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Improved Guide-Weight method for multi-material topology optimization under inertial loads based on the alternating active-phase algorithm 认领 引用
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作者 Zihao Meng Yiru Ren 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2025年第8期138-148,共11页
The application of multi-material topology optimization affords greater design flexibility compared to traditional single-material methods.However,density-based topology optimization methods encounter three unique cha... The application of multi-material topology optimization affords greater design flexibility compared to traditional single-material methods.However,density-based topology optimization methods encounter three unique challenges when inertial loads become dominant:non-monotonous behavior of the objective function,possible unconstrained characterization of the optimal solution,and parasitic effects.Herein,an improved Guide-Weight approach is introduced,which effectively addresses the structural topology optimization problem when subjected to inertial loads.Smooth and fast convergence of the compliance is achieved by the approach,while also maintaining the effectiveness of the volume constraints.The rational approximation of material properties model and smooth design are utilized to guarantee clear boundaries of the final structure,facilitating its seamless integration into manufacturing processes.The framework provided by the alternating active-phase algorithm is employed to decompose the multi-material topological problem under inertial loading into a set of sub-problems.The optimization of multi-material under inertial loads is accomplished through the effective resolution of these sub-problems using the improved Guide-Weight method.The effectiveness of the proposed approach is demonstrated through numerical examples involving two-phase and multi-phase materials. 展开更多
关键词 Topology optimization Improved Guide-Weight method Alternating active-phase algorithm Inertial loads Multi-material
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Progressive quantum algorithm for maximum independent set with quantum alternating operator ansatz 认领 引用
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作者 Xiao-Hui Ni Ling-Xiao Li +3 位作者 Yan-Qi Song Zheng-Ping Jin Su-Juan Qin Fei Gao 《Chinese Physics B》 SCIE EI CAS CSCD 2025年第7期75-87,共13页
The quantum alternating operator ansatz algorithm(QAOA+)is widely used for constrained combinatorial optimization problems(CCOPs)due to its ability to construct feasible solution spaces.In this paper,we propose a prog... The quantum alternating operator ansatz algorithm(QAOA+)is widely used for constrained combinatorial optimization problems(CCOPs)due to its ability to construct feasible solution spaces.In this paper,we propose a progressive quantum algorithm(PQA)to reduce qubit requirements for QAOA+in solving the maximum independent set(MIS)problem.PQA iteratively constructs a subgraph likely to include the MIS solution of the original graph and solves the problem on it to approximate the global solution.Specifically,PQA starts with a small-scale subgraph and progressively expands its graph size utilizing heuristic expansion strategies.After each expansion,PQA solves the MIS problem on the newly generated subgraph using QAOA+.In each run,PQA repeats the expansion and solving process until a predefined stopping condition is reached.Simulation results show that PQA achieves an approximation ratio of 0.95 using only 5.57%(2.17%)of the qubits and 17.59%(6.43%)of the runtime compared with directly solving the original problem with QAOA+on Erd?s-Rényi(3-regular)graphs,highlighting the efficiency and scalability of PQA. 展开更多
关键词 quantum alternating operator ansatz algorithm(QAOA+) constrained combinatorial optimization problems(CCOPs) maximum independent set(MIS) feasible space
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A Control-based Transition Reinforced Optimization Process for Multi-level Threshold Image Segmentation 认领 引用
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作者 Wei Wang Peiying Zhang +5 位作者 Saleh Ali Alomari Raed Abu Zitar Aseel Smerat Mohamed Sharaf Absalom E.Ezugwu Laith Abualigah 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第2期1061-1087,共27页
In this study,we present a novel approach to multi-threshold image segmentation using an adaptive method that combines the Ebola Optimization Search Algorithm(EOSA)with the Aquila Optimizer,termed the Integrated Enhan... In this study,we present a novel approach to multi-threshold image segmentation using an adaptive method that combines the Ebola Optimization Search Algorithm(EOSA)with the Aquila Optimizer,termed the Integrated Enhanced Ebola Optimization Search Algorithm(IEOSA).Our approach leverages this integration to produce high-quality segmented images.The IEOSA method introduces two distinct optimization mechanisms to identify optimal solutions.By blending the randomness of the Aquila Optimizer with the capabilities of EOSA,we enhance the exploration potential of the algorithm.Additionally,we incorporate a self-transition learning system within the IEOSA to further boost its performance.To tackle multi-level threshold image segmentation,we apply Kapur’s entropy between-class variance within the IEOSA framework.Our findings show that the IEOSA-based techniques outperform other comparable methods,offering faster convergence and more stable segmentation results.Through comparative analysis using standard test images,we demonstrate that IEOSA achieves higher solution accuracy than other methods.Ultimately,the proposed IEOSA methodologies effectively address multi-level threshold image segmentation challenges,accurately segmenting even the minor errors that are often overlooked in high-resolution images. 展开更多
关键词 Ebola optimization search algorithm(EOSA) Aquila optimizer(AO) Multi-level threshold Image segmentation Transition mechanism
Binary Archimedes Optimization Algorithm for Computing Dominant Metric Dimension Problem 认领 引用
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作者 Basma Mohamed Linda Mohaisen Mohammed Amin 《Intelligent Automation & Soft Computing》 SCIE 2023年第10期19-34,共16页
In this paper,we consider the NP-hard problem of finding the minimum dominant resolving set of graphs.A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of dista... In this paper,we consider the NP-hard problem of finding the minimum dominant resolving set of graphs.A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of distances to the vertices in B.A resolving set is dominating if every vertex of G that does not belong to B is a neighbor to some vertices in B.The dominant metric dimension of G is the cardinality number of the minimum dominant resolving set.The dominant metric dimension is computed by a binary version of the Archimedes optimization algorithm(BAOA).The objects of BAOA are binary encoded and used to represent which one of the vertices of the graph belongs to the dominant resolving set.The feasibility is enforced by repairing objects such that an additional vertex generated from vertices of G is added to B and this repairing process is iterated until B becomes the dominant resolving set.This is the first attempt to determine the dominant metric dimension problem heuristically.The proposed BAOA is compared to binary whale optimization(BWOA)and binary particle optimization(BPSO)algorithms.Computational results confirm the superiority of the BAOA for computing the dominant metric dimension. 展开更多
关键词 Dominant metric dimension archimedes optimization algorithm binary optimization alternate snake graphs
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Distributed Alternating Direction Method of Multipliers for Multi-Objective Optimization 认领 引用 被引量：1
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作者 Hui Deng Yangdong Xu 《Advances in Pure Mathematics》 2022年第4期249-259,共11页
In this paper, a distributed algorithm is proposed to solve a kind of multi-objective optimization problem based on the alternating direction method of multipliers. Compared with the centralized algorithms, this algor... In this paper, a distributed algorithm is proposed to solve a kind of multi-objective optimization problem based on the alternating direction method of multipliers. Compared with the centralized algorithms, this algorithm does not need a central node. Therefore, it has the characteristics of low communication burden and high privacy. In addition, numerical experiments are provided to validate the effectiveness of the proposed algorithm. 展开更多
关键词 Alternating Direction Method of Multipliers Distributed Algorithm Multi-Objective Optimization Multi-Agent System
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Sum-rate optimization methods and analysis for reconfigurable intelligent surface aided communication system 认领 引用
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作者 Jinfeng Li Xiaorong Zhu 《Digital Communications and Networks》 SCIE EI CSCD 2025年第5期1421-1435,共15页
When deploying Reconfigurable Intelligent Surface(RIS)to improve System Sum-Rate(SSR),the timeliness and accuracy of SSR optimization methods are difficult to achieve simultaneously through a single algorithm.Some alg... When deploying Reconfigurable Intelligent Surface(RIS)to improve System Sum-Rate(SSR),the timeliness and accuracy of SSR optimization methods are difficult to achieve simultaneously through a single algorithm.Some algorithms focus on timeliness,while some focus on accuracy.In this paper,in order to take into account the timeliness and accuracy of the system comprehensively,we construct SSR analysis model of RIS-assisted multiuser downlink communication system and propose several new optimization methods.The goal is to maximize SSR by using the proposed algorithms to jointly optimize power allocation and reflection coefficients.To solve this comprehensive problem,two sets of Alternating Optimization(AO)-based timeliness algorithms and one set of Monotonic Optimization(MO)-based accuracy algorithms are proposed separately to jointly optimize system performance.First,the Water-Filling(WF)-based and penalty-based low complexity algorithms are developed to optimize power allocation and reflection coefficients respectively.To improve the reality of the calculation,penalty-based algorithm cleverly considers residual noise that is difficult to calculate.Then,for further improve the timeliness,a new Successive Convex Approximation(SCA)-based low complexity algorithm is designed to further optimize reflection coefficients and its convergence is proved.Third,in order to verify the effectiveness of the proposed timeliness algorithms,we further propose MO-based accuracy algorithms,in which,the Polyblock Outer Approximation(POA)algorithm,the Semidefinite Relaxation(SDR)method,and the bisection search algorithm are combined in a novel way.Numerical results confirm the timeliness of AO-based algorithms and the accuracy of MO-based algorithms.They supervise and complement each other. 展开更多
关键词 Reconfigurable intelligent surface Timeliness Accuracy Alternating optimization algorithm Polyblock outer approximation algorithm
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An Alternating Gradient Projection Algorithm with Momentum for Nonconvex–Concave Minimax Problems 认领 引用
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作者 Jue-You Li Tao Xie 《Journal of the Operations Research Society of China》 EI CSCD 2026年第2期632-652,共21页
The growing interest in addressing minimax optimization problem has been fueled by recent applications in machine learning.Although extensively studied in the convex–concave regime,where a global solution can be effi... The growing interest in addressing minimax optimization problem has been fueled by recent applications in machine learning.Although extensively studied in the convex–concave regime,where a global solution can be efficiently computed,this paper delves into the minimax problem within the nonconvex–concave setup.We propose an alternating gradient projection algorithm with momentum(M-AGP),belonging to single-loop algorithms that not only are easier to implement but also require only the computation of gradient projection updates.We demonstrate that the proposed algorithm identifies an-stationary point of the nonconvex–strongly concave minimax problem in O(ε-2)iterations,representing the best-known rate in the literature.Finally,we utilize two test problems,namely robust nonlinear regression and an image classification problem,to showcase the efficacy of the proposed algorithm. 展开更多
关键词 Minimax optimization Alternating gradient projection algorithm Gradient descent–ascent algorithm Iteration complexity
An Alternating Proximal Gradient Algorithm for Nonsmooth Nonconvex-Linear Minimax Problems with Coupled Linear Constraints 认领 引用
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作者 Hui-Ling Zhang Zi Xu 《Journal of the Operations Research Society of China》 EI CSCD 2026年第2期502-520,共19页
In this paper,we propose an alternating proximal gradient algorithm for solving nonsmooth nonconvex-linear minimax problems with coupled linear constraints,which have attracted wide attention in machine learning,signa... In this paper,we propose an alternating proximal gradient algorithm for solving nonsmooth nonconvex-linear minimax problems with coupled linear constraints,which have attracted wide attention in machine learning,signal processing and many other fields in recent years.The iteration complexity of the proposed algorithm is proved to be O(ε-3)to reach anε-stationary point.To our knowledge,this is the first algorithm with iteration complexity guarantee for solving nonsmooth nonconvex-linear minimax problems with coupled linear constraints. 展开更多
关键词 Minimax optimization problem Alternating proximal gradient algorithm Iteration complexity Machine learning
Multistrategy Improved Aquila Optimizer for Test Case Prioritization 认领 引用
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作者 Jiali Chen Jiheng Zhang +3 位作者 Xiaojie Chen Chong Zeng Honghui Yi Heming Jia 《Computers, Materials & Continua》 SCIE EI 2026年第8期2328-2362,共35页
Traditional heuristic algorithms often fall into local optima and converge slowly when test case prioritization is addressed in regression testing,making them inadequate for complex real-world scenarios.The Aquila opt... Traditional heuristic algorithms often fall into local optima and converge slowly when test case prioritization is addressed in regression testing,making them inadequate for complex real-world scenarios.The Aquila optimizer,a novel metaheuristic algorithm,demonstrates strong global exploration capability but still faces limitations,including insufficient exploitation capability and slow convergence.To overcome these challenges,a multi-strategy improved chaotic Cauchy inverse cumulative distribution Aquila optimizer for test case prioritization is proposed.First,a logistic–sine–cosine composite chaotic mapping is introduced during the initialization phase of the Aquila optimizer to increase population diversity.Second,the mutated random walk strategy is used to improve global exploration,further enhancing the global search ability of the Aquila optimizer.Moreover,during the narrowed exploration and narrowed exploitation phases,the Cauchy inverse cumulative distribution flight replaces the Lévy flight strategy to reallocate individual positions,strengthening individuals’optimization capability and preventing the algorithm from becoming trapped in local optima.Finally,in the later iteration stage,the specular reflection learning strategy is used to perturb the optimal individual positions and improve the Aquila optimizer’s convergence accuracy and comprehensive optimization performance.Five Java projects were selected from the Defects4J benchmark datasets to conduct comparative experiments with the Aquila optimizer and seven other metaheuristic algorithms.The results demonstrate the effectiveness and superiority of the improved algorithm in test case prioritization.It achieves average improvements of approximately 4.96%in the average percentage of fault detection,3.82%in the average percentage of block coverage,and 5.64%in the average percentage of decision coverage,enabling faster coverage of code blocks and branches.The results provide an efficient priority sorting solution for complex regression testing scenarios. 展开更多
关键词 Heuristic algorithm search-based software engineering(SBSE) Aquila optimizer(AO) test case prioritization(TCP) average percentage of fault detection(APFD) average percentage of block coverage(APBC) average percentage of decision coverage(APDC)
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可移动天线赋能的无线供电混合比特语义通信网络资源优化方案 认领 引用
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作者 吴巨爱 谢家豪 吕斌 《数据采集与处理》 CSCD 北大核心 2026年第3期896-908,共13页
针对混合比特语义通信网络(Heterogeneous bit and semantic communication network,HBSCN)存在的能量供应受限和传输性能不足问题,本文构建了无线供电混合比特语义通信网络(Wireless powered HBSCN,WP-HBSCN),并提出了可移动天线(Movab... 针对混合比特语义通信网络(Heterogeneous bit and semantic communication network,HBSCN)存在的能量供应受限和传输性能不足问题,本文构建了无线供电混合比特语义通信网络(Wireless powered HBSCN,WP-HBSCN),并提出了可移动天线(Movable antennas,MAs)赋能的高效传输方案。在该方案中,混合接入点(Hybrid access point,HAP)首先向所有用户发送射频信号以实现远程能量供应,然后比特用户和语义用户分别利用收集的能量以时分多址方式向HAP传输比特信息和语义信息。通过在HAP中部署MAs并调整其位置来构建良好的信道条件,实现下行能量传输效率和上行信息传输效率的提升。在保证语义用户的服务质量(Quality of service,QoS)约束的前提下,定义了总比特信息量最大化问题。为了处理该问题的非凸性,设计了基于连续凸近似(Successive convex approximation,SCA)方法和粒子群优化(Particle swarm optimization,PSO)算法的交替优化算法。仿真结果表明,相较于参考方案,所提出的方案最多可以将系统的总比特信息量提升100%。 展开更多
关键词 可移动天线 无线供电通信 混合比特语义通信 交替优化算法 粒子群优化
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基于量子近似优化算法的配电网故障恢复重构 认领 引用
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作者 王宝楠 张安邦 +2 位作者 张伟娜 薛梅 张丹 《科学技术与工程》 EI 北大核心 2026年第18期7720-7729,共10页
配电网故障恢复重构是保障电力系统安全与稳定运行的重要任务。传统的优化算法在解决这一问题时,往往面临求解精度与计算效率之间的权衡。为此,提出了一种结合量子近似优化算法和交替方向乘子法的混合算法,旨在通过量子计算与经典优化... 配电网故障恢复重构是保障电力系统安全与稳定运行的重要任务。传统的优化算法在解决这一问题时,往往面临求解精度与计算效率之间的权衡。为此,提出了一种结合量子近似优化算法和交替方向乘子法的混合算法,旨在通过量子计算与经典优化技术的协同作用,提升配电网故障恢复重构的效率与精度。量子近似优化算法利用量子计算的优势,在全局优化过程中加速解的搜索,而交替方向乘子法则有效地处理大规模约束优化问题。将本文算法分别应用于33节点和69节点配电网中,在两个场景下分别使总成本下降了40.39%和50.82%,并且与其他传统算法进行对比,证明了本文算法具有良好的准确率和全局寻优能力,为配电网故障恢复重构问题提供了新的解决思路。 展开更多
关键词 量子近似优化算法 配电网 交替方向乘子法 故障恢复重构
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不完美信道下STAR-RIS联合协作干扰辅助的鲁棒安全通信 认领 引用
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作者 李安 王智伟 《湖南大学学报（自然科学版）》 EI CAS CSCD 北大核心 2026年第4期41-51,共11页
针对窃听者的被动特性导致的信道状态信息(channel state information,CSI)不完美问题,引入外部友好干扰节点降级窃听信道质量,提出了一种可同时传输和反射智能超表面(simultaneously transmitting and reflecting reconfigurable intel... 针对窃听者的被动特性导致的信道状态信息(channel state information,CSI)不完美问题,引入外部友好干扰节点降级窃听信道质量,提出了一种可同时传输和反射智能超表面(simultaneously transmitting and reflecting reconfigurable intelligent surface,STAR-RIS)联合协作干扰辅助的双侧单用户多窃听下行通信系统的鲁棒物理层安全优化方法.以系统最坏情况下最大化系统的和保密速率(sum secrecy rate,SSR)为优化目标,联合优化基站/干扰节点发射波束成形向量和STAR-RIS传输/反射系数矩阵.由于原始优化问题为多个变量相互耦合的非凸优化问题,提出一种联合交替优化(alternative optimization,AO)、半正定松弛(semidefinite relaxation,SDR)和S-procedure的高效迭代算法,求解得到原问题的次优解.仿真结果表明,与基准方案相比,所提的鲁棒方案能显著提升系统的保密性能. 展开更多
关键词 安全通信 干扰 可同时传输和反射智能超表面 交替优化 鲁棒方案 和保密速率
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考虑分层需求的干支通多层航空运输网络优化模型 认领 引用
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作者 姜雨 刘洪阳 +2 位作者 李焓璐 原野 薛清文 《交通运输工程学报》 EI CAS CSCD 北大核心 2026年第2期140-154,共15页
以干支通多层航空运输网络为研究对象,进行符合其运行的建模,以构建分层需求多层级枢纽选址模型并求解为目标;通过需求层区分不同类别的需求,以包括运输成本、枢纽建设固定成本、航线连接固定成本在内的总成本最小为目标,构建了允许非... 以干支通多层航空运输网络为研究对象,进行符合其运行的建模,以构建分层需求多层级枢纽选址模型并求解为目标;通过需求层区分不同类别的需求,以包括运输成本、枢纽建设固定成本、航线连接固定成本在内的总成本最小为目标,构建了允许非枢纽直接连接和普通枢纽直接连接的r分配多层级枢纽选址模型;根据航空网络的拓扑结构特点,结合变邻域搜索(VNS)算法和遗传算法(GA)的优势,设计了基于交替机制的VNS-GA混合启发式算法,通过变邻域搜索优化枢纽选择和需求点分配,利用遗传算法优化直接连接关系;针对CAB、AP两个经典数据集和中国长三角区域机场数据进行建模求解,对比现有模型和分层需求模型,验证了算法有效性,并分析了参数灵敏度。研究结果表明:在15个点的小规模案例中,分层需求模型降低了9.23%的总成本;在25个点的小规模案例中,交替式VNS-GA算法在多种参数配置下与最优解差距均不超过2.56%,且平均求解时间仅为商业求解软件的10.78%;在100个点的中大规模案例中,灵敏度分析表明,分层权重系数的设置对优化结果影响最大,r分配策略能够降低总成本但存在明显的边际效益递减;在长三角区域半实例试验中,模型能够在增加50条直连航线的同时实现成本降低2.75%,验证了模型在干支通多层航空运输网络优化的可行性和效果。 展开更多
关键词 航空运输 干支通多层航空运输网络 分层需求 多层级枢纽选址模型 交替式VNS-GA混合启发式算法 基于成本的网络优化 多规模试验 长三角区域
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基于DPSO的改进AO^*算法在大型复杂电子系统最优序贯测试中的应用 认领 引用 被引量：19
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作者 蒋荣华 王厚军 龙兵 《计算机学报》 EI CAS 北大核心 2008年第10期1835-1840,共6页
针对大型复杂电子系统最优序贯测试问题,提出一种基于离散粒子群算法（DPSO）和改进AO^＊算法相结合的方法.DPSO优化AO^＊算法中每个要扩展节点的测试集从而减少测试个数;改进AO^＊算法通过规定扩展节点估价值的范围,减少其回溯次数.实... 针对大型复杂电子系统最优序贯测试问题,提出一种基于离散粒子群算法（DPSO）和改进AO^＊算法相结合的方法.DPSO优化AO^＊算法中每个要扩展节点的测试集从而减少测试个数;改进AO^＊算法通过规定扩展节点估价值的范围,减少其回溯次数.实例验证表明,该算法不仅有效地降低了计算复杂度,大大减少测试代价,缩短测试时间,而且避免了原有AO^＊算法当备选的测试集太大时容易出现“计算爆炸”的缺点. 展开更多
关键词 离散粒子群算法 AO^＊算法 序贯测试 哈夫曼编码 可测性设计
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Transfer of Improvement Strategies Between DRS and ADMM:A Unified Classification Framework 认领 引用
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作者 Shuting Liu 《Communications in Mathematical Research》 CSCD 2026年第2期121-135,共15页
Douglas-Rachford splitting(DRS)and the alternating direction method of multipliers(ADMM)are two fundamental first-order methods for structured convex optimization.Although derived from different viewpoints,ADMM can be... Douglas-Rachford splitting(DRS)and the alternating direction method of multipliers(ADMM)are two fundamental first-order methods for structured convex optimization.Although derived from different viewpoints,ADMM can be interpreted as the application of DRS to the dual problem.Based on this structural equivalence,this paper studies how algorithmic improvement strategies can be transferred between the two methods.We classify transferable strategies into three categories:exact operator-level transfer,parameterdriven transfer,and heuristic transfer.Representative examples including relaxation,metric scaling,adaptive parameter updates,and residual balancing are discussed to illustrate the different levels of transferability.This perspective provides a systematic way to understand the relationship between DRS and ADMM and clarifies how algorithmic ideas developed for one method may inform the design of variants of the other,offering a unified framework that both explains existing variants and guides the design of new ones. 展开更多
关键词 Douglas-Rachford splitting alternating direction method of multipliers monotone operator splitting convex optimization algorithmic equivalence relaxation techniques adaptive step sizes
防御作弊行为的多区域含氢综合能源系统三重博弈鲁棒优化 认领 引用
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作者 丁小强 袁至 李骥 《电力系统自动化》 EI CSCD 北大核心 2026年第8期128-138,共11页
多区域含氢综合能源系统(IES)的源荷不确定性、多重利益博弈及交易过程的作弊行为对其协作运行构成挑战。因此,文中提出了一种防御作弊行为的多区域含氢IES三重博弈鲁棒优化方法。首先,建立了含有光伏产消者的多区域含氢IES运行架构,将... 多区域含氢综合能源系统(IES)的源荷不确定性、多重利益博弈及交易过程的作弊行为对其协作运行构成挑战。因此,文中提出了一种防御作弊行为的多区域含氢IES三重博弈鲁棒优化方法。首先,建立了含有光伏产消者的多区域含氢IES运行架构,将分布鲁棒机会约束融入Wasserstein距离模糊集下的两阶段分布鲁棒优化模型中,充分考虑了光伏产消者与各IES源荷两侧的不确定性。然后,建立“主从+作弊+合作”的三重博弈架构,解决各含氢IES多重利益博弈及交易过程的作弊问题。最后,采用交替方向乘子法嵌套列与约束生成算法进行分布式求解,可以在防御作弊行为的同时保护主体隐私。经仿真验证,该策略在抵御作弊行为的同时,可以有效提升IES的运行效益以及应对不确定风险的能力。 展开更多
关键词 综合能源系统 光伏产消者 作弊 分布鲁棒优化 博弈 交替方向乘子法 列与约束生成算法
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RLDEAO优化的空气质量数据聚类分析 认领 引用 被引量：1
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作者 田闯 黄鹤 +2 位作者 杨澜 王会峰 茹锋 《浙江大学学报（理学版）》 CAS CSCD 北大核心 2024年第5期542-553,共12页
对空气质量数据进行聚类,传统聚类方法因受初始点的影响,存在随机性高、聚类精度低以及多个中心点出现在同一簇中的问题,为此提出了一种反向学习差分进化天鹰优化器(RLDEAO)优化的K-means互补迭代空气质量数据聚类方法。天鹰优化器(aqui... 对空气质量数据进行聚类,传统聚类方法因受初始点的影响,存在随机性高、聚类精度低以及多个中心点出现在同一簇中的问题,为此提出了一种反向学习差分进化天鹰优化器(RLDEAO)优化的K-means互补迭代空气质量数据聚类方法。天鹰优化器(aquila optimizer,AO)算法具有很强的探索能力,不易受初始点的影响且更易实现,但易陷入局部最优。基于自适应逐维小孔成像反向学习策略、停滞扰动结合莱维飞行策略以及生物进化策略等改进思想,对AO算法进行了改进,有效提高了搜索性能,避免了局部最优;在求取聚类中心点时,设计了一种加权最大最小距离积法(weighted maximum minimum distance product,WMMP),能反映各特征的重要性,对改进聚类结果作用良好;将RLDEAO与WMMP相结合优化K-means互补迭代,提高了搜索速率和搜索精度。通过在多个数据集上的聚类测试,发现RLDEAO-KMC算法的收敛精度和聚类效果较AO-KMC、FCM、KMC、KMC++算法更优。可知,RLDEAO-KMC算法可以更高效地对空气质量数据进行聚类分析,有针对性地做出预测和应对。 展开更多
关键词 K-means聚类算法 天鹰优化器(AO) 加权最大最小距离积法
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基于OFDM-ISAC的无人机-无人船协同巡检系统设计 认领 引用
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作者 朱峥 李海鹏 张瑞 《移动通信》 2026年第6期78-84,共7页
针对复杂海上环境中UAV(无人机)因能源受限难以长时间执行检查任务,提出一种基于ISAC技术的UAV-USV(无人机-无人船)协同海上巡检系统。为提升海上目标感知精度,构建了以最大化最小感知目标互信息为目标的公平性导向模型。针对该非凸优... 针对复杂海上环境中UAV(无人机)因能源受限难以长时间执行检查任务,提出一种基于ISAC技术的UAV-USV(无人机-无人船)协同海上巡检系统。为提升海上目标感知精度,构建了以最大化最小感知目标互信息为目标的公平性导向模型。针对该非凸优化问题,提出一种交替优化算法,将原问题拆解为无人机-无人船联合轨迹设计与OFDM子载波分配两个子问题,通过交替迭代求解以获得次优解。为了求解这两个子问题,利用SCA技术求解轨迹优化问题,并提出一种次优子信道分配算法完成OFDM子载波分配。仿真结果表明,面向公平性的方案提供了均衡的目标感知性能,保障了所有待巡检目标的最低性能。 展开更多
关键词 无人机 无人船 通信感知一体化 正交频分复用 交替优化算法
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基于凸聚类和AMA优化的雷达信号分选算法 认领 引用
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作者 范越 张永祥 方亚军 《现代防御技术》 北大核心 2026年第2期147-154,共8页
针对现有雷达信号分选算法依赖人工预设聚类数目且对输入初始值敏感等问题,提出一种基于凸聚类的雷达信号分选算法。凸聚类算法是一种基于目标函数的聚类分析方法,通过优化凸目标函数可以保证雷达信号分选任务的全局最优解,且聚类效果... 针对现有雷达信号分选算法依赖人工预设聚类数目且对输入初始值敏感等问题,提出一种基于凸聚类的雷达信号分选算法。凸聚类算法是一种基于目标函数的聚类分析方法,通过优化凸目标函数可以保证雷达信号分选任务的全局最优解,且聚类效果不受雷达信号输入顺序的影响。对截获接收机输出的脉冲描述字进行标准化处理后,构造凸优化目标函数,并使用交替最小化方法(alternating minimization algorithm, AMA)进行优化求解,得到最终的分选结果。仿真实验表明,基于凸聚类的雷达信号分选算法可以获得较高的分选准确率,同时具有较好的稳定性和噪声鲁棒性。 展开更多
关键词 电子对抗 信号分选 凸聚类 脉冲描述字 AMA优化算法
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RIS辅助安全无人机通信的联合波束成型与用户调度设计 认领 引用
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作者 冯世尧 周颢晖 《无线通信技术》 2026年第2期52-57,共6页
近年来,无人机参与通信引起了广泛的研究。但是仍面临诸多挑战,一方面由于地空视距(Line-of-Sight,LOS)信道的存在,无人机通信容易受到窃听:另一方面随着通信用户需求的增长,有限资源的合理利用显得尤为重要。针对该挑战,本文提出了一... 近年来,无人机参与通信引起了广泛的研究。但是仍面临诸多挑战,一方面由于地空视距(Line-of-Sight,LOS)信道的存在,无人机通信容易受到窃听:另一方面随着通信用户需求的增长,有限资源的合理利用显得尤为重要。针对该挑战,本文提出了一个存在窃听者的情况下,可重构智能表面(reconfigurable intelligent surface,RIS)辅助的无人机网络的安全传输设计,通过联合优化无人机主动波束成型、RIS被动波束成型和用户调度,在最大限度提高平均保密率的同时,保障每个用户的服务质量(Quality of Service,QoS)。然而,这会导致一个困难的非凸混合整数优化问题。针对这一问题,提出了一种基于交替优化(alternating optimization,AO)和MM(Majorization-Minimization)的迭代算法来求解次优解。仿真结果表明,相比于基准方案,本论文的方案能够有效提高通信质量和安全性。 展开更多
关键词 无人机通信 智能反射面 交替优化算法 MM算法 用户调度
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