检索结果-内蒙古大学图书馆

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

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Multi-spectral evaluation of total nitrogen, phosphorus and potassium content in soil using Vis-NIR spectroscopy based on a modified support vector machine with whale optimization algorithm

引用

SOIL & TILLAGE RESEARCH 2025年 252卷

作者： Liu, Mochen Yang, Kuankuan Yan, Yinfa Song, Zhanhua Tian, Fuyang Li, Fade Yu, Zhenwei Zhang, Rongyao Yang, Qinglu Lu, Yao Shandong Agr Univ Coll Mech & Elect Engn Tai An 271018 Peoples R China Shandong Key Lab Hort Machineries & Equipment Tai An 271018 Peoples R China Shandong Engn Lab Agr Equipment Intelligence Tai An 271018 Peoples R China Weichai Lovol Intelligent Agr Technol Co Ltd Weifang 261206 Peoples R China

Accurate and non-destructive detection of total nitrogen (TN), total phosphorus (TP), and total potassium (TK) levels in soil is crucial for precise soil testing and fertilization in modernized precision agriculture. Traditional methods for soil composition analysis are expensive, time-consuming, and destructive. This research aims to establish a low-cost, high-precision, and non-destructive method for soil nutrient detection based on visible-near-infrared (Vis-NIR) spectroscopy (350-2500 nm) combined with improved machine learning algorithms. The VisNIR spectra of soil samples were acquired using the RS-5400 high-resolution ground feature spectrometer. Subsequently, the Monte Carlo sampling cross-validation (MCCV) algorithm was used to eliminate abnormal samples, and then different preprocessing methods were performed on the spectral data including first-derivative (FD), Savitzky-Golay smoothing (SG) and others. The optimal preprocessing method was selected from these options. In order to remove redundant information and increase the speed of calculation, five algorithms such as competitive adaptive reweighted sampling (CARS), iteratively retains informative variables (IRIV) and the variable iterative space shrinkage approach (VISSA)-IRIV algorithm were used to select feature variables. The characteristic wavelengths closely related to TN, TP, and TK in the soil have been extracted. Then, the RBF kernel (radial basis function) and poly kernel were mixed to obtain the RBF-poly hybrid kernel function, and then the hybrid kernel function support vector machine (RBF-poly-SVM) and the radial basis kernel function support vector machine (RBF-SVM) were applied respectively. Establish prediction models and introduce the whale optimization algorithm (WOA) to optimize the g (kernel function parameter), c (penalty factor) and k-rbf (weight coefficient) parameters in the two models. The performance of the developed models was tested using the coefficient of determination (R2),

关键词： Soil Vis-NIR Nitrogen whale optimization algorithm Support vector machine

来源：评论

学校读者我要写书评

暂无评论

Localization of a radioactive source with the predictive range-whale optimization algorithm method

引用

NUCLEAR ENGINEERING AND TECHNOLOGY 2025年第8期57卷

作者： Hong, Zongheng Cong, Feiyun Yang, Bo Chen, Zhangyu Niu, Yunlong Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China Shanghai Inst Special Equipment Supervis & Inspect Shanghai 200062 Peoples R China Zhejiang Ecol Environm Co Ltd Hangzhou 311121 Peoples R China Zhejiang Univ Taizhou Inst Hangzhou 318000 Peoples R China

Heuristic algorithms can effectively improve the accuracy and efficiency of estimating the status of radioactive sources based on the maximum likelihood estimation method. However, heuristic algorithms often converge prematurely at local optima, compromising stability and making them unsuitable for prolonged monitoring tasks. To address this problem, the Predictive Range-whale optimization algorithm (PR-WOA) method was proposed in this paper. Initially, the predictive location and intensity ranges of the radioactive source were determined by integrating historical prediction results. Subsequently, the initial population was more concentrated within the predictive range to improve the algorithm's capability for local optimization. Finally, an inertia weight was introduced to adaptively adjust the search step, consequently improving its global searching capability and efficiency. The performance of the PR-WOA method was evaluated with the simulation and experimental data. Comparative studies demonstrated that the proposed method significantly improves accuracy and stability in predicting the status of the radioactive source. In the 2022 radioactive source localization experiment in Hangzhou, PR-WOA method achieved an average localization accuracy of 1.30m during the trajectory tracking of a moving radioactive source mounted on the drone. Compared to traditional heuristic algorithms, this method improved localization accuracy by 17.9 % and enhanced localization stability during long-term monitoring by 19.0 %.

关键词： Radioactive source Localization Maximum likelihood estimation whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Fusion of wavelet packet-fuzzy contraction and whale optimization algorithm for hemoglobin spectral feature extraction

Fusion of wavelet packet-fuzzy contraction and whale optimiz...

引用

2024 Applied Optics and Photonics China

作者： Fang, Renjie Han, Xin Li, Xiangxian Tong, Jingjing Gao, Minguang Chinese Acad Sci Hefei Inst Phys Sci Hefei 230031 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China

ISBN: (数字)9781510687769

ISBN: (纸本)9781510687752;9781510687769

Near-infrared spectroscopy (NIRS) technology has a wide range of potential applications in hemoglobin detection, but its accuracy is susceptible to noise and background interference. In order to improve the accuracy of quantitative analysis of hemoglobin in blood NIRS spectra, this study introduces a hybrid method (WPT-FS-WOA), which integrates wavelet packet transform (WPT), fuzzy shrinkage (FS), and whale optimization algorithm (WOA) for hemoglobin feature band extraction. The method uses WPT to decompose the blood near-infrared spectrum at multiple scales, applies FS to denoise the wavelet packet node coefficients for data affiliation assessment, and combines WOA to optimize the wavelet packet nodes at different frequencies, and finally reconstructs the hemoglobin feature spectrum. The analysis of real blood data shows that this method can effectively capture the hemoglobin spectral features compared with the traditional preprocessing techniques.

关键词： Near-infrared spectroscopy hemoglobin wavelet packet transform fuzzy shrinkage whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

An Enhanced whale optimization algorithm for Task Scheduling in Cloud Computing

An Enhanced Whale Optimization Algorithm for Task Scheduling...

引用

International Conference on 6G Communications Networking and Signal Processing, SGCNSP 2023

作者： Srilatha, P. Ramadan, Ghazi Mohamad Kumar, T. M. Kiran Alekhya, Y. Pani, Alok Kumar Department of AI & DS Chaitanya Bharathi Institute of Technology Telangana Hyderabad India College of MLT Ahl Al Bayt University Karbala Iraq Department of CSE Siddaganga Institute of Technology Tumakuru India Maisammaguda Hyderabad India Bangalore India

ISBN: (纸本)9789819778751

Task Scheduling is the significant challenge in the environment of Cloud Computing (CC) and has attention in numerous researchers in recent years with respect to attain cost effective computation and improve resource utilization. The existing algorithms has limitations of role and selection criteria of inertia weight was not considered. In this research, Enhanced whale optimization algorithm (EWOA) is proposed for maximize effectiveness of task scheduling in CC. An inertia weight is implemented in WOA algorithm that enhances the convergence and accuracy of algorithm that helps in task scheduling effectiveness. The performance of proposed technique is estimated with performance measure of Makespan (ms), execution time (s) and resource utilization (%). The proposed method attained less execution time of 2304, 2537, 2765, 2983 and 3016 s for 200, 400, 600, 800 and 1000 number of tasks. The proposed method attained the superior results when compared with other existing algorithms like Ant Colony optimization (ACO), Grey Wolf optimization (GWO), Particle Swarm optimization (PSO) and whale optimization algorithm (WOA). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud computing Inertia weight Resource utilization Task scheduling whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Research on Interference Resource optimization Based on Improved whale optimization algorithm

引用

IEEE ACCESS 2025年 13卷 83136-83147页

作者： Chen, Xuyi Ma, Mingxi Liu, Chengkui Xie, Haifeng Wang, Shaoqi Air Force Command Coll Beijing 100080 Peoples R China Army 96716 Ganzhou 341001 Peoples R China

Interference resource optimization is a prerequisite for air defense suppression mission planning, and the degree of optimization of interference resources directly determines the quality of the interference results. In this problem, this paper establishes an interference resource optimization model with radar detection probability, interference effectiveness, and interference bandwidth utilization as the objective functions. Then, in the solution process, to address the issues of the whale optimization algorithm (WOA) easily falling into local optima and low convergence accuracy, the BIO-WOA (Bernoulli Chaotic mapping In-nonlinear Factors and Opposition-based Learning Improved whale optimization algorithm, BIO-WOA) is proposed. First, the population initialization is completed using Bernoulli chaotic mapping based on the whale optimization algorithm, increasing the diversity and uniformity of solutions and enhancing the algorithm's global search capability. Then, a nonlinear convergence factor is proposed to balance the local and global search capabilities of the algorithm. Subsequently, the centroid opposition-based learning is used to generate mutated whales, improving the algorithm's ability to escape local optima. Finally, the effectiveness of the algorithm is verified through test functions and simulation experiments.

关键词： Interference Radar Jamming whale optimization algorithms optimization Radar detection Heuristic algorithms Resource management Radar measurements Linear programming whale optimization algorithm chaotic mapping nonlinear convergence factor centroid opposition-based learning interference resource optimization

来源：评论

学校读者我要写书评

暂无评论

A novel WO-ANT: whale-ant optimization algorithm for detection of Parkinson’s disease

引用

International Journal of Information Technology (Singapore) 2025年第3期17卷 1873-1881页

作者： Singh, Neha Sinha, Sapna Singh, Laxman Amity Institute of Information Technology Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering (AI-ML) KIET Group of Institutions U.P Ghaziabad India

Parkinson’s disease (PD) is a neurological condition that impacts the quality of life for millions of people all over the world. A prompt and precise diagnosis of PD is absolutely necessary for the successful treatment and management of the condition. For the detection of PD, numerous methods exist in the literature, including speech-based methods, gait-based methods, and handwriting-based methods. Compared to the other two methods, the speech-based method is regarded as the most effective and competitive. The whale-Ant optimization algorithm (WO-ANT) is a novel swarm optimization strategy that is presented in this paper as a potential method for the detection of PD via feature selection. To help in early detection, the proposed method attempts to enhance classification accuracy of PD detection by selecting an optimum subset of features from a wide feature space. In this paper, authors evaluate the classification efficacy of WO-ANT for PD using a speech-based method. MATLAB is used to implement the proposed method on the PD dataset obtained from ***. The efficacy of the algorithm is evaluated based on the optimal subset of features, accuracy, and computational cost. WO-ANT accurately reduced the number of features from 23 to 4 giving an accuracy of 90.29%. According to the numerical results, the algorithm performed well in terms of accuracy and selection of the optimal number of features. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Ant colony optimization algorithm Feature selection Parkinson’s disease whale optimization algorithm whale-ant optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

whale optimization algorithm With Applications to Resource Allocation in Wireless Networks

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第4期69卷 4285-4297页

作者： Pham, Quoc-Viet Mirjalili, Seyedali Kumar, Neeraj Alazab, Mamoun Hwang, Won-Joo Pusan Natl Univ Inje Univ Res Inst Comp Informat & Commun Busan 46241 South Korea Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Qld 4006 Australia Thapar Univ Dept Comp Sci & Engn Patiala 147004 Punjab India Charles Darwin Univ Coll Engn IT & Environm Casuarina NT 0810 Australia Pusan Natl Univ Dept Biomed Convergence Engn Busan 46241 South Korea

Resource allocation plays a pivotal role in improving the performance of wireless and communication networks. However, the optimization of resource allocation is typically formulated as a mixed-integer non-linear programming (MINLP) problem, which is non-convex and NP-hard by nature. Usually, solving such a problem is challenging and requires specific methods due to the major shortcomings of the traditional approaches, such as exponential computation complexity of global optimization, no performance optimality guarantee of heuristic schemes, and large training time and generating a standard dataset of machine learning based approaches. whale optimization algorithm (WOA) has recently gained the attention of the research community as an efficient method to solve a variety of optimization problems. As an alternative to the existing methods, our main goal in this article is to study the applicability of WOA to solve resource allocation problems in wireless networks. First, we present the fundamental backgrounds and the binary version of the WOA as well as introducing a penalty method to handle optimization constraints. Then, we demonstrate three examples of WOA to resource allocation in wireless networks, including power allocation for energy-and-spectral efficiency tradeoff in wireless interference networks, power allocation for secure throughput maximization, and mobile edge computation offloading. Lastly, we present the adoption ofWOA to solve a variety of potential resource allocation problems in 5G wireless networks and beyond.

关键词： Wireless and communication networks non-orthogonal multiple access meta-heuristic optimization edge computing resource allocation whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：