检索结果-内蒙古大学图书馆

6th International Electric Drives Production Conference (EDPC)

作者： Herrmann, Patrick Stenzel, Peer Voegele, Ulrich Endisch, Christian Tech Hsch Ingolstadt Res Grp Electromobil & Learning Syst D-85049 Ingolstadt Germany AUDI AG D-85045 Ingolstadt Germany

ISBN: (纸本)9781509029099

High power density and efficiency are key factors for automotive traction machines. One possible way to reach these goals is to increase the slot filling factor. As yet, most research has either been focused on optimizing the slot geometry with a given magnet wire diameter or on finding the optimal diameter for a given geometry. Oftentimes the submitted results lead either to a magnetically suboptimal stator geometry or the suggested winding pattern and geometries are not producible. The introduction of the needle winding technology, as an alternative to the insertion technology for the manufacturing of stators of automotive traction machines, enabled a defined wire placement in the slot. To use the full benefit of this advantage an optimal and producible winding layout is necessary. Therefore, in this article new optimization algorithms are proposed and compared to algorithms found in literature with regard to reachable slot filling factors and producibility. In a case study, the best performing algorithm was used to obtain an optimal combination of wire diameter and slot geometry to maximize the filling factor. With the proposed algorithm feasible winding patterns and slot geometries with an optimized filling factor can be obtained.

关键词： Slot Filling Factor optimization algorithm Slot Geometry Filling Factor optimization Needle Winding

来源：评论

学校读者我要写书评

暂无评论

The Bedbug Meta-heuristic algorithm to Solve optimization Problems

引用

Journal of Bionic Engineering 2023年第5期20卷 2465-2485页

作者： Kouroush Rezvani Ali Gaffari Mohammad Reza Ebrahimi Dishabi Department of Computer Engineering Miyaneh BranchIslamic Azad UniversityMiyanehIran Department of Computer Engineering Tabriz BranchIslamic Azad UniversityTabrizIran

Small parasitic Hemipteran insects known as bedbugs(Cimicidae)feed on warm-blooded mammal’s *** most famous member of this family is the Cimex lectularius or common *** current paper proposes a novel swarm intelligence optimization algorithm called the Bedbug Meta-Heuristic algorithm(BMHA).The primary inspiration for the bedbug algorithm comes from the static and dynamic swarming behaviors of bedbugs in *** two main stages of optimization algorithms,exploration,and exploitation,are designed by modeling bedbug social interaction to search for *** proposed algorithm is benchmarked qualitatively and quantitatively using many test functions including *** results of evaluating BMHA prove that this algorithm can improve the initial random population for a given optimization problem to converge towards global optimization and provide highly competitive results compared to other well-known optimization *** results also prove the new algorithm's performance in solving real optimization problems in unknown search *** achieve this,the proposed algorithm has been used to select the features of fake news in a semi-supervised manner,the results of which show the good performance of the proposed algorithm in solving problems.

关键词： Bedbug Meta-Heuristic algorithm optimization algorithm BMHA

来源：评论

学校读者我要写书评

暂无评论

optimization algorithms used in Cognitive Radio Networks: An Overview 2

Optimization Algorithms used in Cognitive Radio Networks: An...

引用

2nd International Conference on Computer Science and Software Engineering (CSASE)

作者： Salih, Kurdistan Mohsin Shakir, Mohammed Ahmed Univ Duhok Elect & Comp Engn Duhok Kurdistan Regio Iraq

ISBN: (纸本)9781665426329

With the massive demand for spectrum resources due to the massive increase of wireless devices, it was necessary to manage the scarcity of radio spectrum resources. Cognitive Radio is a technology for efficiently using the available spectrum resources in a wireless communication system. However, with the help of using various optimization algorithms, Cognitive Radio can manage and utilize the spectrum of resources more efficiently. This paper gives an overview of the state-of-art research that utilizes many optimization algorithms for different purposes such as sensing, allocating, sharing, and mobilizing the spectrum for better utilization and improving the throughput, convergence speed, delay, and minimization the interference. The main algorithms enclosed in this paper are Genetic algorithm, Particle Swarm optimization, Ant Colony optimization, and Artificial Bee Colony optimization algorithm.

关键词： Cognitive Radio optimization algorithm Genetic algorithm Particle Swarm optimization Ant Colony optimization Bee Colony optimization

来源：评论

学校读者我要写书评

暂无评论

optimization algorithms for Local Features

Optimization Algorithms for Local Features

引用

International Conference on Automation, Communication, Architectonics and Materials

作者： Cao, Jian Chen, Hongqian Mao, Mingyi Beijing Technol & Business Univ Coll Comp & Informat Engn Beijing 100048 Peoples R China

ISBN: (纸本)9783037851036

Feature spaces optimization plays a very important role in object recognition and categorization. After analyzing of several fashionable local features at present, some optimization algorithms based on the information theory are proposed. In this paper, we describe the approaches to recognize generic objects using these features which have been optimized. As baselines for comparison, we also implemented some additional recognition systems using other optimization algorithms. The performance analysis on the obtained experimental results demonstrates that the proposed optimization algorithms are effective and efficient.

关键词： optimization algorithm local features object recognition feature selection clustering

来源：评论

学校读者我要写书评

暂无评论

Compressor airfoil optimization method driven by data-mechanism integration based on evolutionary multi-tasking algorithm

引用

AEROSPACE SCIENCE AND TECHNOLOGY 2024年 148卷

作者： Cheng, Jinxin Zhang, Yong Chen, Jiang Ma, Hui Liu, Beiying Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221008 Peoples R China Beihang Univ Sch Energy & Power Engn Beijing 100191 Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China

To address the challenge of the "curse of dimensionality" in aerodynamic design optimization of compressors, this study introduces an innovative optimization technique suitable for compressor airfoil design. This technique, rooted in a hybrid mechanism-data-driven approach, seamlessly integrates a hierarchical parameterization method, based on elliptic topological deformation, into a multitasking evolutionary algorithm framework. This integration deviates from the conventional approach of treating parameterization methods and optimization algorithms as distinct elements. The proposed method positions airfoil parameterization as its core, constructing two tasks within the optimization algorithm. It leverages the critical influence of the parameterization method on the aerodynamic performance landscape of the airfoils and the intrinsic qualities of the hierarchical parameterization method in the design space. The multitasking evolutionary optimization framework facilitates effective information exchange between tasks, significantly boosting optimization efficiency. In comparison to standard data-driven multitasking evolutionary algorithms, the proposed method achieves superior optimized solutions with merely 11 x D aerodynamic performance evaluations, where D denotes the number of design variables.

关键词： Hybrid driven optimization Multitasking evolutionary optimization algorithm Curse of dimensionality

来源：评论

学校读者我要写书评

暂无评论

Designing thermal demultiplexer: Splitting phonons by negative mass and genetic algorithm optimization

引用

Chinese Physics B 2021年第3期30卷 36-40页

作者： Yu-Tao Tan Lu-Qin Wang Zi Wang Jiebin Peng Jie Ren Center for Phononics and Thermal Energy Science China-EU Joint Laboratory on NanophononicsShanghai Key Laboratory of Special Artificial Microstructure Materials and TechnologySchool of Physics Science and EngineeringTongji UniversityShanghai 200092China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 200092China

We propose the concept of thermal demultiplexer, which can split the heat flux in different frequency ranges intodifferent directions. We demonstrate this device concept in a honeycomb lattice with dangling atoms. From the view ofeffective negative mass, we give a qualitative explanation of how the dangling atoms change the original transport *** first design a two-mass configuration thermal demultiplexer, and find that the heat flux can flow into different ports incorresponding frequency ranges roughly. Then, to improve the performance, we choose the suitable masses of danglingatoms and optimize the four-mass configuration with genetic algorithm. Finally, we give out the optimal configuration witha remarkable effect. Our study finds a way to selectively split spectrum-resolved heat to different ports as phonon splitter,which would provide a new means to manipulate phonons and heat, and to guide the design of phononic thermal devices inthe future.

关键词： phonon transport thermal demultiplexer optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Design of High Gain Metasurface Antenna Using Hybrid African Vulture's optimization and Capuchin Search algorithm for RF Energy Harvesting (Jul, 10.1007/s11277-023-10583-1, 2023)

引用

WIRELESS PERSONAL COMMUNICATIONS 2023年第1期132卷 95-95页

作者： Kumar, M. Vinod Sharma, Dinesh Sri Vasavi Engn Coll Tadepalligudem 534101 Andhra Pradesh India Panjab Univ CCET Chandigarh Dept ECE Chandigarh 160019 India

In general, Metasurface Antennas (MSA) are designed to diminish the antenna shape by enhancing the operating band and directivity. As the efficiency decreases, the design complexity of MSA increases. In order to enhance the antenna design, a high-gain MSA is designed using the hybrid African Vulture's optimization algorithm (AVOA), and the Capuchin Search algorithm (CapSA) is used for Radio Frequency (RF) energy harvesting. The dimensions of the designed antenna are \(1.66\lambda_optimization algorithm \times 1.25\lambda_optimization algorithm \times 0.02\lambda_optimization algorithm\) with a resonating frequency of 5 GHz. To design the high gain MSA, the proposed Hybrid African Vulture’s optimization and Capuchin Search algorithm (Hyb-AVOA-CapSA) is used to enhance the antenna parameters such as radiation efficiency, Bandwidth, gain, and return loss. Therefore, the proposed MSA design has achieved high efficiency and profit. Finally, the simulation has done on HFSS19 and ADS2020 version software; and evaluated using MATLAB. The proposed antenna gives a better efficiency of 70.12%, and resonate at 1.5 GHz of the axial ratio bandwidth at 5 GHz resonant frequency. The gain of the proposed antenna has increased from 6.86 to 7.6 dBi. While examining the comparative outcomes, the proposed approach has attained 22.4%, 23.7% high gain, and 18.85%, 12.6% lower return loss than the compared methods. Thus, the designed MSA is applied in RF energy harvesting applications because of its compact, low-profile, and simple structure. The Rectenna design uses a voltage doubler circuit at the receiver end and produces 5.55 V.

关键词： African vulture’s optimization algorithm Capuchin search algorithm Gain Metasurface antenna RF energy harvesting

来源：评论

学校读者我要写书评

暂无评论

A novel hybrid algorithm based on Biogeography-Based optimization and Grey Wolf Optimizer

引用

APPLIED SOFT COMPUTING 2018年 67卷 197-214页

作者： Zhang, Xinming Kang, Qiang Cheng, Jinfeng Wang, Xia Henan Normal Univ Coll Comp & Informat Engn Xinxiang Henan Peoples R China Engn Technol Res Ctr Comp Intelligence & Data Min Xinxiang Henan Peoples R China

In order to obtain a Biogeography-Based optimization (BBO) algorithm with strong universal applicability, this paper presents a novel hybrid algorithm based on BBO and Grey Wolf Optimizer (GWO), named HBBOG. Firstly, BBO and GWO are improved respectively. For BBO, the mutation operator is got rid of and a differential mutation operation is merged into the migration operator to enhance the global search ability. The original migration operation is replaced by a multi-migration operation to enhance the local search ability. For GWO, the opposition-based learning approach is merged to prevent the algorithm from falling into the local optima to some degree. Then, the improved BBO and the opposition learning based GWO are hybridized by a new strategy, named single-dimensional and all-dimensional alternating strategy, to formulate HBBOG. HBBOG can effectively maximize the two algorithms' advantages and overall balance exploration and exploitation, therefore, it can obtain strong universal applicability. We make a large number of experiments on a set of various kinds of benchmark functions and CEC2014 test set and apply HBBOG to clustering optimization. The experimental results show that HBBOG outperforms quite a few state-of-the-art algorithms. (C) 2018 Elsevier B.V. All rights reserved.

关键词： optimization algorithm Evolutionary algorithm Biogeography-Based optimization Grey Wolf Optimizer Opposition-based learning approach

来源：评论

学校读者我要写书评

暂无评论

Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm

引用

NEURAL COMPUTING & APPLICATIONS 2020年第13期32卷 9383-9425页

作者： Zhao, Weiguo Wang, Liying Zhang, Zhenxing Hebei Univ Engn Sch Water Conservancy & Hydropower Handan 056021 Hebei Peoples R China Univ Illinois Prairie Res Inst Illinois State Water Survey Champaign IL 61820 USA

A novel nature-inspired meta-heuristic optimization algorithm, named artificial ecosystem-based optimization (AEO), is presented in this paper. AEO is a population-based optimizer motivated from the flow of energy in an ecosystem on the earth, and this algorithm mimics three unique behaviors of living organisms, including production, consumption, and decomposition. AEO is tested on thirty-one mathematical benchmark functions and eight real-world engineering design problems. The overall comparisons suggest that the optimization performance of AEO outperforms that of other state-of-the-art counterparts. Especially for real-world engineering problems, AEO is more competitive than other reported methods in terms of both convergence rate and computational efforts. The applications of AEO to the field of identification of hydrogeological parameters are also considered in this study to further evaluate its effectiveness in practice, demonstrating its potential in tackling challenging problems with difficulty and unknown search space. The codes are available at.

关键词： Artificial ecosystem-based optimization Global optimization Constrained problems optimization algorithm Engineering design Hydrogeological parameter

来源：评论

学校读者我要写书评

暂无评论

optimization of automotive battery pack casing based on equilibrium response surface model and multi-objective particle swarm algorithm

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2023年第6期237卷 1183-1194页

作者： Liu, Fei Xu, Yalong Li, Meng Guo, Jiale Han, Bing Shanghai Univ Engn Sci Sch Mech & Automot Engn 333 Long Teng Rd Shanghai 201620 Peoples R China

Lightweight research based on battery pack structural strength can improve the endurance and safety of electric vehicles. Based on the adaptive response surface and multi-objective particle swarm optimization algorithm, this paper proposes an optimization design method for lightweight of battery pack shell. The thickness of the battery pack shell is the optimization parameter. The stress, strain, and frequency of the battery pack shell under typical working conditions are used as boundary conditions. The response surface model is established according to the criterion of cross terms in adaptive response surface method, and the multi-objective particle swarm optimization algorithm is used for iterative solution. The optimization results show that the maximum stress of the battery pack is reduced to the appropriate range, the first-order frequency is increased by 41% to reduce resonance, the maximum deformation is reduced from 2.7 to 1.12 mm, and the total mass is reduced by 26.8%. The battery pack optimization design method proposed in this paper can achieve lightweighting while meeting safety performance.

关键词： Battery pack casing mechanical performance adaptive response surface lightweight energy saving 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：