检索结果-内蒙古大学图书馆

Fault diagnosis based on support vector machines with parameter optimization by an ant colony algorithm

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2010年第C1期224卷 217-229页

作者： Zhang, X. L. Chen, X. F. He, Z. J. Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China

Since support vector machines (SVM) exhibit a good generalization performance in the small sample cases, these have a wide application in machinery fault diagnosis. However, a problem arises from setting optimal parameters for SVM so as to obtain optimal diagnosis result. This article presents a fault diagnosis method based on SVM with parameter optimization by ant colony algorithm to attain a desirable fault diagnosis result, which is performed on the locomotive roller bearings to validate its feasibility and efficiency. The experiment finds that the proposed algorithm of ant colony optimization with SVM (ACO-SVM) can help one to obtain a good fault diagnosis result, which confirms the advantage of the proposed ACO-SVM approach.

关键词： fault diagnosis support vector machines ant colony optimization algorithm parameter optimization

来源：评论

学校读者我要写书评

暂无评论

Mcaaco: a multi-objective strategy heuristic search algorithm for solving capacitated vehicle routing problems

引用

COMPLEX & INTELLIGENT SYSTEMS 2025年第5期11卷 1-21页

作者： Chen, Yanling Wei, Jingyi Luo, Tao Zhou, Jie Shihezi Univ Dept Informat Sci & Technol North 4th Rd Shihezi 832000 Xinjiang Peoples R China

Vehicle routing is a critical issue in the logistics and distribution industry. In practical applications, optimizing vehicle capacity allocation can significantly improve route optimization performance and service coverage. However, solving this problem remains challenging due to the complex constraints involved. Therefore, to address this real-world challenge, a novel intelligent optimization method, multi-objective capacity adjustment ant colony optimization algorithm (MCAACO), is proposed, which integrates advanced multi-objective optimization strategies, including capacity adjustment operators and crossover operators. Combined with pheromone updating and Pareto front-end optimization, the method effectively resolves the conflict between vehicle capacity constraints and multi-objective optimization. To further enhance the algorithm's performance, dynamic pheromone updating mechanisms and elite individual retention strategies are proposed. Additionally, an adaptive parameter adjustment strategy is designed to balance global search and local exploitation capabilities. Through a series of experiments, it is demonstrated that compared to multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm II (NSGA-II), and multi-objective sparrow search algorithm (MOSSA), the proposed MCAACO significantly reduces travel paths by an average of 3.05% and increases vehicle service coverage by an average of 3.2%, while satisfying vehicle capacity constraints. Experimental indicators demonstrate that the breakthrough algorithm significantly addresses the issues of high costs and low efficiency prevalent in the practical logistics distribution industry.

关键词： Capacitated vehicle routing planning (CVRP) ant colony optimization algorithm Crossover operator Capacity adjustment operator Multi-objective optimization

来源：评论

学校读者我要写书评

暂无评论

ant colony optimization Task Scheduling algorithm for SWIM Based on Load Balancing

引用

FUTURE INTERNET 2019年第4期11卷

作者： Li, Gang Wu, Zhijun Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Baicheng Normal Univ Sch Mech Engn Baicheng 137000 Peoples R China Civil Aviat Univ China Sch Elect Informat & Automat Tianjin 300300 Peoples R China

This paper focuses on the load imbalance problem in System Wide Information Management (SWIM) task scheduling. In order to meet the quality requirements of users for task completion, we studied large-scale network information system task scheduling methods. Combined with the traditional ant colony optimization (ACO) algorithm, using the hardware performance quality index and load standard deviation function of SWIM resource nodes to update the pheromone, a SWIM ant colony task scheduling algorithm based on load balancing (ACTS-LB) is presented in this paper. The experimental simulation results show that the ACTS-LB algorithm performance is better than the traditional min-min algorithm, ACO algorithm and particle swarm optimization (PSO) algorithm. It not only reduces the task execution time and improves the utilization of system resources, but also can maintain SWIM in a more load balanced state.

关键词： System Wide Information Management ant colony optimization algorithm hardware performance quality index load standard deviation function load balancing

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

Design and optimization of Vibration Sieve for Specific Sandy Soil and Its Screening Motion Parameters 17th

Design and Optimization of Vibration Sieve for Specific Sand...

引用

17th International Conference on Intelligent Robotics and Applications

作者： Liu, Zhenxing Fan, Chengzhou Li, Yanxia Li, Shiguo Bi, Yongdong Yang, Jinshuai Northwest Inst Nucl Technol Xian Peoples R China

ISBN: (纸本)9789819607709;9789819607716

As an efficient and continuous screening equipment, vibrating screens are widely used in the screening process of various raw materials. However, aim at the screening of specific sandy soil, traditional vibrating screens often suffer from issues such as low screening efficiency and unstable motion performance. Therefore, this paper designs a vibrating screen specifically for sandy soil, which mainly includes a screen body, an automatically replaceable screen mesh mechanism, and a screen mesh cleaning mechanism. The vibrating screen can automatically change the screen mesh during the screening process, greatly improving the screening efficiency, and adopts a screen mesh cleaning method combining extrusion and high-pressure air knives to reduce dust volatilization. Subsequently, a screening model of the vibrating screen is established, and a sandy soil particle model is constructed using the weighted roundness method based on the discrete element overlapping particle family ODEC method. The impact of various model parameters on screening efficiency is analyzed. Based on multi-parameter orthogonal experimental analysis, the key parameters of the screening motion are optimized using the ant colony algorithm based on four-dimensional path planning, thereby improving the screening efficiency and stability of the vibrating screen.

关键词： linear vibrating screen discrete element method multi-parameter orthogonal experiment ant colony optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A Novel Strategy of Initializing the Population Size for ant colony optimization algorithms in TSP 13

A Novel Strategy of Initializing the Population Size for Ant...

引用

13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Liu, Fanzhen Zhong, Jiaqi Liu, Chen Gao, Chao Li, Xianghua Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China Pangang Grp Xichang Steel & Vanadium Co Ltd Panzhihua Sichuan Peoples R China Potsdam Inst Climate Impact Res PIK D-14473 Potsdam Germany

ISBN: (纸本)9781538621653

The ant colony optimization (ACO) algorithm belonging to swarm intelligence methods has been used to solve quantities of optimization problems. Among those problem, the travelling salesman problem (TSP) is a very essential application of ACO algorithm, which displays the great ability of ACO algorithm to find short paths through graphs. However, the existing ant colony optimization algorithms still perform a low efficiency in solving TSP within a limited time. In order to overcome these shortcomings, a hypothesis about initializing the population size for ACO algorithms is put forward, based on the analysis of the relationship among the initial number of ant, the average optimal solution and the computational cost. Furthermore, some experiments are implemented in six datasets, and the results prove that the hypothesis is reasonable and reveal that the initial population size is relevant to the number of cities in a dataset. Based on the hypothesis, this paper proposes a novel strategy of initializing the number of ants for ACO algorithms in TSP, so that the relative high-quality optimal solutions can be obtained within a short time.

关键词： ant colony optimization algorithm TSP Initial population size

来源：评论

学校读者我要写书评

暂无评论

A unified framework for personalized learning pathway recommendation in e-learning contexts

引用

EDUCATION AND INFORMATION TECHNOLOGIES 2025年第6期30卷 7911-7948页

作者： Zheng, Yaqian Wang, Deliang Zhang, Junjie Li, Yanyan Xu, Yaping Zhao, Yaqi Zheng, Yafeng Beijing Normal Univ Sch Educ Technol Beijing Peoples R China Beijing Normal Univ Res Ctr Knowledge Engn Beijing Peoples R China Univ Hong Kong Fac Educ Hong Kong Peoples R China Peoples Publ Secur Univ China Sch Informat Cyber Secur Beijing Peoples R China Beijing Normal Univ Zhuhai Inst Adv Studies Humanities & Social Sci Zhuhai Peoples R China

Generating personalized learning pathways for e-learners is a critical issue in the field of e-learning as it plays a pivotal role in guiding learners towards the successful achievement of their learning objectives. The existing literature has proposed various methods from different perspectives to address this issue, including learner-based, knowledge-based, and hybrid recommendation approaches. Among these, hybrid recommendation approaches have shown significant potential in generating highly personalized and logically structured learning pathways by combining the advantages of both learner-based and knowledge-based recommendation methods. However, there is a lack of a unified learning pathway recommendation framework that comprehensively incorporates essential parameters related to learners, learning objects, and domain knowledge. To overcome these challenges, we propose a unified framework to address the personalized learning pathway recommendation problem. In this framework, we develop a novel two-hierarchy modeling architecture that comprehensively formulates the requirements of the problem. Additionally, we present a Modified ant colony optimization algorithm to effectively discover the optimal learning pathways tailored to meet the diverse requirements and preferences of e-learners. To evaluate the effectiveness of the proposed method, we carry out extensive computational experiments on 12 simulation datasets of varying sizes and complexity levels. The computational results demonstrate that our proposed method outperforms other competing methods in terms of optimization performance and stability. Furthermore, we conduct an empirical study to verify the effectiveness of our method in a real-world learning scenario. The results obtained from this study show that our method effectively generates high-quality personalized learning pathways, thereby enhancing the learning experiences and outcomes of e-learners.

关键词： E-learning Learning pathway recommendation Unified framework Two-hierarchy modeling ant colony optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Feature Extraction of Stored-grain Insects Based on ant colony optimization and Support Vector Machine algorithm

引用

Agricultural Science & Technology 2012年第2期13卷 457-459页

作者：胡玉霞张红涛罗康张恒源郑州大学电气工程学院河南郑州450001 华北水利水电学院电力学院河南郑州450011

[Objective] The aim was to study the feature extraction of stored-grain insects based on ant colony optimization and support vector machine algorithm, and to explore the feasibility of the feature extraction of stored-grain insects. [Method] Through the analysis of feature extraction in the image recognition of the stored-grain insects, the recognition accuracy of the cross-validation training model in support vector machine （SVM） algorithm was taken as an important factor of the evaluation principle of feature extraction of stored-grain insects. The ant colony optimization （ACO） algorithm was applied to the automatic feature extraction of stored-grain insects. [Result] The algorithm extracted the optimal feature subspace of seven features from the 17 morphological features, including area and perimeter. The ninety image samples of the stored-grain insects were automatically recognized by the optimized SVM classifier, and the recognition accuracy was over 95%. [Conclusion] The experiment shows that the application of ant colony optimization to the feature extraction of grain insects is practical and feasible.

关键词： Stored-grain insects ant colony optimization algorithm Support vector machine Feature extraction Recognition

来源：评论

学校读者我要写书评

暂无评论

Modeling and performance evaluation of wind turbine based on ant colony optimization-extreme learning machine

引用

APPLIED SOFT COMPUTING 2020年 94卷 106476-106476页

作者： Wen, Xiaoqiang Northeast Elect Power Univ Dept Automat Jilin Jilin Peoples R China Jilin Prov Int Res Ctr Precis Drive & Intelligent Jilin Jilin Peoples R China

In this paper, an innovative hybrid multi-variable generator's actual-output-power predicting model is proposed based on ant colony optimization algorithm and extreme learning machine network, and a data-driven performance evaluation model is presented based on the two indices, K-means clustering algorithm and Markov chain for the performance evaluation of the wind turbines. ant colony optimization algorithm is used to optimize the initial weights and thresholds of the extreme learning machine network, then the optimized combinations of weights and thresholds are provided into the extreme learning machine models to overcome the sensitivity problem of initialization setting and the disadvantage of easily falling into local optimum. Through the actual-output-power prediction of the WTs in a wind farm, the results show that the proposed model has more higher prediction accuracy than other methods mentioned in this paper. The optimization process also shows that the prediction accuracy is sensitive to the number of hidden-layer nodes and is relatively insensitive to other model parameters. Then, the data-driven performance evaluation models are proposed based on the error sequences obtained above. The case study is conducted and the results show that the method can evaluate the operating performance of the wind turbines correctly. The effectiveness of the evaluation results is also verified by the actual operation results. (C) 2020 Elsevier B.V. All rights reserved.

关键词： ant colony optimization algorithm Extreme learning machine network Generator's actual output power Performance evaluation Wind turbine

来源：评论

学校读者我要写书评

暂无评论

A tri-objective ant colony optimization based model for planning safe construction site layout

引用

AUTOMATION IN CONSTRUCTION 2018年 89卷 1-12页

作者： Ning, Xin Qi, Jingyan Wu, Chunlin Wang, Wenjuan Dongbei Univ Finance & Econ Sch Investment & Construct Management Dalian Liaoning Peoples R China Beihang Univ Sch Econ & Management Beijing Peoples R China Beihang Univ Beijing Key Lab Emergency Support Simulat Technol Beijing Peoples R China Dongbei Univ Finance & Econ Sch Managanent Sci & Engn Dalian Liaoning Peoples R China

Realizing safety improvements in construction site layout planning (CSLP) is vitally important to construction project safety management. Unlike previous studies in which the safety objective is built without detailed risk factors analysis, this study transforms CSLP into a multi-objective optimization (MOO) problem with designing two safety objective functions due to facility safety relationships (potential risks arising from interaction flows) and geographic safety relationship (potential risks arising from hazardous sources) from the holistic interpretation of interaction relationship connecting temporary facilities. Besides, a supplementary cost reduction objective function was also derived as cost is a critical barrier against safety improvement. Subsequently, a tri-objective ant colony optimization based model was developed to solve MOO problem. Finally, a case study is used to verify the proposed model. The study enriches safety implications by considering onsite safety issues from interaction relationship and enhances site safety of CSLP in the pre-construction stage.

关键词： Construction site layout planning ant colony optimization algorithm optimization model Interaction relationship Safety management

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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：