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Low-Noise,High-Gain 28 GHz LNA Design Using Multi-Objective Optimization with NSGA-Ⅱ and MOPSO 认领 引用
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作者 Spandana Saggurthi Anand Nayyar +1 位作者 Sk Hasane Ahammad Sumendra Yogarayan 《Computers, Materials & Continua》 SCIE EI 2026年第9期691-709,共19页
This work presents a multi-objective optimization framework for systematic design-space exploration of a 28 GHz single-stage cascode LNA(Low noise amplifier)in 22 nm FDSOI technology using NSGA-Ⅱ and MOPSO algorithms... This work presents a multi-objective optimization framework for systematic design-space exploration of a 28 GHz single-stage cascode LNA(Low noise amplifier)in 22 nm FDSOI technology using NSGA-Ⅱ and MOPSO algorithms.The objectives of the paper include simultaneous minimization of noise figure(NF)and power consumption while maximizing gain under matching and stability constraints.Using device parameters and circuit models that were developed for a 22 nm FDSOI process technology,an optimization framework was created in Python,with the passive components LG,LS,LD,LOUT,and COUT chosen to be the variables optimized.The NSGA-Ⅱ optimized design achieves 1.7 dB NF,17 dB gain,and 4.7 mW DC power,while MOPSO achieves 1.8 dB NF,17.1 dB gain,and 5.0 mW power.NSGA-Ⅱ provides improved Pareto diversity and slightly better output matching,whereas MOPSO reduces computational time by 24%with comparable RF performance.The results demonstrate effective multi-objective design-space exploration and controlled algorithm benchmarking at the schematic-level for mm-wave LNA design. 展开更多
关键词 LNA mm-wave multi-objective optimization NSGA-Ⅱ MOPSO internet of things(IoT) S-parameters gain and noise figure
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Improved Artificial Rabbit Optimization Algorithm Fused with Particle Swarm Optimization for Wireless Sensor Network Coverage Optimization 认领 引用 被引量：1
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作者 WU Jin SU Zhengdong 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第2期375-389,共15页
Aiming at the problem of low node coverage during node deployment in wireless sensor network(WSN),an improved artificial rabbit optimization algorithm incorporating particle swarm optimization(ARO-PSO)is proposed for ... Aiming at the problem of low node coverage during node deployment in wireless sensor network(WSN),an improved artificial rabbit optimization algorithm incorporating particle swarm optimization(ARO-PSO)is proposed for network coverage optimization.ARO-PSO successfully combines the stochastic characteristics of ARO and the global characteristics of PSO.Firstly,to optimize the quality of the initial population,Sine chaos mapping is introduced to initialize the population;secondly,to better balance the exploration and exploitation,adaptive settings are made;finally,combined with the characteristics of the ARO energy factor,a population decreasing strategy is introduced to further accelerate the convergence speed of the algorithm.Experimental and analytical comparisons are made with ARO and PSO and 6 other excellent optimizers on 13 benchmark functions.The results show that ARO-PSO largely outperforms the original algorithm.Finally,ARO-PSO is applied to WSN coverage optimization experiments in 2D and 3D environments,and the proposed algorithm exhibits higher network coverage and improves the monitoring quality of the network compared to standard ARO and PSO and other state-of-the-art algorithms.The experimental results fully demonstrate the superiority of the ARO-PSO-based WSN node deployment optimization method. 展开更多
关键词 wireless sensor network(WSN) swarm intelligence optimization artificial rabbits optimization(ARO) particle swarm optimization(PSO) coverage optimization
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自适应扰动PSO算法的城域低空物资配送路径规划 认领 引用 被引量：1
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作者 孙哲 谢雨轩 +1 位作者 袁凯 孙知信 《小型微型计算机系统》 CSCD 北大核心 2026年第1期10-17,共8页
低空物流是发展物流新质生产力的典型应用,本文围绕城市低空环境物资高效运输问题,构建了一种城域无人机配送三维路径规划模型.该模型关注配送活动的时效性和成本要求,反映城市场景的地形特点,可以实现城域环境无人机的高效低能耗物资配... 低空物流是发展物流新质生产力的典型应用,本文围绕城市低空环境物资高效运输问题,构建了一种城域无人机配送三维路径规划模型.该模型关注配送活动的时效性和成本要求,反映城市场景的地形特点,可以实现城域环境无人机的高效低能耗物资配送.进一步为了实现模型求解飞行路径,提出了一种自适应扰动粒子群算法(ADPSO),分别引入拉丁超立方抽样、自适应参数调整和自适应t分布扰动策略来解决粒子群算法易陷入局部最优的问题,提升算法的全局搜索性能.最后通过数据实验及对比仿真,结果表明本文所构建模型及所提方法可以更加有效地实现多场景下城域低空物资配送,特别是在复杂环境中,相比于原算法路径缩短了12.10%. 展开更多
关键词 低空物资配送 无人机 三维路径规划 改进PSO算法 自适应t分布
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基于PSO算法的无人平台无线电能传输系统参数优化 认领 引用 被引量：2
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作者 魏曙光 许非凡 +1 位作者 李嘉麒 袁东 《电源学报》 CSCD 北大核心 2026年第5期275-285,共11页
无人平台具有体积小、成本低、无需载员操作等优势,广泛应用于各类军事任务中。目前,无人平台主要以电能作为能源,其对可靠电能传输方式的需求不断增大,在人员无法参与、充电接口不适配、充电流程效率低等场合,有线充电或更换电池存在弊... 无人平台具有体积小、成本低、无需载员操作等优势,广泛应用于各类军事任务中。目前,无人平台主要以电能作为能源,其对可靠电能传输方式的需求不断增大,在人员无法参与、充电接口不适配、充电流程效率低等场合,有线充电或更换电池存在弊端,无法满足无人平台对电能传输的需求。针对无人平台无线电能传输需求设计系统结构,并基于该结构提出了一种基于粒子群优化PSO(particle swarm optimization)算法的参数优化方法。通过对无人平台无线电能传输系统补偿电路、耦合线圈、储能器件结构及特性进行分析,提出系统参数优化数学模型的优化目标及功率、效率、电流3个方面的约束条件,设计基于PSO算法的无人平台无线电能传输系统参数优化方法的算法流程。通过算法仿真与样机实验相结合的方法进行验证,结果表明,提出的基于PSO算法的无人平台无线电能传输系统参数优化方法能够降低系统输出电压和功率、效率随负载电阻和耦合系数变化的波动幅度,提高无人平台无线电能传输系统的鲁棒性和环境适应性。 展开更多
关键词 无线电能传输 LCC/S补偿电路 参数优化方法 粒子群优化算法
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基于PSO-GWO-RF的液压系统智能故障诊断方法研究 认领 引用 被引量：2
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作者 郭媛 刘迎春 +1 位作者 李孟飞 吴凛 《机床与液压》 北大核心 2026年第5期211-217,共7页
为了检测液压系统的多源信息故障,提出一种基于粒子群优化(PSO)与灰狼优化(GWO)融合算法(PSO-GWO)的随机森林(RF)超参数优化方法(PSO-GWO-RF)。PSO-GWO算法结合PSO的快速收敛性和GWO的全局搜索能力,通过混合更新策略(交替采用GWO围猎机... 为了检测液压系统的多源信息故障,提出一种基于粒子群优化(PSO)与灰狼优化(GWO)融合算法(PSO-GWO)的随机森林(RF)超参数优化方法(PSO-GWO-RF)。PSO-GWO算法结合PSO的快速收敛性和GWO的全局搜索能力,通过混合更新策略(交替采用GWO围猎机制和PSO速度更新)优化RF的超参数,显著提升模型的分类性能。对原始数据集进行多维度特征提取与融合,采用最小-最大归一化方法对数据进行标准化预处理,并合理划分训练集与测试集。基于公开液压数据集开展实验,结果表明:经PSO-GWO算法优化的RF模型在交叉验证准确率和训练准确率上均表现优异,二者相互验证,证明了该模型具有良好的泛化性能、较强的鲁棒性以及较快的收敛速度。与传统方法(RF、SVM、CNN等)及其他优化算法(GA、BWO)进一步进行对比,结果表明:PSO-GWO-RF的分类准确率达97.58%,较未优化的RF提升了11.13%,且具有更强的泛化能力和鲁棒性。所提算法显著提升了故障诊断的准确率,有效提升诊断效率,为液压系统智能故障诊断提供了新的技术途径。 展开更多
关键词 智能故障诊断 粒子群灰狼融合优化 随机森林
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Memristor devices for next-generation computing:from performance optimization to application-specific co-design 认领 引用 被引量：1
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作者 Zhaorui Liu Caifang Gao +5 位作者 Jingbo Yang Zuxin Chen Enlong Li Jun Li Mengjiao Li Jianhua Zhang 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2026年第1期119-146,共28页
Memristors have emerged as a transformative technology in the realm of electronic devices,offering unique advantages such as fast switching speeds,low power consumption,and the ability to sensor-memory-compute.The app... Memristors have emerged as a transformative technology in the realm of electronic devices,offering unique advantages such as fast switching speeds,low power consumption,and the ability to sensor-memory-compute.The applications span across non-volatile memory,neuromorphic computing,hardware security,and beyond,prompting memristors to become a versatile solution for next-generation computing and data storage systems.Despite enormous potential of memristors,the transition from laboratory prototypes to large-scale applications is challenging in terms of material stability,device reproducibility,and array scalability.This review systematically explores recent advancements in high-performance memristor technologies,focusing on performance enhancement strategies through material engineering,structural design,pulse protocol optimization,and algorithm control.We provide an in-depth analysis of key performance metrics tailored to specific applications,including non-volatile memory,neuromorphic computing,and hardware security.Furthermore,we propose a co-design framework that integrates device-level optimizations with operational-level improvements,aiming to bridge the gap between theoretical models and practical implementations. 展开更多
关键词 memristor performance optimization device design neuromorphic computing
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Optimization of throttling windows to improve flow control of three-way control combiner valves 认领 引用 被引量：1
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作者 Jin-yuan QIAN Zhe-hui MA +7 位作者 Shi-jie LIN Chuang LIU Yu-wei WANG Fei LING Liang ZHANG Man-man CUI Tian-zuo QU Zhi-jiang JIN 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2026年第3期275-287,共13页
Three-way control combiner valves(TCCVs)are critical components used in nuclear power plants to regulate the concentration of boron acid for neutron absorption and reactor safety.However,current TCCV designs often suf... Three-way control combiner valves(TCCVs)are critical components used in nuclear power plants to regulate the concentration of boron acid for neutron absorption and reactor safety.However,current TCCV designs often suffer from suboptimal control performance and high flow resistance,leading to control deviations and reduced operational efficiency.In this paper,a numerical model based on the standard K–ωturbulence model is established and validated against experimental data to analyze the flow characteristics and local flow resistance of a TCCV.A parametric design method for the throttling windows is proposed,establishing relationships between shape parameters and performance indexes,including control performance and flow resistance.The adaptive non-dominated sorting genetic algorithm(ANSGA-II)is used to optimize the shape parameters of the throttling windows.The optimization results show an improvement in the performance indexes of the TCCV,with the adjustable operating range increasing by 31.0%and the maximum local resistance decreasing by 18.3%.We also introduce the concepts of effective and controllable domains to characterize the inlet backflow phenomena and regulation dead zones,which are crucial for ensuring the reliability and effectiveness of control valves.These findings provide insights for enhancing the design and performance of TCCVs in nuclear power plants. 展开更多
关键词 Three-way valve Intersection flow Throttling windows Multi-objective optimization
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A novel Angle-Constrained Optimization method of Conformal Lattice Structures 认领 引用 被引量：1
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作者 Jun Yan Weibin Xu +2 位作者 Fuhao Wang Sixu Huo Kun Yan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期269-295,共27页
Conformal truss-like lattice structures face significant manufacturability challenges in additive manufac-turing due to overhang angle limitations.To address this problem,we propose a novel angle-constrained optimizat... Conformal truss-like lattice structures face significant manufacturability challenges in additive manufac-turing due to overhang angle limitations.To address this problem,we propose a novel angle-constrained optimization method grounded in the global adjustment of nodal coordinates.First,a build direction is selected to minimize the number of violating struts.Then,an angular-constraint matrix is assembled from strut direction vectors,and analytical sensitivities with respect to nodal coordinates are derived to enable efficient constrained optimization under nonlinear angular inequality constraints.Numerical studies on two complex curved-surface lattices demonstrate that all overhang violations are eliminated while only minor changes are induced in global stiffness and strength.In particular,the maximum displacement of an ergonomic insole varies by only 2.87%after optimization.The results confirm the method’s versatility and engineering robustness,providing a practical approach for additive manufacturing-oriented lattice structure design. 展开更多
关键词 Conformal lattice structures additive manufacturing structural optimization complex structures
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Optimization of microgrid scheduling based on multi-strategy improved MOPSO algorithm 认领 引用
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作者 Yang Xue Shiwei Liang +1 位作者 Fengwei Qian Jinyi Tang 《Global Energy Interconnection》 EI CSCD 2025年第6期959-968,共10页
A multi-strategy Improved Multi-Objective Particle Swarm Algorithm(IMOPSO)method for microgrid operation optimization is proposed for the coordinated optimization problem of microgrid economy and environmental protect... A multi-strategy Improved Multi-Objective Particle Swarm Algorithm(IMOPSO)method for microgrid operation optimization is proposed for the coordinated optimization problem of microgrid economy and environmental protection.A grid-connected microgrid model containing photovoltaic cells,wind power,micro gas turbine,diesel generator,and storage battery is constructed with the aim of optimizing the multi-objective grid-connected microgrid economic optimization problem with minimum power generation cost and environmental management cost.Based on the optimization of the standard multi-objective particle swarm optimization algorithm,four strategies are introduced to improve the algorithm,namely,Logistic chaotic mapping,adaptive inertia weight adjustment,adaptive meshing using congestion distance mechanism,and fuzzy comprehensive evaluation.The proposed IMOPSO is applied to the microgrid optimization problem and the performance is compared with other unimproved multi-objective gray wolf algorithm(MOGWO),multi-objective ant colony algorithm(MOACO),and MOPSO algorithms,and the total cost of the proposed method is reduced by 3.15%,8.34%,and 10.27%,respectively.The simulation results show that IMOPSO can more effectively reduce the cost and optimize power distribution,and verify the effectiveness of the proposed method. 展开更多
关键词 Microgrid Multi-objective particle swarm System economic operation Optimal scheduling
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基于分层结构和多策略自适应机制的GA-PSO微震震源定位优化算法 认领 引用
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作者 周官群 罗世凌 +4 位作者 高永新 张维鑫 金学良 孟凡彬 王亚飞 《煤炭科学技术》 EI CAS CSCD 北大核心 2026年第6期283-293,共11页
微震震源定位是地质灾害监测与矿区安全预警中的关键环节,但受现场环境噪声强烈等不同类型的影响,传统算法易陷入局部最优,收敛速度慢,难以满足复杂地下条件下的高精度定位需求。为提升定位精度与优化效率,提出一种分层结构与多策略自... 微震震源定位是地质灾害监测与矿区安全预警中的关键环节,但受现场环境噪声强烈等不同类型的影响,传统算法易陷入局部最优,收敛速度慢,难以满足复杂地下条件下的高精度定位需求。为提升定位精度与优化效率,提出一种分层结构与多策略自适应机制的GA-PSO微震震源定位优化算法:首先利用遗传算法(Genetic Algorithm,GA)在全局范围内进行粗搜索,快速获取高质量初始震源位置;随后引入具备“探索群–利用群”结构的自适应粒子群优化(Particle Swarm Optimization,PSO),结合指数衰减的惯性权重、动态学习因子及精英粒子信息交换策略,实现对空间的精细局部优化。该分层混合机制旨在协调全局搜索与局部寻优之间的平衡,提升算法的收敛性能与定位稳定性。通过典型多维复杂函数的优化测试,结果表明所提算法在寻优精度与收敛速度方面均优于传统PSO、GA及优化PSO算法。将该算法应用于实际矿区的校正炮数据中,震源空间定位误差控制在15 m以内,精确度较传统算法提高了12.29%,速度模型反演结果更为准确,适应度函数收敛更快,体现出良好的稳健性与工程适应性。所提出的GA-PSO混合优化算法有效融合了遗传算法的全局搜索优势与粒子群优化算法的高效局部搜索能力,显著提升了微震震源定位在复杂地质环境中的精度与稳定性,为震源精确定位提供了切实可行的优化路径。 展开更多
关键词 遗传算法(GA) 粒子群优化(PSO) 多策略自适应 全局优化 微震定位
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Structural optimization of stress-bearing structures of nearly incompressible problems under design-dependent pressure loads 认领 引用 被引量：1
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作者 T.T.BANH N.T.Y.NGUYEN +1 位作者 H.P.BAN D.LEE 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2026年第4期905-926,共22页
An efficient and innovative method is presented for the stress-related structural topology optimization(TO)in coupled mechanical-pressure systems by leveraging flexible polygonal meshes.With a polytopal composite fini... An efficient and innovative method is presented for the stress-related structural topology optimization(TO)in coupled mechanical-pressure systems by leveraging flexible polygonal meshes.With a polytopal composite finite element approach,the volumetric locking in nearly incompressible materials is reduced.A fluid-flow-based model is built,in which a design-dependent pressure variable is introduced to capture the loading conditions within the system.The P-norm approach consolidates the stress metrics into a global measure,while the clustered regional scaling and adaptive techniques enhance the solutions for stress-limited cases.The primary contributions of this work include a novel framework for addressing the stress challenges in coupled mechanical-pressure systems via flow-based modeling,the adaptability to both compressible and nearly incompressible materials,and the compatibility with diverse mesh types,including triangular,quadrilateral,and polygonal elements.The numerical examples demonstrate,for the first time,optimized topologies for nearly incompressible materials under stress constraints in coupled mechanical-pressure environments,emphasizing the unique strength of this approach. 展开更多
关键词 topology optimization(TO) stress-related problem design-dependent load near incompressibility mechanical-pressure system polytopal composite finite element
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OptimizationDesign and Numerical Simulation of Variable Tube Diameter Heat Exchanger for Split Air Conditioning Indoor Unit 认领 引用 被引量：1
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作者 Zheming Cheng Xinping Ou Yang +2 位作者 Leren Tao Zihao Wang Ke Sun 《Frontiers in Heat and Mass Transfer》 EI CAS 2026年第1期288-313,共26页
Energy shortage has become one of themost concerning issues in the world today,and improving energy utilization efficiency is a key area of research for experts and scholars worldwide.Small-diameter heat exchangers of... Energy shortage has become one of themost concerning issues in the world today,and improving energy utilization efficiency is a key area of research for experts and scholars worldwide.Small-diameter heat exchangers offer advantages such as reduced material usage,lower refrigerant charge,and compact structure.However,they also face challenges,including increased refrigerant pressure drop and smaller heat transfer area inside the tubes.This paper combines the advantages and disadvantages of both small and large-diameter tubes and proposes a combined-diameter heat exchanger,consisting of large and small diameters,for use in the indoor units of split-type air conditioners.There are relatively few studies in this area.In this paper,A theoretical and numerical computation method is employed to establish a theoretical-numerical calculation model,and its reliability is verified through experiments.Using this model,the optimal combined diameters and flow path design for a combined-diameter heat exchanger using R32 as the working fluid are derived.The results show that the heat transfer performance of all combined diameter configurations improves by 2.79%to 8.26%compared to the baseline design,with the coefficient of performance(COP)increasing from 4.15 to 4.27~4.5.These designs can save copper material,but at the cost of an increase in pressure drop by 66.86%to 131.84%.The scheme IIIH,using R32,is the optimal combined-diameter and flow path configuration that balances both heat transfer performance and economic cost. 展开更多
关键词 Split air condition variable tube diameter enhanced heat transfer numerical simulation structural design optimization
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An Efficient Evolutionary Algorithm for Few-for-Many Optimization 认领 引用
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作者 Ke Shang Hisao Ishibuchi +1 位作者 Zexuan Zhu Qingfu Zhang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1362-1377,共16页
Few-for-many(F4M)optimization,recently introduced as a novel paradigm in multi-objective optimization,aims to find a small set of solutions that effectively handle a large number of conflicting objectives.Unlike tradi... Few-for-many(F4M)optimization,recently introduced as a novel paradigm in multi-objective optimization,aims to find a small set of solutions that effectively handle a large number of conflicting objectives.Unlike traditional many-objective optimization methods,which typically attempt comprehensive coverage of the Pareto front,F4M optimization emphasizes finding a small representative solution set to efficiently address highdimensional objective spaces.Motivated by the computational complexity and practical relevance of F4M optimization,this paper proposes a new evolutionary algorithm explicitly tailored for efficiently solving F4M optimization problems.Inspired by Smetric selection evolutionary multi-objective optimization algorithm(SMS-EMOA),our proposed approach employs a(μ+1)-evolution strategy guided by the objective of F4M optimization.Furthermore,to facilitate rigorous performance assessment,we propose a novel benchmark test suite specifically designed for F4M optimization by leveraging the similarity betw een the R2indicator and F4M formulations.Our test suite is highly flexible,allowing any existing multi-objective optimization problem to be transformed into a corresponding F4M instance via scalarization using the weighted Tchebycheff function.Comprehensive experimental evaluations on benchmarks demonstrate the superior performance of our algorithm compared to existing state-of-the-art algorithms,especially on instances involving a large number of objectives.The source code of the proposed algorithm will be released publicly.Source code is available at http://gffzz188fe103f8f1460asvnffc6xff0bp65wk.ffgz.tsg.suse.edu.cn/MOL-SZU/SoM-EMOA. 展开更多
关键词 Evolutionary algorithm few-for-many optimization many-objective optimization (MOO) multi-objective optimization
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Integrated topology optimization method for crashworthiness of metal-FRP hybrid thin-walled tubes:A review and analysis 认领 引用
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作者 Lele Zhang Yanzhao Guo +2 位作者 Zhizhong Cheng Weiyuan Dou Sebastian Stichel 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第1期508-525,共18页
Based on the demands for crashworthiness and lightweight in the passive safety of transportation vehicles,metal-fiber reinforced polymer(FRP)hybrid thin-walled tubes(MFHTWTs)integrate the toughness,strength and lightw... Based on the demands for crashworthiness and lightweight in the passive safety of transportation vehicles,metal-fiber reinforced polymer(FRP)hybrid thin-walled tubes(MFHTWTs)integrate the toughness,strength and lightweight of two distinct material characteristics.MFHTWTs can achieve energy absorption through the coupling of material plastic deformation and fracture,demonstrating significant engineering value in passive safety.This review provides a comprehensive examination of the crashworthiness topology optimization of MFHTWTs,aiming to demonstrate that a deeply integrated approach combining topology and parameter opti-mization can realize an optimal design method for MFHTWTs,thereby maximizing the functional utilization of limited material.Firstly,the review highlights the crashworthiness topology optimization methods(CTOMs)based on thin-walled structures.With a particular focus on metal,the review discusses both the practical ap-plicability and limitations of CTOMs under crash conditions.Additionally,based on the methodology of the equivalent static load method(ESLM),the review emphasizes that topology optimization methods considering continuous fiber paths and multi-material interface connections are also applicable to the crashworthiness op-timization of MFHTWTs.Furthermore,to couple structural parameters and configuration characteristics,in-tegrated topology optimization methods,including parameter optimization,are proposed to provide a valuable reference for the global optimization of MFHTWTs.Thus,these methods can establish the mapping relationship between key parameters and the structural energy absorption capacity. 展开更多
关键词 Metal-FRP hybrid thin-walled tube Topology optimization Parameter optimization Crashworthiness Integrated optimization scheme
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A satellite layout-structure integrated optimization method based on thermal metamaterials 认领 引用
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作者 Senlin HUO Bingxiao DU +2 位作者 Wei CONG Yong ZHAO Xianqi CHEN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第2期328-340,共13页
In the conceptual design phase of the satellite thermal management system,components layout optimization and structural topology optimization of satellite panel can meet global and local thermal management requirement... In the conceptual design phase of the satellite thermal management system,components layout optimization and structural topology optimization of satellite panel can meet global and local thermal management requirements,respectively.However,achieving non-interfering coupling between these two optimization processes remains a challenge.An integrated layout-structure design method based on thermal metamaterials is proposed,which comprises two design stages.In the first stage,components layout optimization is conducted to maximize temperature uniformity within the satellite module,yielding a globally optimized layout with balanced thermal characteristics.In the second stage,topology optimization guided by the design principle of thermal metamaterials is implemented in critical local panel regions to satisfy differentiated heat transfer requirements of components with diverse functional and thermal sensitivity properties.The key innovation lies in utilizing thermal metamaterials as a mediator to synergistically couple global components layout optimization with local structural topology optimization,which enables customized local heat flux manipulation without interfering with the globally optimized temperature field derived from the layout optimization.The method introduces neither additional mass nor special materials,offering advantages of low cost,high reliability,and strong versatility.It provides a new solution paradigm for the design of passive thermal management systems in satellites. 展开更多
关键词 Layout optimization Metamaterials Satellites Structure design Thermal management Topology optimization
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PSO算法优化下工业机器人抓取位姿一体化控制 认领 引用
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作者 杨利红 蒋亚静 《机械设计与制造》 北大核心 2026年第7期303-307,共5页
针对目标物体位置微小变化导致的机器人抓取精度下降问题,提出PSO算法优化下工业机器人抓取位姿一体化控制方法。通过误差雅可比矩阵量化末端执行器的横向、纵向及角度偏差,结合逆运动学构建效用函数确定目标位姿矩阵。采用PSO算法对多... 针对目标物体位置微小变化导致的机器人抓取精度下降问题,提出PSO算法优化下工业机器人抓取位姿一体化控制方法。通过误差雅可比矩阵量化末端执行器的横向、纵向及角度偏差,结合逆运动学构建效用函数确定目标位姿矩阵。采用PSO算法对多目标参数进行全局优化,在关节角度约束下通过线性加权平衡位姿误差与运行稳定性,经粒子群迭代寻优输出最优控制参数,实现位姿动态补偿与高精度抓取。实验结果表明,所提方法控制后的抓取轨迹曲线与期望曲线基本保持一致,满足高精度制造需求。 展开更多
关键词 PSO算法优化 工业机器人 抓取位姿一体化控制 粒子群算法 最优粒子
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Optimization of the frequency offset increment of FDA-MIMO based on cuckoo search algorithm 认领 引用 被引量：1
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作者 WANG Bo ZHAO Yu +2 位作者 LI Yonglin YANG Rennong XUE Junjie 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第1期157-170,共14页
Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic e... Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic environments.The effectiveness of interference suppression by FDA-MIMO is limited by the inherent range-angle coupling issue in the FDA beampattern.Existing literature primarily focuses on control methods for FDA-MIMO radar beam direction under the assumption of static beampatterns,with insufficient exploration of techniques for managing nonstationary beam directions.To address this gap,this paper initially introduces the FDA-MIMO signal model and the calculation formula for the FDA-MIMO array output using the minimum variance distortionless response(MVDR)beamformer.Building on this,the problem of determining the optimal frequency offset for the FDA is rephrased as a convex optimization problem,which is then resolved using the cuckoo search(CS)algorithm.Simulations confirm the effectiveness of the proposed approach,showing that the frequency offsets obtained through the CS algorithm can create a dot-shaped beam direction at the target location while effectively suppressing interference signals within the mainlobe. 展开更多
关键词 frequency diverse array multiple-input multiple-output(FDA-MIMO) convex optimization cuckoo search algorithm beampattern
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Optimal abort guidance and online trajectory optimization algorithm for Mars vehicles 认领 引用
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作者 Yuan LI Huaiyi WANG +3 位作者 Tuo HAN Qinglei HU Yueyang LIU Dongyu LI 《Science China(Technological Sciences)》 SCIE EI CAS CSCD 2026年第7期263-277,共15页
When the Mars vehicles encounter a storm or other failure situations,the ability to abort and rise to a predetermined orbit in the powered descent flight phase is critical.Unlike the abort guidance mission of lunar ex... When the Mars vehicles encounter a storm or other failure situations,the ability to abort and rise to a predetermined orbit in the powered descent flight phase is critical.Unlike the abort guidance mission of lunar exploration,Mars has an atmosphere and stronger gravity,leading to high requirements on the efficiency,autonomy,reliability,and robustness of the guidance system.To this end,this paper proposes a safety abort guidance algorithm to stop the descent of the Mars vehicle with fast responses in the initial stage of abort.Then,to meet the requirements of rendezvous and docking with the orbiter,the constraints of terminal orbit parameters and orbit insertion time and position in the perifocal coordinate system are explicitly defined.To achieve a higher terminal orbit,a“powered-coast-powered”trajectory onboard optimization problem is established,which allows the trajectory optimization problem to be handled by convex optimization rapidly and accurately.Moreover,a switching strategy between the online trajectory optimization and terminal iterative guidance is detailed to ensure the accuracy of terminal orbit insertion.Finally,numerical experiments with Monte Carlo tests under various conditions are conducted to verify the efficiency,robustness,and onboard application performance of the proposed method. 展开更多
关键词 abort guidance trajectory optimization convex optimization Mars vehicles
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Review of Metaheuristic Optimization Techniques for Enhancing E-Health Applications 认领 引用
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作者 Qun Song Chao Gao +3 位作者 Han Wu Zhiheng Rao Huafeng Qin Simon Fong 《Computers, Materials & Continua》 SCIE EI 2026年第2期185-233,共49页
Metaheuristic algorithms,renowned for strong global search capabilities,are effective tools for solving complex optimization problems and show substantial potential in e-Health applications.This review provides a syst... Metaheuristic algorithms,renowned for strong global search capabilities,are effective tools for solving complex optimization problems and show substantial potential in e-Health applications.This review provides a systematic overview of recent advancements in metaheuristic algorithms and highlights their applications in e-Health.We selected representative algorithms published between 2019 and 2024,and quantified their influence using an entropy-weighted method based on journal impact factors and citation counts.CThe Harris Hawks Optimizer(HHO)demonstrated the highest early citation impact.The study also examined applications in disease prediction models,clinical decision support,and intelligent health monitoring.Notably,the Chaotic Salp Swarm Algorithm(CSSA)achieved 99.69% accuracy in detecting Novel Coronavirus Pneumonia.Future research should progress in three directions:improving theoretical reliability and performance predictability in medical contexts;designing more adaptive and deployable mechanisms for real-world systems;and integrating ethical,privacy,and technological considerations to enable precision medicine,digital twins,and intelligent medical devices. 展开更多
关键词 Metaheuristic optimization E-Health disease diagnosis medical resource optimization complex optimization
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Deep Reinforcement Learning Based on Search Space Independent Operators for Black-Box Continuous Optimization 认领 引用
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作者 Ye Tian Yisai Liu +1 位作者 Shangshang Yang Xingyi Zhang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第4期913-925,共13页
Deep reinforcement learning(DRL)has demonstrated exceptional capabilities in combinatorial optimization,which automatically devises policies for solution construction and optimizer refinement.DRL is particularly adept... Deep reinforcement learning(DRL)has demonstrated exceptional capabilities in combinatorial optimization,which automatically devises policies for solution construction and optimizer refinement.DRL is particularly adept in generating training samples by itself,thereby providing the flexibility to solve a variety of combinatorial optimization problems without supervision.While DRL takes actions according to states extracted from problem-specific information,it cannot be directly applied to black-box continuous optimization lacking explicit information.To address this issue,this paper proposes a search space independent operator based DRL method for black-box continuous optimization.It conceptualizes the optimization process driven by search space independent operators as a Markov decision process,wherein actions are defined as operators and states are extracted from solutions generated by operators.In contrast to other DRLassisted metaheuristics,the proposed method does not rely on any existing metaheuristic.Instead,it innovates by creating totally new operators,able to surpass the performance boundaries of existing metaheuristics.Compared with state-of-the-art metaheuristics and DRL methods,the proposed method shows significantly faster convergence speed on challenging continuous optimization problems. 展开更多
关键词 Black-box optimization continuous optimization metaheuristic reinforcement learning search operator
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