检索结果-内蒙古大学图书馆

16th International Conference on Microwave and Millimeter Wave Technology (ICMMT)

作者： Gao, Jingpeng Zhao, Pengjie Zhang, Tianran Qian, Jianfei Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Beijing Inst Elect Syst Engn Beijing 100854 Peoples R China

ISBN: (纸本)9798350389968

The traditional swarm intelligence optimization algorithm is used to solve the UAV single station passive location with reduced positioning accuracy due to the continuous motion of the UAV. To address this problem, this paper proposes a UAV single station passive location algorithm based on Harris Trevally Fish optimization (HTFO). The algorithm combines historical and real-time phase difference information, and introduces the Harris factor and Host foraging ideology. It improves the algorithm's searching ability while avoiding the algorithm from falling into local optimum. The simulation results show that the proposed algorithm is better than the traditional algorithm in performance stability, and the positioning accuracy is improved by 6.13%, which has good value for engineering applications.

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Secure Clustering and Routing Based on Modified Whale optimization Algorithm in MANET

Secure Clustering and Routing Based on Modified Whale Optimi...

引用

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

作者： Ravi Kiran Varma, P. Kumar, Bura Vijay Hemalatha, K.L. Jabar, Muhammed Basim Manjula, B.M. Department of CSE Sagi Rama Krishnam Raju Engineering College Andhra Pradesh Bhimavaram India School of Computer Science and Artificial Intelligence SR University Warangal India Department of Information Science and Engineering Sri Krishna Institute of Technology Bangalore India National University of Science and Technology Dhi Qar Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9789819778751

Mobile Ad Hoc Networks (MANETs) are a self-organizing and adaptive technique comprising several mobile nodes. These networks’ dynamic and changeable architecture creates substantial issues in terms of trust and security for wireless communication lines. To solve these concerns, the Modified Whale optimization Algorithm (MWOA) is used to provide a secure clustering and routing solution in MANET. The Modified K-means (MKM) method is used in the Cluster Head (CH) selection process to provide effective transmission to the target node without packet loss. MWOA, an optimized technique, is introduced to improve MANET routing security. Key parameters such as energy consumption (EC), packet delivery ratio (PDR), throughput, network Lifetime (NL), and end-to-end delay (EED) are used to evaluate the suggested methodology. Comparative investigation shows that the MWOA methodology outperforms present methods in terms of performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Enhanced Electrical Characterization of Dusty Solar Modules: Integrating Outdoor Experiments With Single- and Double-Diode Models

引用

INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2025年第1期2025卷

作者： Younis, Abubaker Belabbes, Fatima Cotfas, Petru Adrian Cotfas, Daniel Tudor Transilvania Univ Brasov Elect & Comp Dept Brasov Romania Univ Djillali Liabes Sidi Bel Abbes Dept Elect Sidi Bel Abbes Algeria

This study addresses a research gap regarding the impact of dust accumulation on photovoltaic (PV) modules, with a specific focus on parameter extraction using single- and double-diode models (SDMs and DDMs) under dusty conditions. While dust effects on PV performance are well-studied, few have explored how existing models can accurately represent these effects. Experimental data from outdoor testing of small-scale modules subjected to artificially deposited dust were analyzed. The direct current parameters were then extracted using the SDM and DDM, with the application of the improved snake optimization algorithm to enhance the accuracy. Preliminary analysis shows that the fill factor of dusty panels gradually increases, surpassing that of clean panels, due to increased absorption of diffuse light from reflections off the nonuniform dust layer. Efficiency uniformly decreases under dust presence. Computational comparison reveals a significant impact of dust on the algorithm's prediction quality, with maximum root mean square error decreases of 339.1% and 303.5% for DDM and SDM, respectively. The study observes that DDM effectively represents dust effects with fewer parameters than SDM, which includes more parameters conveying dust deposition effects. On average, DDM photocurrent values decrease by 24.2% due to dust, while shunt resistance decreases by 79.7%. For SDM, photocurrent decreases by 24.2%, shunt resistance by 80.1%, diode saturation current by 84.6%, and ideality factor by 10.5%. These findings suggest that current models inadequately represent dust effects, favoring SDM for its simplicity, while partial shading serves as a weak approximation.

关键词： dust soiling equivalent circuit model optimization algorithms parameter identification photovoltaics sustainable development goal 7

来源：评论

学校读者我要写书评

暂无评论

Recent advances in Multi-objective Cuckoo Search Algorithm, its variants and applications

引用

ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 2025年 1-28页

作者： Makhadmeh, Sharif Naser Awadallah, Mohammed A. Kassaymeh, Sofian Al-Betar, Mohammed Azmi Sanjalawe, Yousef Kouka, Shaimaa Al-Redhaei, Anessa Univ Jordan UJ King Abdullah II School Informat Technol Dept Informat Technol Amman 11942 Jordan Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Al Aqsa Univ Dept Comp Sci POB 4051 Gaza 4051 Palestine Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res POB 19328 Amman Jordan Aqaba Univ Technol Fac Informat Technol Software Engn Dept Aqaba Jordan

The Cuckoo Search Algorithm (CSA) is an optimization algorithm inspired by the brood parasitism behavior of cuckoo birds. It mimics the reproductive and breeding tactics of cuckoos to tackle optimization challenges. To better handle multi-objective optimization problems (MOPs), a variation called the multi-objective CSA (MOCSA) has been developed. MOCSA is designed to uncover a spectrum of solutions, each providing a balance between various objectives, thereby allowing decision-makers to choose the optimal solution according to their specific preferences. The literature has witnessed a notable increase in the number of published MOCSAs, with MOCSA research papers recorded in the SCOPUS database. This paper presents a comprehensive survey of 123 distinct variants of MOCSAs published in scientific journals. Through this survey, researchers will gain insights into the growth of MOCSA, the theoretical foundations of multi-objective optimization and the MOCSA algorithm, the various existing MOCSA variants documented in the literature, the application domains in which MOCSA has been employed, and a critical analysis of its performance. In sum, this paper provides future research directions for MOCSA. Overall, this survey provides a valuable resource for researchers seeking to explore and understand the advancements, applications, and potential future developments in the field of multi-objective CSA.

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Flight tracking control of mini-drone: Flower pollination algorithm based model-free control

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2025年第6期239卷 578-592页

作者： Glida, Hossam Eddine Abdelghani, Chelihi Chouki, Sentouh Univ Caen Normandie LIS UR 7478 UNICAEN ENSICAEN F-14000 Caen France Univ Biskra Dept Elect Engn Lab LI3CUB Biskra Algeria Constantine 1 Univ Fac Technol Dept Elect Constantine Algeria Univ Polytech Hauts Defrance UMR 8201 CNRS LAMIH Valenciennes France INSA Hauts Defrance Valenciennes France

This paper presents a novel model-free control approach, Flower Pollination Algorithm-based Model-Free Control (FPA-MFC), for trajectory tracking of mini-drone quadrotor unmanned aerial vehicles (UAVs). The proposed approach employs an adaptive estimator based on filtered signals to approximate the nonlinear dynamic functions of the system. This approximator allows the development of a robust decentralized control law able to separately manage the position and attitude dynamics of the drone. The controller design is free of any prior knowledge of the system dynamics, and the control inputs are computed solely from instantaneous input and output measurements. Indeed, this can significantly reduce the computational burden and improve the efficiency of the control algorithm while preserving its simplicity. The design gains of the control law are selected using the metaheuristic flower pollination algorithm to achieve greater trajectory tracking performance and ensure closed-loop system stability. Simulation tests conducted on the Parrot mini drone platform validate the effectiveness and superior performance of FPA-MFC, compared to similar controllers without optimization and using the particle swarm optimization algorithm.

关键词： Mini-drone model-free control optimization algorithms flower pollination algorithm

来源：评论

学校读者我要写书评

暂无评论

AI-based Control and optimization Algorithm for Synthesis of Electrified Drives

AI-based Control and Optimization Algorithm for Synthesis of...

引用

Transportation Electrification Conference and Expo (ITEC)

作者： Sturm, Axel Inglese, Michele Henze, Roman Kuecuekay, Ferit Tech Univ Carolo Wilhelmina Braunschweig Inst Automot Engn Braunschweig Germany Innovationsgesellschaft Tech Univ Braunschweig mb Ctr Automot Engn Braunschweig Germany

ISBN: (纸本)9798350317671;9798350317664

Drive electrification is one major development area to decrease greenhouse emissions. Especially computer aided synthesis and optimization tools are necessary for early drive concept development as well as the concept definition. Depending on the investigation and boundary parameters, the number of possible variants is in the millions. Hence the result quality as well as the investigation time depend significantly on the used optimization and control algorithm of the synthesis. In this paper we introduce an algorithm, comprising of a fuzzy logic in combination with a search space algorithm for controlling a drive system synthesis as well as optimizations. In a first application of the algorithm for a D-segment parallel hybrid topology optimization, the global optimum was found within 1.33 % of all possible simulations. Furthermore, the algorithm was able to find additional solutions close to the global optimum.

关键词： drive optimization drive synthesis optimization algorithms artificial intelligence hybrid drives fuzzy search space algorithm

来源：评论

学校读者我要写书评

暂无评论

FRAM EVALUATION AS UNIFIED MEMORY FOR CONVEX optimization algorithms 6

FRAM EVALUATION AS UNIFIED MEMORY FOR CONVEX OPTIMIZATION AL...

引用

6th European Embedded Design in Education and Research Conference (EDERC)

作者： Gionata, Cimini Alberto, Bemporad Gianluca, Ippoliti Sauro, Longhi Univ Politecn Marche I-60131 Ancona Italy IMT Inst Adv Studies I-55100 Lucca Italy

ISBN: (纸本)9781479968435

Ferroelectric Random Access Memory (FRAM) by Texas Instruments (TI) is a non-volatile memory which allows lower power and faster data throughput compared to other nonvolatile solutions. These features have accelerated the interest in this technology as the future of embedded unified memory, in particular in data logging, remote sensing and Wireless Sensor Network (WSN). The application of Model Predictive Control (MPC) in WSN has gained lot of attention in the last years and it requires solving convex optimization problems in real-time. In this paper several convex optimization algorithms have been implemented and compared on a FRAM-based MSP-EXP430FR5739 node by TI, to evaluate its suitability in extending the potentialities of onboard volatile Static Random Access Memory (SRAM) for embedded optimization-based control.

关键词： Convexfunctions Ferroelectricfilms Nonvolatilememory Randomaccessmemory Resourcemanagement Wirelesscommunication Wirelesssensornetworks Wireless sensor networks Static random access storage WSN gene optimization algorithms Titanium Thallium Nonvolatile memory LOW POWER Memory Memory Ferroelectric RAM data throughput

来源：评论

学校读者我要写书评

暂无评论

Modeling of Missing Data Prediction: Computational Intelligence and optimization algorithms

Modeling of Missing Data Prediction: Computational Intellige...

引用

IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Leke, Collins Twala, Bhekisipho Marwala, Tshilidzi Univ Johannesburg Dept Elect & Elect Engn Sci Johannesburg South Africa

ISBN: (纸本)9781479938407

Four optimization algorithms (genetic algorithm, simulated annealing, particle swarm optimization and random forest) were applied with an MLP based auto associative neural network on two classification datasets and one prediction dataset. This work was undertaken to investigate the effectiveness of using auto associative neural networks and optimization algorithms in missing data prediction and classification tasks. If performed appropriately, computational intelligence and optimization algorithm systems could lead to consistent, accurate and trustworthy predictions and classifications resulting in more adequate decisions. The results reveal GA, SA and PSO to be more efficient when compared to RF in terms of predicting the forest area to be affected by fire. GA, SA, and PSO had the same accuracy of 93.3%, while RF showed 92.99% accuracy. For the classification problems, RF showed 93.66% and 92.11% accuracy on the German credit and Heart disease datasets respectively, outperforming GA, SA and PSO.

关键词： optimization algorithms auto-associative neural networks prediction classification missing data

来源：评论

学校读者我要写书评

暂无评论

Research on the Application of Information Sharing Technology in Distributed Photovoltaic Aggregation optimization Control Strategy 5

Research on the Application of Information Sharing Technolog...

引用

5th IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Ling, Daiwei Huang, Kailang Wang, Kai Kong, Detian Zhou, Ran Wanning Power Supply Bureau of Hainan Power Grid Co. Ltd Wanning China

ISBN: (纸本)9798331533694

This paper studies the application of information sharing technology in distributed photovoltaic aggregation optimization control, and proposes an optimization control strategy based on distributed gradient descent algorithm. This strategy realizes collaborative control between each substation through information sharing technology to reduce power fluctuations and improve the overall efficiency of photovoltaic power generation. This paper constructs a mathematical model of distributed photovoltaic system and designs a corresponding distributed gradient descent algorithm, which can realize global optimization control under the premise of exchanging only a small amount of information between each substation. The effectiveness of the optimization control strategy is verified by modeling and testing the photovoltaic system data of a certain actual substation. The simulation results show that after applying the distributed gradient descent algorithm, the information sharing between each substation improves the overall power generation efficiency of the system and reduces power fluctuations. Specific data show that without increasing additional hardware costs, the power generation efficiency of the system is increased by about 8% and the power fluctuation is reduced by about 12%. These results fully demonstrate that the proposed distributed photovoltaic optimization control strategy based on information sharing has significant advantages in practical applications. © 2025 IEEE.

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Golden Jackal optimization Algorithm for Mobile Robotic Path Planning

An Adaptive Golden Jackal Optimization Algorithm for Mobile ...

引用

2024 International Conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Chen, Siwen Zheng, Xiujuan Shanghai United Imaging Healthcare Co. Ltd. Shanghai China Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and Technology Wuhan China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Given the shortcomings of the traditional Golden Jackal optimization (GJO) algorithm, including limited accuracy and slow convergence speed in solving mobile robot path planning problems, an improved adaptive Golden Jackal optimization (IAGJO) method is proposed. Firstly, a nonlinear adaptive energy strategy is presented to adjust the energy decay pattern, balancing global and local search. Secondly, an enhanced position update mechanism based on Cauchy and Gaussian mutation increases population diversity and guide the search based on optimal individuals, thereby facilitating efficient exploration of unknown regions and avoiding local optima. Finally, the IAGJO algorithm is applied to mobile robot path planning (MRPP), demonstrating that the IAGJO achieves shorter path lengths and higher search efficiency, exhibiting significant advantages over existing methods. © 2025 SPIE.

关键词： optimization algorithms

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：