检索结果-内蒙古大学图书馆

ARIME-DWA: Robot Path Planning Utilizing Advanced RIME Optimization and Dynamic Windows Approach

IEEE ACCESS 2024年 12卷 191480-191497页

作者： Hu, Likun Cai, Chengjie Guangxi Univ Coll Elect Engn Nanning 530004 Guangxi Peoples R China

To overcome the limitations of insufficient optimization ability and the propensity to get stuck in local optima in the standard RIME algorithm for global path planning, an advanced RIME algorithm integrating the Artemisinin Optimization (AO) algorithm is proposed (ARIME). Moreover, by integrating ARIME with the strengthened Dynamic Windows Approach (DWA), the ARIME-DWA algorithm is formed, which addresses the issue of the RIME algorithm's lack of capability for dynamic path planning. Firstly, during the population initialization stage, the good-point set strategy is employed to ensure a uniform overall distribution of the ARIME's initial population;in the soft rime search strategy of ARIME, by integrating the comprehensive elimination mechanism of the AO, the ARIME's global search capability is boosted;in the hard rime puncturing mechanism of ARIME, the local clearing strategy of the AO is incorporated to boost the capabilities to get away from local optima;finally, the dynamic reverse learning strategy is adopted to strengthen the diversity of population. Theoretical analysis has proven that the time complexity of the ARIME is the same as that of the RIME algorithm. To validate the performance of the ARIME, global path planning simulation experiments are conducted adopting the ARIME and six comparative algorithms. Experimental results demonstrate that ARIME outperforms other six algorithms with more robust path planning capabilities. Among them, the average path lengths solved by the ARIME are reduced by 7.7%, 24.5%, and 26.9%, respectively. Finally, local path planning experiments with the ARIME-DWA reveal its effectiveness in dynamic local path planning.

关键词： Heuristic algorithms Path planning Optimization Robots Standards Particle swarm optimization Prediction algorithms Search problems Inference algorithms artificial bee colony algorithm RIME algorithm artemisinin optimization algorithm dynamic windows approach mobile robot path planning

来源：评论

学校读者我要写书评

暂无评论

AI-Supported probabilistic steady-state and transient seepage analysis of an embankment dam with clay core: A case study on Büyükcekmece Dam

引用

ENGINEERING GEOLOGY 2025年 351卷

作者： Gunay, Arife Akbas, Sami Oguzhan Karadeniz Tech Univ Civil Engn Dept TR-61080 Ortahisar Trabzon Turkiye Gazi Univ Civil Engn Dept TR-06570 Ankara Turkiye

The integration of the finite element method with probabilistic approaches accounting for uncertainties has become a widely adopted strategy for accurately modelling seepage (Q) in embankment dams. However, this approach is time-intensive, prompting this study to conduct a probabilistic analysis encompassing both steadystate and transient seepage to demonstrate the efficiency of using artificial neural networks (ANN) for accurately estimating Q values, thereby significantly reducing the reliance on extensive finite element simulations. In this context, first, the variability in the permeability of the clay core of embankment dams was investigated. Permeability was modeled as a random variable using two different approaches: First, as a log-normally distributed random variable for the entire clay core;and second, using random field theory, as a spatially correlated log-normally distributed random variable in horizontal layers. The data obtained were analyzed with artificial neural networks (ANN) and artificial bee colony (ABC) to ensure the consistency of the analyses. In all steady-state analyses, the lowest discharge was obtained when spatial variability was considered. Finally, the trained ANN-ABC network was utilized to estimate Q values for 0.75 COV (ks), a scenario for which finite element analyses were not conducted. This approach demonstrated the efficacy of using ANN to obtain Q values efficiently, thereby eliminating the need for labour-intensive finite element analyses.

关键词： artificial bee colony algorithm artificial neural networks Embankment dams Monte Carlo simulation Permeability Random Field Theory Seepage analysis

来源：评论

学校读者我要写书评

暂无评论

Short-term power forecasting method for 5G photovoltaic base stations on non-sunny days based on SDN-integrated INGO-BP and RGAN

引用

IET RENEWABLE POWER GENERATION 2024年第6期18卷 1019-1039页

作者： Huang, Jinbao Guo, Wenhao Wei, Rui Yan, Ming Hu, Yongle Qin, Tuanfa Guangxi Univ Sch Comp Elect & Informat Nanning 530004 Guangxi Peoples R China Guangxi Univ Guangxi Key Lab Multimedia Commun Network Technol Nanning Guangxi Peoples R China Guangxi Univ Sch Elect Engn Nanning Guangxi Peoples R China Runjian Co Ltd Nanning Guangxi Peoples R China

In response to the suboptimal efficiency observed in the network configuration and administration of 5G photovoltaic base stations (PVBSs), as well as the inherent limitations in accurately forecasting photovoltaic power during inclement weather conditions, this research article introduces a concise and effective method for short-term power prediction of PVBSs when subjected to non-sunny weather conditions, leveraging the paradigm of software defined networking. This method ingeniously amalgamates the improved Northern Goshawk optimized back propagation neural network (INGO-BP) and recurrent generative adversarial networks (RGAN). The investigation commences by delineating a comprehensive PVBS system framework underpinned by SDN principles. Subsequently, it employs the Pearson correlation coefficient technique and principal component analysis for the judicious selection of pertinent features from the PVBS photovoltaic dataset, thereby ameliorating the issue of multicollinearity within the predictive model. To culminate the research endeavour, the INGO algorithm is enlisted to optimize the weightings and bias parameters of the BP neural network, while RGAN is harnessed for the acquisition of distinctive distribution patterns in photovoltaic data across diverse meteorological conditions. This augmentation enhances the model's capacity to discern the intricate long-term mappings between photovoltaic power and meteorological data. The empirical findings substantiate the elevated accuracy of the INGO-BP prediction model across sunny, cloudy, and rainy weather conditions, with a discernible enhancement in predictive accuracy for PVBS performance under non-sunny weather conditions facilitated by data augmentation via RGAN. This research presents a novel power prediction approach for 5G photovoltaic base stations in non-sunny weather based on software defined networking, integrating the improved Northern Goshawk optimized back propagation neural network and recurrent genera

关键词： artificial bee colony algorithm artificial intelligence energy management systems feature extraction forecasting theory neural net architecture renewable energy sources solar power

来源：评论

学校读者我要写书评

暂无评论

A New Optimized Hybridization Approach for in silico High Throughput Molecular Docking on FPGA Platform

引用

CURRENT COMPUTER-AIDED DRUG DESIGN 2024年第3期20卷 236-247页

作者： Jarrah, Amin Lababneh, Jawad Yarmouk Univ Hijjawi Fac Engn Technol Dept Comp Engn Irbid 21163 Jordan

Background: The development process of a new drug should be a subject of continuous evolution and rapid improvement as drugs are essential to treat a wide range of diseases of which many are life-threatening. The advances in technology resulted in a novel track in drug discovery and development known as in silico drug design. The molecular docking phase plays a vital role in in silico drug development process. In this phase, thousands of 3D conformations of both the ligand and receptor are generated and the best conformations that create the most stable drug-receptor complex are determined. The speed in finding accurate and high-quality complexes depends on the efficiency of the search function in the molecular docking ***: The objective of this research is to propose and implement a novel hybrid approach called hABCDE to replace the EMC searching part inside the BUDE docking algorithm. This helps in reaching the best solution in a much accelerated time and higher solution quality compared to using the ABC and DE algorithms ***: In this work, we have employed a new approach of hybridization between the artificial bee colony (ABC) algorithm and the Differential Evolution (DE) algorithm as an alternative searching part of the Bristol University Docking Engine (BUDE) in order to accelerate the search for higher quality solutions. Moreover, the proposed docking approach was implemented on Field Programmable Gate Array (FPGA) parallel platform using Vivado High-Level Synthesis Tool (HLST) in order to optimize and enhance the execution time and overall efficiency. The NDM-1 protein was used as a model receptor in our experiments to demonstrate the efficiency of our ***: The NDM-1 protein was used as a model receptor in our experiments to demonstrate the efficiency of our approach. The results showed that the execution time for the BUDE with the new proposed hybridization approach was improved by 9,236 ***: Our novel

关键词： Molecular docking vivado HLS FPGA parallel processing artificial bee colony algorithm differential evolution algorithm optimization BUDE

来源：评论

学校读者我要写书评

暂无评论

Modeling and Optimization of Two-Sided Disassembly Line Balancing Problem Considering Interactions Among Precedence Free Tasks

引用

IEEE ACCESS 2025年 13卷 66829-66842页

作者： Wang, Shuwei Liu, Jia Xu, Guoxun Shandong Univ Sci & Technol Coll Econ & Management Qingdao 266590 Peoples R China Qingdao Univ Technol Business Sch Qingdao 266520 Peoples R China Hainan Univ Coll Int Tourism & Publ Adm Haikou 570228 Peoples R China

In disassembly process, task time interactions between two tasks may occur when they do not have any precedence relationship, and this will increase the disassembly time and affect productivity. Therefore, this paper provides an attempt to study a multi-objective two-sided disassembly line balancing problem considering interactions among precedence free tasks, and establishes a mathematical model from efficiency and environment concerns. Due to NP-hard nature of the presented problem, a novel hybrid discrete artificial bee colony algorithm is proposed based on the problem knowledge of the problem to obtain satisfactory solutions within a short computational time. A new encoding scheme based on one-dimensional integer array is designed to represent feasible solutions. A random initialization method based on Logistic chaotic mapping is used to produce an initial population with diversity. The INSERT, SWAP and MUTATION operators developed according to the characteristics of the problem are employed to construct the set of neighborhoods for the VNS method to exploit new solutions. Instead of randomly exploring new solutions in the whole solution space, a MUTATION operator performed on the current best solution is designed to avoid inefficiency in the exploration progress. The effectiveness and superiority of the proposed algorithm are verified by a series of instances. The analysis results indicate that the proposed algorithm is suitable to solve the presented problem as well as its variants and shows superior performance. Moreover, different disassembly solutions are compared to illustrate the important of balancing the tasks among the disassembly line.

关键词： Workstations artificial bee colony algorithm Metaheuristics Hazards Recycling Productivity Mathematical models Indexes Search problems Reverse logistics Two-sided disassembly line disassembly line balancing problem variable neighborhood search artificial bee colony algorithm

来源：评论

学校读者我要写书评

暂无评论

AB-LSTM: Long Short Term Memory with artificial bee colony for Predicting Coal Mine Gas Concentration

AB-LSTM: Long Short Term Memory with Artificial Bee Colony f...

引用

Industrial Automation, Robotics and Control Engineering (IARCE), International Conference on

作者： Peng Wang Bin Liu Hongfei Zhao Zengrong Zheng Jun Wang Zhiqi Xu Lichun Shu Shengqiang Liu Yimin Sun Xiaobo Zhang Information Research Institute Ministry of Emergency Management Beijing China Shenmu Energy Administration Shenmu Shaanxi China Shenmu Information Industry Development Group Co. Ltd Shaanxi China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

The efficacy of gas concentration prediction is of utmost significance as it hinges on a well-defined theoretical foundation. Such a foundation is essential for bolstering preventive measures and facilitating the effective control of gas-related disasters within mining operations. The gas concentration prediction is carried out by integrating the improved artificial bee colony algorithm, abbreviated as AB, with the long short term memory network (LSTM). Through the application of AB optimized LSTM, the prediction accuracy of LSTM is remarkably enhanced. The AB-LSTM gas concentration prediction model was established by meticulously selecting temperature, dust, and wind speed as the principal factors that have a profound impact on gas concentration and combining them with comprehensive laboratory test data. The outcomes obtained from this model vividly demonstrate its robust generalization capabilities. It is highly proficient in predicting both the evolving trends and peak values of gas concentration with remarkable precision. This proficiency not only powerfully underscores the model's reliability but also highlights its immense potential as a valuable and indispensable tool. It offers crucial theoretical insights and technical support that are vital for the comprehensive prevention and control of gas disasters in coal mines, thereby safeguarding the safety and well-being of miners and the overall stability of mining operations.

关键词： Adaptation models Disasters Neural networks Predictive models artificial bee colony algorithm Prediction algorithms Data models Coal mining Long short term memory Optimization

来源：评论

学校读者我要写书评

暂无评论

Efficient Feature Selection for IoMT Security: Using artificial bee colony for Intrusion Detection

Efficient Feature Selection for IoMT Security: Using Artific...

引用

Emerging Research in Computational Science (ICERCS), International Conference on

作者： Divya K V. Nagaraju Department of Computer Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

Internet of Medical Things (IoMT) is refacing healthcare through patient monitoring, remote diagnosis and better informatics in clinical decision making. However, this dependence increases risks as IoMT networks become vulnerable targets of cyber-crimes that seek to compromise the patient data, and the functionality of devices. Therefore, there is need to have strong Intrusion Detection Systems (IDS) which should protect these networks. Although several IDS models have been proposed in the literature, a limitation of most IDS models when applied to high dimensional IoMT data is that they do not perform well in real-time because they are plagued by high false positive rates and long detection times. Towards this end, this paper presents a ABC-DBN model for feature selection employing the artificial bee colony algorithm and intrusion detection using a Deep Belief Network. A feature selection approach of the ABC algorithm, helps in reducing the dimensionality of the data set, thus minimizing noise and computational costs. The ABC-DBN outcomes reveal that the proposed model yields nearly optimal classification solution with the AUC 1.0, high precision and shorter convergence time and give confirmation of superior performance in comparison with conventional IDS algorithms. These results confirm that the model is well placed for real-time scenarios as it can provide good intrusion detection while requiring little computational effort. Hence, the proposed ABC-DBN model present a feasible solution to addressing the secured IoMT and the problem of handling high dimensions data and real-time detection. This approach holds much promise in enhancing the protection of data in IoMT settings and outperforms existing methods substantially.

关键词： Performance evaluation Accuracy Computational modeling Biological system modeling Intrusion detection Feature extraction artificial bee colony algorithm Real-time systems Computational efficiency Security

来源：评论

学校读者我要写书评

暂无评论

Cooperative Multi-Task Assignment of Unmanned Autonomous Helicopters Based on Hybrid Enhanced Learning ABC algorithm

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

引用

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 526-540页

作者： Han, Zengliang Chen, Mou Shao, Shuyi Zhu, Haojie Wu, Qingxian Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Peoples R China

Aiming at the problem of cooperative combat problem for multiple unmanned autonomous helicopters (UAHs) in complex battlefield environments, a hybrid enhanced artificial bee colony (ABC) algorithm-based cooperative multi-task assignment method is proposed in this article. Firstly, by analyzing the battlefield requirements of UAH clusters in multi-target scenarios, a complex constrained task assignment optimization model considering practical application scenarios is established. Furthermore, a hybrid enhanced ABC algorithm is proposed based on cognitive-psychological learning (CPL). The proposed CPL-ABC algorithm integrates swarm intelligence and human cognitive mechanisms to guide the evolutionary direction of the population by introducing individual expectation effects and personality differences. In addition, an introspection evolution mechanism based on individual variability is introduced to enhance the local optimal escape ability of the ABC algorithm. Through the reflection and learning of locally optimal individuals, the uncontrollable influence of direction caused by random evolution is reduced. Finally, the simulation and experiment results verify the effectiveness and feasibility of the proposed cooperative multi-task assignment method.

关键词： Task analysis Multitasking Intelligent vehicles artificial bee colony algorithm Optimization Heuristic algorithms Statistics UAH ABC multi-task assignment cognitive-psychological introspection evolution

来源：评论

学校读者我要写书评

暂无评论

Computation Offloading in Edge Computing for Internet of Vehicles via Game Theory

引用

Computers, Materials & Continua 2024年第10期81卷 1337-1361页

作者： Jianhua Liu Jincheng Wei Rongxin Luo Guilin Yuan Jiajia Liu Xiaoguang Tu Institute of Electronic and Electrical Engineering Civil Aviation Flight University of ChinaGuanghan618307China

With the rapid advancement of Internet of Vehicles(IoV)technology,the demands for real-time navigation,advanced driver-assistance systems(ADAS),vehicle-to-vehicle(V2V)and vehicle-to-infrastructure(V2I)communications,and multimedia entertainment systems have made in-vehicle applications increasingly computingintensive and *** applications require significant computing resources,which can overwhelm the limited computing capabilities of vehicle terminals despite advancements in computing hardware due to the complexity of tasks,energy consumption,and cost *** address this issue in IoV-based edge computing,particularly in scenarios where available computing resources in vehicles are scarce,a multi-master and multi-slave double-layer game model is proposed,which is based on task offloading and pricing *** establishment of Nash equilibrium of the game is proven,and a distributed artificial bee colonies algorithm is employed to achieve game *** proposed solution addresses these bottlenecks by leveraging a game-theoretic approach for task offloading and resource allocation in mobile edge computing(MEC)-enabled IoV *** results demonstrate that the proposed scheme outperforms existing solutions in terms of convergence speed and system ***,the total revenue achieved by our scheme surpasses other algorithms by at least 8.98%.

关键词： Edge computing internet of vehicles resource allocation game theory artificial bee colony algorithm

来源：评论

学校读者我要写书评

暂无评论

COVID-19 Vaccines distribution routes with bioinspired metaheuristic algorithms: Resoluteness

引用

IEEE LATIN AMERICA TRANSACTIONS 2024年第6期22卷 484-493页

作者： Estrada Moreno, Leonardo Daniel Valdovinos Rosas, Rosa Maria Hernandez, Lourdes Loza Alejo, Roberto Univ Autonoma Estado Mexico Toluca Mexico Inst Tecnol Toluca Metepec Mexico

The global emergency of COVID-19 caused by the SARS-CoV-2 virus at the end of 2019, was without a doubt a critical and historical point for society in general;for instance, the effective development of vaccines, as well as the efficient distribution of them;They were an unprecedented challenge to slow down the spread or mitigate its impact on societies around the world. This article specifies three bio-inspired metaheuristic algorithms (genetic algorithm, particle swarm optimization algorithm, and artificial bee colony algorithm) that were used in the context of the capacitated vehicle routing problem to generate vaccine distribution routes, specifically, COVID-19 vaccine for over 18 years old the first and the second doses applications in Mexico, particularly in the State of Mexico. The quality of the solutions obtained by these algorithms is compared, as a result of the performance of the particle swarm optimization (PSO) algorithm being superior in solution quality.

关键词： Vaccines Costs Metaheuristics COVID-19 Vehicle routing Transportation Supply chains artificial bee colony algorithm Bio-inspired Capacitated Vehicle Routing Problem Genetic algorithm Particle Swarm 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

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：