检索结果-内蒙古大学图书馆

9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Wu, Jiajun Wang, Hongxian Xidian University National Key Laboratory of Radar Signal Processing Xi'an China

ISBN: (纸本)9798350376548

Support vector machines (SVM) are commonly used to solve classification and regression problems, however a suitable kernel function needs to be selected to achieve an effective solution. To solve this problem, we propose an improved sand cat swarm optimization algorithm (SSCSO) to optimize the hyperparameters of SVM. First, Logistic- Icmic mapping is used to generate the initial population to improve the convergence speed. Then, the spiral search strategy is used to balance the global and local search capabilities. Finally, the alert mechanism in the Sparrow search algorithm is integrated to further improve the convergence speed. Simulation experiments of 12 benchmark test functions show that SSCSO has faster convergence speed and more accurate search capabilities than SCSO, SCSO1, SCSO2, SCSO3, and Sparrow Search Algorithm (SSA). In the experiment of modulated signal classification, the internal characteristics of the signal were characterized by extracting high-order cumulants and instantaneous characteristic parameters, and a multi-dimensional feature matrix was constructed for data analysis. The classification accuracy of the SSCSO-SVM algorithm is 4. 3%, 1. 6% and 2. 3% higher than that of the SVM, SCSO-SVM and SSA-SVM algorithms respectively, indicating that the SSCSO-SVM algorithm shows better performance in optimizing the SVM parameters. © 2024 IEEE.

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Research on PCI Planning Problem Based on Genetic Algorithm and Sequential Algorithm 6

Research on PCI Planning Problem Based on Genetic Algorithm ...

引用

6th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Sun, Haoming Sun, Yu Lu, Ye Chen, Jiajun Qu, Yixuan School of Physics and Optoelectronic Engineering Shandong University of Technology Shandong Province Zibo City China School of Electrical and Electronic Engineering Shandong University of Technology Shandong Province Zibo City China School of Computer Science and Technology Shandong University of Technology Shandong Province Zibo City China School of Electrical and Electronic Engineering Shandong University of and Technology Shandong Province Zibo City China School of Materials Science and Engineering Shandong University of and Technology Shandong Province Zibo City China

ISBN: (纸本)9798350374315

With the rapid development of mobile communication technology, the construction of the 5G network has become a hotspot worldwide. In this context, rational PCI network planning becomes particularly important. This study takes an area as an example and establishes a multi-objective optimization model based on genetic algorithm (GA) and sequential algorithm (SA) for the PCI planning problem in mobile communication system. By collecting and analyzing key data such as cell numbers and MR data, the objective function Fit is established, and the genetic algorithm is applied to solve the problem with the objective of minimizing the conflict, confusion and the sum of the number of MRs of modulo 3 interference in the PCI allocation, and with the PCI value allocated to each cell as the decision variable. The results show that the method can effectively improve network coverage and system capacity, reduce interference between users, and improve user experience. © 2024 IEEE.

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Minimizing Electromagnetic Radiation in Urban Electric Power Systems through Improved Deep Q-Learning Methodology 3

Minimizing Electromagnetic Radiation in Urban Electric Power...

引用

3rd International Conference on Energy, Power and Electrical Technology, ICEPET 2024

作者： Li, Jianxin Luo, Zongchang Lin, Jinlin Pan, Jiqi Hu, Mengzhu Wei, Yarong Electric Power Research Institute of Guangxi Power Grid Co. LTD. Guangxi Power Grid Equipment Monitoring and Diagnosis Technology Innovation Center Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Nanning China Guangxi Power Grid Co. LTD. Nanning China

ISBN: (纸本)9798350352658

In response to the growing concerns over electromagnetic radiation (EMR) emanating from urban electric power facilities, this study proposes a novel optimization framework. The rapid expansion of urban infrastructure and the consequent proliferation of such facilities necessitate innovative strategies to mitigate the potential health risks associated with EMR, while ensuring the efficient distribution of electricity. This research introduces an optimization method based on an enhanced Deep Q-Learning algorithm, incorporating advancements such as Dual Deep Q Networks (DDQN), Prioritized Experience Replay (PER), and Noisy Networks. These modifications aim to refine the algorithm's ability to navigate the complex dynamics of urban EMR management. The proposed method demonstrates an improvement in minimizing EMR exposure in urban settings, presenting a balanced approach to maintaining power distribution efficiency. By leveraging real-world data and simulations, the study not only contributes to the theoretical modeling of EMR mitigation but also offers practical insights for the design and operation of urban electric power facilities. This work is a step forward in the ongoing efforts to ensure sustainable urban development that prioritizes environmental health and energy efficiency. © 2024 IEEE.

关键词： Deep Q-Learning Electromagnetic radiation environmental impact optimization algorithms urban development

来源：评论

学校读者我要写书评

暂无评论

Generating Optimal Class Integration Test Orders Using Genetic algorithms

引用

INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2022年第6期32卷 871-892页

作者： Zhang, Yanmei Jiang, Shujuan Ding, Yanru Yuan, Guan Liu, Junjie Lu, Dongyu Qian, Junyan China Univ Min & Technol Mine Digitalizat Engn Res Ctr Sch Comp Sci & Technol Minist Educ Xuzhou 221116 Jiangsu Peoples R China Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Guangxi Zhuang Peoples R China

In recent years, many intelligent optimization algorithms have been applied to the class integration and test order (CITO) problem. These algorithms also have been proved to be able to efficiently solve the problem. Here, the design of fitness function is a key task to generate the optimal solution. To better solve the class integration and test order problem, we propose a new fitness function to generate the optimal solution that achieves a balanced compromise between the different measures (objectives) such as the total number of stubs and the total stubbing complexity in this paper. We used some programs to compare and evaluate the different approaches. The experimental results show that our proposed approach is encouraging to some extent in solving the class integration and test order problem.

关键词： optimization algorithms test order fitness function number of stubs stubbing complexity

来源：评论

学校读者我要写书评

暂无评论

Black hole algorithm: A comprehensive survey

引用

APPLIED INTELLIGENCE 2022年第10期52卷 11892-11915页

作者： Abualigah, Laith Abd Elaziz, Mohamed Sumari, Putra Khasawneh, Ahmad M. Alshinwan, Mohammad Mirjalili, Seyedali Shehab, Mohammad Abuaddous, Hayfa Y. Gandomi, Amir H. Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Zagazig Univ Fac Sci Dept Math Zagazig 44519 Egypt Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman 346 U Arab Emirates Galala Univ Dept Artificial Intelligence Sci & Engn Suez Egypt Tomsk Polytech Univ Sch Comp Sci & Robot Tomsk Russia Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat 90 Bowen Terrace Fortitude Valley Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea World Islamic Sci & Educ Univ Informat Technol Amman Jordan Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia

This paper provides an in-depth literature review of the Black Hole Algorithm (BHA) which is considered as a recent metaheuristic. BHA has been proven to be very efficient in different applications. There has been several modifications and variants of this algorithm in the literature, so this work reviews various variants of the BHA. The applications of BHA in engineering problems, clustering, task scheduling, image processing, etc. have been thoroughly reviewed as well. This review article sheds lights on the pros and cons of this algorithm and enables finding a right variant of this algorithm for a certain application area. The paper concludes with an in-depth future direction.

关键词： Black hole algorithm optimization algorithms Meta-heuristics Real-word problems optimization problems Algorithm optimization

来源：评论

学校读者我要写书评

暂无评论

optimization techniques for analog and RF circuit designs: an overview

引用

ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING 2021年第3期106卷 511-524页

作者： Kouhalvandi, Lida Ceylan, Osman Ozoguz, Serdar Istanbul Tech Univ Elect & Commun Engn Dept Istanbul Turkey Maury Microwave Ontario CA 91764 USA

optimizations have gained much consideration from the researchers working in the domains of analog and radio frequency (RF), recently. Dealing with highly nonlinear behavior of active components and aiming to meet design specifications are common issues in all nonlinear circuit designs. As a consequence and accordingly, many studies have been conducted on diverse optimization methods and algorithms for tackling the design problems and meeting optimal solutions with high accuracy. The main purpose of this article is to provide a comprehensive and systematic literature review for the optimization approaches applied by the researchers for designing various analog and microwave circuits. We focus on considering the existing optimization methods from the newly published optimization methods in the last decade. Thus, this study can guide and enlighten complementary metal-oxide-semiconductor analog and RF microwave circuit designers to consider optimization methods commonly used in both areas and to expand conventional performance figures used in their area.

关键词： optimization algorithms CMOS analog circuits RF microwave designs

来源：评论

学校读者我要写书评

暂无评论

Data Assimilation and Online optimization With Performance Guarantees

引用

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第5期66卷 2115-2129页

作者： Li, Dan Martinez, Sonia Univ Calif San Diego Dept Mech & Aerosp Engn La Jolla CA 92092 USA

This article considers a class of real-time stochastic optimization problems dependent on an unknown probability distribution. In the considered scenario, data are streaming frequently while trying to reach a decision. Thus, we aim to devise a procedure that incorporates samples (data) of the distribution sequentially and adjusts decisions accordingly. We approach this problem in a distributionally robust optimization framework and propose a novel Online Data Assimilation Algorithm (OnDA Algorithm) for this purpose. This algorithm guarantees out-of-sample performance of decisions with high probability, and gradually improves the quality of the decisions by incorporating the streaming data. We show that the OnDA Algorithm converges under a sufficiently slow data streaming rate, and provide a criteria for its termination after certain number of data have been collected. Simulations illustrate the results.

关键词： optimization Uncertainty Data assimilation Random variables Probability distribution Real-time systems Decision making Computational methods intelligent systems optimization optimization algorithms stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Robust Controller Design for Stochastic Nonlinear Systems via Convex optimization

引用

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第10期66卷 4731-4746页

作者： Tsukamoto, Hiroyasu Chung, Soon-Jo CALTECH Aerosp Pasadena CA 91125 USA CALTECH Dept Aerosp GALCIT Pasadena CA 91125 USA

This article presents ConVex optimization-based Stochastic steady-state Tracking Error Minimization (CV-STEM), a new state feedback control framework for a class of Ito stochastic nonlinear systems and Lagrangian systems. Its innovation lies in computing the control input by an optimal contraction metric, which greedily minimizes an upper bound of the steady-state mean squared tracking error of the system trajectories. Although the problem of minimizing the bound is nonconvex, its equivalent convex formulation is proposed utilizing SDC parameterizations of the nonlinear system equation. It is shown using stochastic incremental contraction analysis that the CV-STEM provides a sufficient guarantee for exponential boundedness of the error for all time with L-2-robustness properties. For the sake of its sampling-based implementation, we present discrete-time stochastic contraction analysis with respect to a state- and time-dependent metric along with its explicit connection to continuous-time cases. We validate the superiority of the CV-STEM to PID, H-infinity, and baseline nonlinear controllers for spacecraft attitude control and synchronization problems.

关键词： LMIs nonlinear systems optimization algorithms robust control stochastic optimal control

来源：评论

学校读者我要写书评

暂无评论

Application of the improved chaotic grey wolf optimization algorithm as a novel and efficient method for parameter estimation of solid oxide fuel cells model

引用

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2021年第73期46卷 36454-36465页

作者： Hao, Peng Sobhani, Behnam Shanxi Inst Mech & Elect Engn Changzhi 046011 Shanxi Peoples R China Iran Univ Sci & Technol Sch Elect Engn Tehran Iran

ABS T R A C T In this paper, important functional parameters of solid oxide fuel cells are identified by introducing a novel high-speed optimization method, namely adaptive chaotic grey wolf optimization algorithm. The suggested optimization method is obtained by combining the adaptive grey wolf optimization and chaotic grey wolf optimization algorithms. The chaotic algorithm is applied to the basic grey wolf optimization to achieve higher convergence speed, keep the population's diversity, and provide an initial population with uniform distribution. Besides, a nonlinear convergence factor is defined for balancing the global and local exploration abilities. Employing the improved convergence factor resulted in a new version of the grey wolf optimization algorithm, namely adaptive grey wolf optimization algorithm. Adaptive chaotic grey wolf optimization algorithm adopts the advantages of both chaotic grey wolf optimization and adaptive grey wolf optimization methods simultaneously. The adaptive grey wolf optimization algorithm is applied to a 5 kW dynamic tubular stack. The results of the simulation report the lowest values of mean squared error, higher accuracy, higher robustness, and high convergence speed for the adaptive grey wolf optimization algorithm compared to some well-known optimization methods. Besides, the proposed method shows a good agreement with experimental results with lower computational difficulty. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Adaptive grey wolf optimization algorithm Parameter identification Solid oxide fuel cell optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

The Solar System Algorithm: A Novel Metaheuristic Method for Global optimization

引用

IEEE ACCESS 2021年 9卷 4542-4565页

作者： Zitouni, Farouq Harous, Saad Maamri, Ramdane Kasdi Merbah Univ Dept Comp Sci Ouargla 30000 Algeria Abdelhamid Mehri Univ LIRE Lab Constantine 25000 Algeria United Arab Emirates Univ UAEU Dept Comp Sci & Software Engn Abu Dhabi U Arab Emirates Abdelhamid Mehri Univ Dept Comp Sci Constantine 25000 Algeria

A novel metaheuristic algorithm for global optimization, called the Solar System Algorithm (SSA), is presented. The proposed algorithm imitates the orbiting behavior of some objects found in the solar system: i.e., Sun, planets, moons, stars, and black holes. SSA is used to solve five well-known engineering design problems: three-bar truss, pressure vessel, tension/compression spring, welded beam, and gear train. The obtained results are compared to 16 state-of-the-art metaheuristic algorithms. They show that SSA is very competitive in solving the considered engineering problems. In addition, the performance of SSA is evaluated on the benchmarks CEC 2014 and CEC 2020. The experimental results are compared to 27 (12 for CEC 2014 and 15 for CEC 2020) metaheuristic algorithms. They demonstrate that SSA is very promising in finding efficient solutions.

关键词： optimization algorithms metaheuristic algorithms solar system constrained engineering optimization test functions benchmark CEC 2014 benchmark CEC 2020 givens matrices

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：