检索结果-内蒙古大学图书馆

作者： Yao, Pengfei Northeastern University

学位级别：Ph.D., Doctor of Philosophy

The continuous development in manufacturing productivity has pushed product diversification to a very high level. Meanwhile, economic and social development have enticed people into purchasing more and more products. On one hand, continuously updated products (especially electronic and electrical products) delight and fulfill the demands of consumers but, on the other hand, the older models of products are retired early thus shortening their lifespans although they are still functional. The number of end-of-life (EOL) products is therefore increasing day by day putting greater and greater pressure on the waste *** waste problem is a worldwide challenge and governments and researchers are paying more and more attention to it. This has also added to a serious depletion of resources making the problem even worse. In particular, landfilling EOL products are common in many countries, but this method causes long term land pollution. Product recovery is one way to deal with EOL products. Product recovery may involve such things as reusing or remanufacturing EOL products or recycling valuable materials, thus directly decreasing land pollution. For most of the product recovery options, disassembly is often the first ***, apart from being the first step of product recovery, is also one of the most essential and crucial steps. Disassembly aims to physically separate EOL products into different parts or subassemblies. Large scale disassembly is performed on a disassembly line using linked paced workstations. Each workstation operates different sets of disassembly tasks. There are four types of disassembly lines, viz., straight-line, parallel, U-shaped, and two-sided. In a U-shaped disassembly line, operators and/or intelligent robots get access to work on both sides of the workstations, which has been proven to improve disassembly efficiency. For research and real-world disassemblycases, a single profit-oriented objective is not enough. In this dissertation

关键词： Disassembly line balancing meta-heuristic algorithm Remanufacturing Sequence-dependent relationship Supply chain management U-shaped layout

来源：评论

学校读者我要写书评

暂无评论

Domain Knowledge Used in meta-heuristic algorithms for the Job-Shop Scheduling Problem:Review and Analysis

引用

Tsinghua Science and Technology 2024年第5期29卷 1368-1389页

作者： Lin Gui Xinyu Li Qingfu Zhang Liang Gao State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Department of Computer Science City University of Hong KongHong KongChina

meta-heuristic algorithms search the problem solution space to obtain a satisfactory solution within a reasonable *** combining domain knowledge of the specific optimization problem,the search efficiency and quality of meta-heuristic algorithms can be significantly improved,making it crucial to identify and summarize domain knowledge within the *** this paper,we summarize and analyze domain knowledge that can be applied to meta-heuristic algorithms in the job-shop scheduling problem(JSP).Firstly,this paper delves into the importance of domain knowledge in optimization algorithm *** that,the development of different methods for the JSP are reviewed,and the domain knowledge in it for meta-heuristic algorithms is summarized and *** of this domain knowledge are analyzed,showing it is indispensable in ensuring the optimization performance of meta-heuristic ***,this paper analyzes the relationship among domain knowledge,optimization problems,and optimization algorithms,and points out the shortcomings of the existing research and puts forward research *** paper comprehensively summarizes the domain knowledge in the JSP,and discusses the relationship between the optimization problems,optimization algorithms and domain knowledge,which provides a research direction for the metaheuristic algorithm design for solving the JSP in the future.

关键词： domain knowledge job-shop scheduling problem meta-heuristic algorithm

来源：评论

学校读者我要写书评

暂无评论

meta-heuristic bus transportation algorithm

引用

Iran Journal of Computer Science 2019年第1期2卷 23-32页

作者： Bodaghi, Mohammad Samieefar, Koosha Amirkabir University of Technology Tehran Iran

Over recent decades, several experience-based mathematical models have been proposed. In addition to collective intelligence, in recent years some efforts have been made to apply human experience-based intelligence to open up a new world of possibilities to design new meta-heuristic algorithms for solving NP problems. In these algorithms instead of only relying on collective intelligence, each individual tries to search for optimal solution based on her or his own experience and others. In this paper, we use a social behavior of humans in transportations to reach their destination and we took experience-based human behavior and smartness of humans as an inspiration to propose our meta-heuristic algorithm and we name it bus transportation algorithm. As a simple example, we restrict our paper to solve a well-known problem integer programming which is known as an NP-Complete problem. Compared to other algorithms, the results in these papers show that our algorithm outperforms PSO (particle swarm optimization), GA (genetic algorithm) and SA (simulated annealing) in terms of efficiency and convergence. © Springer Nature Switzerland AG 2018.

关键词： Bus transportation algorithm Empiricism Human intelligence Intelligent algorithms meta-heuristic algorithm

来源：评论

学校读者我要写书评

暂无评论

EO-LGBM-HAR: A novel meta-heuristic hybrid model for human activity recognition

引用

Computers in Biology and Medicine 2025年 189卷 110004-110004页

作者： Topuz, Elif Kevser Kaya, Yasin Department of Computer Engineering Adana Alparslan Türkeş Science and Technology University Adana Turkey Department of Artificial Intelligence Engineering Adana Alparslan Türkeş Science and Technology University Adana Turkey

Sensor-based Human Activity Recognition (HAR) is widely utilized in various fields, including smart health, security, and smart home applications, to identify a person's physical activities automatically. One of the main challenges in sensor-based HAR is managing the numerous features collected and extracted from sensor data. As the number of features increases, the complexity of the models also rises, leading to greater demands for computing power and resources—especially in devices with limited power and memory. To tackle this challenge, this study presents a novel hybrid approach called EO-LGBM-HAR, which leverages a metaheuristic-based method to optimize features and enhance the training and evaluation processes of the model, making it more effective and robust. The proposed model combines an Equilibrium Optimizer (EO) as the optimizer with a LightGBM (LGBM) classifier. It has been rigorously tested and compared against the UCI-HAR and WISDM datasets to assess its effectiveness. The results are highly promising, with the proposed model achieving an impressive accuracy of 98.72% on the UCI-HAR dataset and 95.39% on the WISDM dataset. © 2025 Elsevier Ltd

关键词： Human activity recognition Equilibrium optimization meta-heuristic algorithm Feature selection

来源：评论

学校读者我要写书评

暂无评论

Support Vector Regression-Based Performance Prediction for Unmanned Aerial Vehicles

引用

INTERNATIONAL JOURNAL OF AERONAUTICAL AND SPACE SCIENCES 2025年 1-17页

作者： Xu, Han Bi, Wenhao Xu, Shuangfei Huang, Zhanjun Zhang, An Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China Natl Key Lab Aircraft Configurat Design Xian 710072 Peoples R China

The increasing incidence of fires has resulted in a growing number of casualties and economic losses. On the one hand, traditional firefighting approaches and equipment are inefficient and expose firefighters to great danger. On the other hand, in recent years, unmanned aerial vehicles (UAVs) are gradually being applied in firefighting fields such as fire detection and extinguishing due to their safety, flexibility, and low cost. The performance of the firefighting UAV has a significant impact on its efficiency in performing their tasks. To calculate the performance quantitatively, support vector regression (SVR) is employed to establish the performance prediction model. However, it is difficult to set SVR parameters efficiently because of the high dimensionality, nonlinearity, and complexity in this problem, which restricts the prediction effect. Therefore, the mutant shuffled frog leaping algorithm (MSFLA) based SVR method is proposed to optimize SVR parameters and predict the performance of the UAV. First, the step size equation is modified in MSFLA, which expands the search range in the iterations and can help to find better solutions. Meanwhile, considering that traditional optimization algorithms are prone to falling into local optimums, the Cauchy mutation is introduced into the position update process of the proposed MSFLA to overcome this shortcoming. The mutation strategy improves the global optimization capabilities. Finally, the performance prediction based on the proposed methods are simulated. The simulations focus on comparing the prediction errors of the different methods on the testing set. The average predicted errors and four commonly used assessment indicators are calculated, and the corresponding results suggest that the prediction effectiveness of MSFLA-SVR is much better than the other methods. Moreover, the results of statistical test further demonstrated the superiority of the proposed approaches.

关键词： Firefighting UAV meta-heuristic algorithm Performance prediction Shuffled frog leaping algorithm (SFLA) Support vector regression (SVR)

来源：评论

学校读者我要写书评

暂无评论

Hybrid meta-heuristic algorithms for U-shaped assembly line balancing problem with equipment and worker allocations

引用

SOFT COMPUTING 2022年第5期26卷 2241-2258页

作者： Khorram, Morteza Eghtesadifard, Mahmood Niroomand, Sadegh Shiraz Univ Technol Dept Ind Engn Shiraz Iran Firouzabad Inst Higher Educ Dept Ind Engn Firouzabad Fars Iran

In this paper, a new U-shaped assembly line balancing problem is studied. For the first time, the criteria such as equipment cost, number of stations and activity performing quality level are considered to be optimized simultaneously by activity to station and worker to station decisions. For this aim, a multi-objective nonlinear formulation is proposed and its linearized version is also presented. Since, according to the literature, the U-shaped assembly line balancing problem with equipment requirements is an NP-hard problem, the problem of this study is NP-hard too. Because of this complexity, the classical algorithms like simulated annealing, variable neighborhood search, and classical genetic algorithm with a novel encoding/decoding scheme are used as solution approaches. As an extension, two hybrid versions of the proposed classical algorithms are proposed according to the characteristics of the problem. In order to evaluate the proposed meta-heuristics, because the problem is new, some test problems are generated randomly. Computational study of the paper, including sensitivity analysis of the proposed meta-heuristics and final experiments on the test problems, proves the superiority of the hybrid versions of the classical algorithms.

关键词： U-shaped assembly line balancing meta-heuristic algorithm Worker assignment NP-hard problem Equipment assignment

来源：评论

学校读者我要写书评

暂无评论

Phasmatodea Population Evolution algorithm Based on Spiral Mechanism and Its Application to Data Clustering

引用

Computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-Shyang Pan Mengfei Zhang Shu-Chuan Chu Xingsi Xue Václav Snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution algorithm(SPPE)to improve clustering *** SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)***,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

来源：评论

学校读者我要写书评

暂无评论

Advancing Reservoir Water Level Predictions: Evaluating Conventional, Ensemble and Integrated Swarm Machine Learning Approaches

引用

WATER RESOURCES MANAGEMENT 2025年第2期39卷 779-794页

作者： Rehamnia, Issam Mahdavi-Meymand, Amin Badji Mokhtar Annaba Univ Fac Technol Dept Hydraul POB 12 Annaba 23000 Algeria Polish Acad Sci Inst Hydroengn Gdansk Poland

Accurate estimation of reservoir water level fluctuation (WLF) is crucial for effective dam operation and environmental management. In this study, seven machine learning (ML) models, including conventional, integrated swarm, and ensemble learning methods, were employed to estimate daily reservoir WLF. The models comprise multi-linear regression (MLR), shallow neural network (SNN), deep neural network (DNN), support vector regression (SVR) integrated with homonuclear molecules optimization (HMO) and particle swarm optimization (PSO) meta-heuristic algorithms, classification and regression tree (CART), and random forest (RF). These models were trained and evaluated using in situ data from three embankment dams in Algeria: the Kramis dam, the Bougous dam, and the Fontaine Gazelles dam. Performance evaluation was conducted using statistical indices, scatter plots, violin plots, and Taylor diagrams. The results revealed superior prediction accuracy for the Fontaine Gazelles dam compared to Kramis and Bougous dams. Particularly, the RF, DNN, and SVR-HMO models exhibited consistent and excellent predictive performance for WLF at the Fontaine Gazelles dam with RMSE values of 0.502 m, 0.536 m, and 0.57 m, respectively. The RF model demonstrates remarkable accuracy across all three case studies. This can be attributed to the ensemble structure of RF, as evidenced by the results. This study underscores the significance of considering factors such as seepage flow intensity in understanding WLF variability. Furthermore, the proposed ML models offer promising capabilities in WLF prediction, highlighting their potential utility in enhancing reservoir management practices and addressing the limitations of traditional regression models. Keys words. Embankment dam, Water level fluctuations, Seepage, Artificial neural network, meta-heuristic algorithm.

关键词： Embankment dam Water level fluctuations Seepage Srtificial neural network meta-heuristic algorithm

来源：评论

学校读者我要写书评

暂无评论

Solving a new cost-oriented assembly line balancing problem by classical and hybrid meta-heuristic algorithms

引用

NEURAL COMPUTING & APPLICATIONS 2020年第12期32卷 8217-8243页

作者： Salehi, Maryam Maleki, Hamid Reza Niroomand, Sadegh Shiraz Univ Technol Fac Math Shiraz Iran Firouzabad Inst Higher Educ Dept Ind Engn Firuzabad Fars Iran

In this study, a new cost-oriented assembly line balancing problem is proposed and formulated. A single objective function consisting of minimizing the cost associated with equipment, labor wage, and station establishment is considered for the problem. This problem is more complicated comparing to the literature as worker qualification is considered for determining his/her wage. As this problem is of NP-hard optimization problems, some meta-heuristic solution approaches, e.g., simulated annealing, variable neighborhood search, genetic algorithm, tabu search, population-based simulated annealing, and their hybrid versions are proposed. In the proposed algorithms, a novel encoding-decoding scheme is applied. This scheme uses the Hungarian method to assign the workers to the station to reduce the total wage of the workers. To study the performance of the proposed meta-heuristic algorithms, ten test problems are generated randomly, and using one of them the parameters of the algorithms are tuned by the Taguchi method. The final experiments on the proposed algorithms and the test problems show that in the most of the experiments, among the proposed algorithms, the single-solution-based algorithms, except TS, perform better than the population-based algorithms, especially for the case of large size test problems.

关键词： Assembly line balancing problem meta-heuristic algorithm Taguchi method Hungarian method

来源：评论

学校读者我要写书评

暂无评论

A systematic mapping study on solving university timetabling problems using meta-heuristic algorithms

引用

NEURAL COMPUTING & APPLICATIONS 2020年第23期32卷 17397-17432页

作者： Bashab, Abeer Ibrahim, Ashraf Osman AbedElgabar, Eltayeb E. Ismail, Mohd Arfian Elsafi, Abubakar Ahmed, Ali Abraham, Ajith Alzaiem Alazhari Univ Fac Comp Sci & Informat Technol Khartoum 13311 Sudan Arab Open Univ Khartoum Sudan Al Neelain Univ Fac Comp Sci & Informat Technol Khartoum Sudan Univ Malaysia Pahang Fac Comp Syst & Software Engn Pekan Pahang Malaysia Univ Jeddah Coll Comp Sci & Engn Software Engn Dept POB 34 Jeddah 21959 Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Dept Comp Sci Rabigh 25732 Saudi Arabia Sci Network Innovat & Res Excellence Machine Intelligence Res Labs MIR Labs Auburn WA 98071 USA

Since university timetabling is commonly classified as a combinatorial optimisation problem, researchers tend to use optimisation approaches to reach the optimal timetable solution. meta-heuristic algorithms have been presented as effective solutions as proven on their leverage over the last decade. Extensive literature studies have been published until today. However, a comprehensive systematic overview is missing. Therefore, this mapping study aimed to provide an organised view of the current state of the field and comprehensive awareness of the meta-heuristic approaches, by conducting meta-heuristic for solving university timetabling problems. In addition, the mapping study tried to highlight the intensity of publications over the last years, spotting the current trends and directions in the field of solving university timetabling problems, as well as having the work to provide guidance for future research by indicating the gaps and open questions to be fulfilled. Primary studies on mapping study that have been published in the last decade from 2009 until the first quarter of 2020, which consist of 131 publications, were selected as a benchmark for future research to solve university timetabling problems using meta-heuristic algorithms. The majority of the articles based on the publication type are hybrid methods (32%), in which the distribution of meta-heuristic algorithms the hybrid algorithms represent the higher application (31%). Likewise, the majority of the research is solution proposals (66%). The result of this study confirmed the efficiency and intensive application of the meta-heuristic algorithms in solving university timetabling problems, specifically the hybrid algorithms. A new trend of meta-heuristic algorithms such as grey wolf optimiser, cat swarm optimisation algorithm, Elitist self-adaptive step-size search and others with high expectations for reliable and satisfying results can be proposed to fill this gap.

关键词： Systematic mapping review University timetabling meta-heuristic algorithm Optimisation

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：