (AHP) were employed to extract representative emotional vocabularies, while representative samples were defined and deconstructed through clustering and morphological analysis. Then, semantic Differential (SD) questionnaires were distributed to collect consumer evaluations of product shape imagery, leading to the development of a user emotional prediction model for product shape. Then, WOA was introduced to optimize the performance of Back Propagation Neural Network (BPNN) and Support Vector Regression (SVR) models. Finally, Particle Swarm optimization (PSO) and Seagull optimization algorithm (SOA) were employed to improve the prediction model, and the effect of these models was compared by the error method. This analysis explored the accuracy of these non-linear models in order to identify the optimal model for application in product form design cases. The scientific validity and effectiveness of this method were demonstrated utilizing whiskey bottle shape design as a case study. The

关键词： Product Shape Design Kansei Engineering Latent Dirichlet Allocation whale optimization algorithm Whiskey Bottle Shape

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A hybrid style transfer with whale optimization algorithm model for textual adversarial attack

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NEURAL COMPUTING & APPLICATIONS 2024年第8期36卷 4263-4280页

作者： Kang, Yan Zhao, Jianjun Yang, Xuekun Fan, Baochen Xie, Wentao Yunnan Univ Natl Pilot Sch Software Key Lab Software Engn Yunnan Prov Kunming 650500 Yunnan Peoples R China Broadvis Engn Consultants Co Ltd Natl Engn Lab Surface Transportat Weather Impacts Kunming Peoples R China

Deep learning has been widely used in various research fields. However, researchers have discovered that deep learning models are vulnerable to adversarial attacks. Existing word-level attacks can be seen as a combinatorial optimization problem to effectively conduct textual adversarial attacks, but inappropriate search spaces and search methods may affect attack effectiveness. Sentence-level attacks are successfully used in the field of reading comprehension, but the generated examples sometimes lead to semantic deviation. To address these issues, we propose a hybrid textual adversarial attack method that effectively enhances the performance of textual adversarial attacks. To the best of our knowledge, we are the first to conduct textual adversarial attacks by hybridizing whale optimization algorithm (WOA) with style transfer from multiple sentence and word levels. The WOA is improved by incorporating data characteristics and the Metropolis criterion to escape from local optima and by leveraging the mutation operator to increase population diversity. The improved WOA and style transfer algorithm are fused in a parallel and vertical way. Style transfer can increase population diversity and expand the search space, usually without destroying the semantics and syntax of sentences. The parallel combination improves attack performance by attacking from both word-level and sentence-level perspectives. As a black-box attack model, our method can attack without knowing the internal structure of the model. Compared with the state-of-the-art method, our framework can improve the attack success rate by 6.8%. Additionally, further experiments on grammatical error increase rates, semantic consistency, and transferability demonstrate that our model has excellent performance in many respects.

关键词： Natural language processing Textual adversarial attacks Black box attacks Artificial intelligence security whale optimization algorithm Text style transfer

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Method using singular value decomposition and whale optimization algorithm to quantitatively detect multiple damages in turbine blades

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2024年第2期23卷 1025-1036页

作者： Jiang, Hu Jiang, Yongying Xiang, Jiawei Wenzhou Univ Coll Mech & Elect Engn Wenzhou Zhejiang Peoples R China Wenzhou Univ Coll Mech & Elect Engn Wenzhou 325035 Zhejiang Peoples R China

Renewable energy has increased in recent years with a consequential increase in equipment maintenance. Maintenance costs can be reduced by structural health monitoring techniques especially for wind turbine (WT) blade damages. However, the majority are not suitable for on-line measurements and quantitative detections. A quantitative damage detection method is developed to identify multiple damages in a WT blade under in-service operation conditions. Firstly, singular value decomposition is applied to reveal singular information in the operating deflection shape (ODS), which can be treated as damage locations. Secondly, whale optimization algorithm is utilized for a damage severity decision about the natural frequency database between damage severities and natural frequencies, which are constructed by finite element method (FEM) simulations on the detected damage locations in the WT blade. The procedure is applied to FEM numerical simulations of a single WT blade with two and three damages. By adding a certain noise to the simulation dataset, the robustness of the present method is validated. Furthermore, the laser scanning vibrometer is employed to test the ODS as well as natural frequencies of WT blades to testify the performance of the multiple damage detection method. Results show that the present method is effective for the detection of multi-damage in WT blades with a certain noise robustness.

关键词： Wind turbine blade operating deflection shape singular value decomposition whale optimization algorithm multi-damage detection

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An Optimal Node Localization in WSN Based on Siege whale optimization algorithm

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2201-2237页

作者： Thi-Kien Dao Trong-The Nguyen Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Multimedia Communication Lab University of Information TechnologyHo Chi Minh CityVietnam

Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging andfundamental operations in various monitoring or tracking applications because the network deploys a large areaand allocates the acquired location information to unknown devices. The metaheuristic approach is one of themost advantageous ways to deal with this challenging issue and overcome the disadvantages of the traditionalmethods that often suffer from computational time problems and small network deployment scale. This studyproposes an enhanced whale optimization algorithm that is an advanced metaheuristic algorithm based on thesiege mechanism (SWOA) for node localization inWSN. The objective function is modeled while communicatingon localized nodes, considering variables like delay, path loss, energy, and received signal strength. The localizationapproach also assigns the discovered location data to unidentified devices with the modeled objective functionby applying the SWOA algorithm. The experimental analysis is carried out to demonstrate the efficiency of thedesigned localization scheme in terms of various metrics, e.g., localization errors rate, converges rate, and executedtime. Compared experimental-result shows that theSWOA offers the applicability of the developed model forWSNto perform the localization scheme with excellent quality. Significantly, the error and convergence values achievedby the SWOA are less location error, faster in convergence and executed time than the others compared to at least areduced 1.5% to 4.7% error rate, and quicker by at least 4%and 2% in convergence and executed time, respectivelyfor the experimental scenarios.

关键词： Node localization whale optimization algorithm wireless sensor networks siege whale optimization algorithm optimization

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Application of terahertz Time-Domain spectroscopy and chemometrics-based whale optimization algorithm in PDE5 inhibitor detection

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024年 310卷 123894页

作者： Zhang, Aolin Qin, Ge Wang, Jifen Li, Na Wu, Shihao Peoples Publ Secur Univ China Sch Invest Beijing 102600 Peoples R China Mat Evidence Authenticat & Res Ctr Dezhou Publ Sec Dezhou 253000 Shandong Peoples R China

Combating the illicit use of PDE5 inhibitor drugs is a focal point in forensic science research. In order to achieve rapid identification of such drugs, this study applies terahertz time-domain spectroscopy combined with chemometrics to establish a fast and accurate detection method for PDE5 inhibitors. The optimal detection method is determined by comparing the spectral performance of three optical parameters, namely absorption coefficient, refractive index, and dielectric constant. Linear discriminant models based on different spectral parameters, whale optimization algorithm optimized extreme learning machine models, and whale optimization algorithm optimized random forest models are established. The effectiveness and performance of principal component analysis and competitive adaptive reweighted sampling algorithm for spectral feature data selection are also investigated. The PDE5 inhibitor identification model based on the competitive adaptive reweighted samplingwhale optimization algorithm - random forest (CARS-WOA-RF) model achieves an accuracy of 98.61%, and the identification model for two concentrations of Sildenafil achieves 100% accuracy. The results demonstrate that terahertz time-domain spectroscopy combined with chemometrics can effectively detect various common types of PDE5 inhibitor drugs and different concentrations.

关键词： PDE5 inhibitors Drug adulteration Terahertz time-domain spectroscopy Optical parameters whale optimization algorithm

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whale optimization algorithm Based on Dynamic Inertia Weight Factor 5

Whale Optimization Algorithm Based on Dynamic Inertia Weight...

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5th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Jin, Yuhao Yang, Shipin Nanjing Tech University College of Electrical Engineering and Control Science Nanjing China

ISBN: (纸本)9798350309034

Aiming at the drawbacks of whale optimization algorithm (WOA) that the precision is poor, the convergence speed is slow, and it is easily trapped in local optimization when solving complex projects, this paper proposes an improved whale optimization algorithm (IWOA).This strategy incorporates dynamic inertia weight factor into the updated expression of the traditional whale position to guarantee a counterbalance between the early and late algorithm's ability to search for an optimal position, thus improving the search precision and rate of the WOA. In addition, for validating the effectiveness of our proposed algorithm in dealing with multidimensional and nonlinear optimization problems, six classical benchmark functions are selected as the test objective in this paper. The test functions contain unimodal and multimodal functions of fixed dimensions and are compared with genetic algorithm (GA), particle swarm algorithm (PSO) and standard whale optimization algorithm. The test results show that the improved algorithm converges faster and is more accurate than the original algorithm and several classical algorithms. And it has better results in the values of the two indicators, mean and standard deviation. © 2023 IEEE.

关键词： benchmark function component dynamic inertia weights whale optimization algorithm

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