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多向人工势场法引导的RRT-Connect路径规划算法研究 认领 引用 被引量：1
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作者 丁建军 梁甲杭 +3 位作者 胡志明 章超 叶子安 孙超 《机电工程》 CAS 北大核心 2026年第3期499-513,共15页
针对快速随机扩展树(RRT-Connect)算法的随机性强、搜索效率低、路径规划时间长的问题,提出了一种面向机械臂的多向人工势场法引导的RRT-Connect路径规划算法。首先,引入了多向随机树拓展策略,在初始节点与目标节点连线中点选取了第三... 针对快速随机扩展树(RRT-Connect)算法的随机性强、搜索效率低、路径规划时间长的问题,提出了一种面向机械臂的多向人工势场法引导的RRT-Connect路径规划算法。首先,引入了多向随机树拓展策略,在初始节点与目标节点连线中点选取了第三节点作为根节点,增加了随机树的连接概率;其次,在路径拓展过程中融入了虚拟人工势场法,构建了复合势场函数,该函数将环境信息转化为具有梯度特征的势能空间,其中,引力场结合路径平滑度约束与运动学模型生成了渐进优化的轨迹牵引力,引导随机树向目标节点拓展;斥力场梯度通过自适应参数动态调整,形成了柔性避障区域,实时感知障碍物,提高了算法的收敛速度与避障能力;最后,在二维平面与三维空间环境下进行了仿真分析,还进行了实物抓取实验,验证了该算法的性能。研究结果表明:相较于传统RRT-Connect算法,多向人工势场法引导的RRT-Connect算法的路径平均节点数减少了54.36%,平均路径长度降低了10.23%,路径规划运行时间缩短了53.12%;此外,将该算法结合视觉抓取网络GR-ConvNet,开展了路径规划与实际抓取试验,该算法的路径规划长度减少了15.97%,规划运行时间缩短了51.74%,平均迭代次数降低了27.63%。该算法显著提升了路径规划的效率与稳定性,可为机械臂实现高效自主路径规划提供有力支撑。 展开更多
关键词 机械臂 运动学建模 多向随机树 人工势场法 快速随机扩展树算法
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改进RRT-Connect与AFSA融合算法移动机器人路径规划 认领 引用 被引量：1
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作者 陈志澜 古春祥 《山东大学学报（工学版）》 CAS CSCD 北大核心 2026年第3期73-83,共11页
针对RRT-Connect算法在路径规划中搜索效率低、目标导向性弱、路径冗余节点多、平滑性不佳的问题,在改进RRT-Connect算法与人工鱼群算法基础上,提出ARRT-Connect融合算法.该算法引入中间节点,采用目标偏置策略、引力势场引导、自适应步... 针对RRT-Connect算法在路径规划中搜索效率低、目标导向性弱、路径冗余节点多、平滑性不佳的问题,在改进RRT-Connect算法与人工鱼群算法基础上,提出ARRT-Connect融合算法.该算法引入中间节点,采用目标偏置策略、引力势场引导、自适应步长调节及剪枝优化,并结合B样条曲线平滑路径;改进人工鱼群算法步长与视野范围,增强全局搜索能力.试验表明,与RRT-Connect算法相比,ARRT-Connect融合算法在简单和复杂环境中平均耗时分别减少82.22%和76.92%,平均路径长度分别缩短17.41%和19.38%,平均节点数分别减少79.21%和77.84%.将其应用于现实场景,移动机器人路径长度和耗时明显缩短,路径转折更平缓,验证了该算法有效性、优越性与可行性. 展开更多
关键词 融合算法 移动机器人 路径规划 RRT-Connect 人工鱼群算法
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Novel Sea Otter Optimization Algorithm for WSN Coverage Intelligence Optimization 认领 引用 被引量：2
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作者 WU Jin GAO Yaqiong +2 位作者 SU Zhengdong CHONG Gege XIONG Hao 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期828-842,I0002,共15页
A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for... A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for food in seawater,grooming their fur,feeding with the aid of stones,and escaping from danger.In the exploration stage,a wetness factor is introduced to control the behavior of sea otters in foraging and grooming;a danger factor is introduced to control the behavior of sea otters in feeding and avoiding dangers in the exploitation stage,and the behaviors of sea otters in responding to different dangers are mathematically modeled.The proposed algorithm is compared with 9 well-known intelligent optimization algorithms,and evaluated in 13 benchmark functions as well as wireless sensor network coverage optimization problems to verify the effectiveness of the proposed algorithm.The experimental results show that the node coverage after SOOA optimization reaches 91.2%in 2D environment and 90.47%in 3D environment.Compared with other algorithms,SOOA is superior and possesses the ability to solve complex optimization problems. 展开更多
关键词 sea otter optimization algorithm(SOOA) swarm intelligence optimization wireless sensor network coverage optimization
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Study on the destabilizing damage precursors of cemented tailings backfill based on critical slowing down theory combined with multiple denoising algorithms under consideration of initial defect conditions 认领 引用 被引量：1
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作者 ZHAO Kang ZHONG Jun-cheng +3 位作者 YAN Ya-jing LIU Yang WEN Dao-tan XIAO Wei-ling 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第1期375-399,共25页
The cemented tailings backfill(CTB)with initial defects is more prone to destabilization damage under the influence of various unfavorable factors during the mining process.In order to investigate its influence on the... The cemented tailings backfill(CTB)with initial defects is more prone to destabilization damage under the influence of various unfavorable factors during the mining process.In order to investigate its influence on the stability of underground mining engineering,this paper simulates the generation of different degrees of initial defects inside the CTB by adding different contents of air-entraining agent(AEA),investigates the acoustic emission RA/AF eigenvalues of CTB with different contents of AEA under uniaxial compression,and adopts various denoising algorithms(e.g.,moving average smoothing,median filtering,and outlier detection)to improve the accuracy of the data.The variance and autocorrelation coefficients of RA/AF parameters were analyzed in conjunction with the critical slowing down(CSD)theory.The results show that the acoustic emission RA/AF values can be used to characterize the progressive damage evolution of CTB.The denoising algorithm processed the AE signals to reduce the effects of extraneous noise and anomalous spikes.Changes in the variance curves provide clear precursor information,while abrupt changes in the autocorrelation coefficient can be used as an auxiliary localization warning signal.The phenomenon of dramatic increase in the variance and autocorrelation coefficient curves during the compression-tightening stage,which is influenced by the initial defects,can lead to false warnings.As the initial defects of the CTB increase,its instability precursor time and instability time are prolonged,the peak stress decreases,and the time difference between the CTB and the instability damage is smaller.The results provide a new method for real-time monitoring and early warning of CTB instability damage. 展开更多
关键词 initial defects cemented tailings backfill critical slowing down acoustic emission RA/AF values denoising algorithms
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基于智能采样的APF-RRT-Connect路径规划 认领 引用 被引量：1
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作者 王庆辉 熊兰慧 《电光与控制》 CSCD 北大核心 2026年第4期29-34,63,共6页
针对传统RRT-Connect算法在复杂环境下随机性大、收敛速度慢、路径节点冗余等问题,提出了一种基于智能采样的APF-RRT-Connect路径规划算法。首先,引入智能动态盒型采样策略,提高采样目标性和规划效率,减少无效采样点。其次,在扩展树的... 针对传统RRT-Connect算法在复杂环境下随机性大、收敛速度慢、路径节点冗余等问题,提出了一种基于智能采样的APF-RRT-Connect路径规划算法。首先,引入智能动态盒型采样策略,提高采样目标性和规划效率,减少无效采样点。其次,在扩展树的过程中,引入改进的人工势场法,并结合自适应步长使新节点扩展方向远离障碍物,增强避障能力,避免陷入局部最优解。最后,通过贪心算法和三次B样条插值优化路径的平滑性与长度,生成更高效、更短的路径。仿真结果表明,在不同环境下,与RRT-Connect算法相比,所提算法平均节点数大幅减少,规划速度显著提高,路径长度明显缩短,验证了算法的高效性与适应性。 展开更多
关键词 RRT-Connect 智能采样策略 人工势场法 路径优化
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基于改进RRT-Connect算法的路径规划设计 认领 引用
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作者 魏芳波 冯吴穹 +1 位作者 杨佳敏 王卓银 《武夷学院学报》 2026年第3期76-84,共9页
针对传统RRT算法在复杂环境中搜索效率低、路径冗长和曲折的问题,提出在RRT-Connect算法基础上融入人工势场和动态步长的改进方法。该方法将目标点视为引力源,障碍物视为斥力源,提高搜索导向性,实现快速路径规划,同时引入动态步长自适... 针对传统RRT算法在复杂环境中搜索效率低、路径冗长和曲折的问题,提出在RRT-Connect算法基础上融入人工势场和动态步长的改进方法。该方法将目标点视为引力源,障碍物视为斥力源,提高搜索导向性,实现快速路径规划,同时引入动态步长自适应调节步长,优化路径。实验结果表明:与传统RRT算法和传统RRT-Connect算法相比,改进后的算法在规划时间和路径长度上均有较大改善,展现出更优的性能,显著提高路径规划的效率。 展开更多
关键词 改进RRT-Connect算法 路径规划 人工势场法 轨迹优化
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改进RRT-Connect的机械臂路径规划算法研究 认领 引用
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作者 李明明 刘广 《制造技术与机床》 CAS 北大核心 2026年第7期63-72,共10页
针对机械臂路径规划中快速扩展随机树(rapidly-exploring random tree,RRT)算法存在的规划效率偏低、迭代次数冗余和节点冗余度高等问题,提出一种基于三棵树协同增长机制的改进路径规划算法。改进算法采用起点相连树、中心扩展树和终点... 针对机械臂路径规划中快速扩展随机树(rapidly-exploring random tree,RRT)算法存在的规划效率偏低、迭代次数冗余和节点冗余度高等问题,提出一种基于三棵树协同增长机制的改进路径规划算法。改进算法采用起点相连树、中心扩展树和终点相连树的三树协同架构,同步构建起点相连树与终点相连树,实现向中心树的双向协同靠拢;设计适配性限定采样空间并融合目标偏置扩展技术,显著提升树节点生成的有效性与生长的定向性;路径生成后,采用反向搜索剪枝完成路径粗优化,结合正向插值剪枝实现路径精优化,通过二者协同作用缩短路径长度,最终采用三次B样条曲线完成路径平滑处理。基于Matlab和机器人操作系统2(Robot Operating System 2,ROS2)平台的MoveIt2的三维随机地图仿真实验表明,与传统算法相比,改进算法在路径长度略微减少的前提下,规划时间缩短40%~70%,迭代次数减少50%~70%,有效提升机械臂避障路径规划的效率和质量。 展开更多
关键词 机械臂 三维路径规划 RRT-Connect算法 目标偏置策略 路径剪枝策略
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碰撞地图指导的RRT-Connect路径规划研究 认领 引用
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作者 冯永利 张宝增 《重庆理工大学学报（自然科学）》 CAS 北大核心 2026年第7期138-145,155,共8页
针对传统RRT-Connect算法在复杂环境规划中找到的路径存在目标导向差、采样随机性强、路径质量不佳等问题,提出一种基于碰撞地图改进RRT-Connect路径规划算法。利用贝叶斯环境建模理论搭建碰撞概率场,设计了多级自适应步长调整,依靠碰... 针对传统RRT-Connect算法在复杂环境规划中找到的路径存在目标导向差、采样随机性强、路径质量不佳等问题,提出一种基于碰撞地图改进RRT-Connect路径规划算法。利用贝叶斯环境建模理论搭建碰撞概率场,设计了多级自适应步长调整,依靠碰撞概率自动调整步长以降低无效试探;构建双树动态权重调整机制,根据迭代次数动态调整向目标点的学习率,实现“探索-收敛权衡”;最后利用混合平滑算法,确保生成平滑的路径。通过多组实验对比,结果表明,在三维空间下,路径长度减少了23.1%,规划时间缩短了33.3%,且算法节点数减少了65.8%,路径的最大曲率和曲率连续性分别减少了74.1%、68.7%。证明改进后的算法规划时间更短,节点利用率更高,生成的路径更平滑,对复杂障碍环境有更好的适应能力。 展开更多
关键词 RRT-Connect算法 碰撞地图 路径规划 自适应步长 混合平滑
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基于分层强化学习的RRT-Connect机械臂路径规划 认领 引用
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作者 王崴 王帅航 +2 位作者 王庆力 瞿珏 刘海平 《控制与决策》 EI CSCD 北大核心 2026年第7期1958-1969,共12页
针对双向快速搜索随机树(RRT-Connect)算法在机械臂路径规划中存在的搜索效率低下、路径规划质量不高以及复杂环境适应性差等核心问题,提出一种融合分层启发式引导与强化学习的机械臂路径规划算法H-RRT-C.该方法构建多策略协同优化体系... 针对双向快速搜索随机树(RRT-Connect)算法在机械臂路径规划中存在的搜索效率低下、路径规划质量不高以及复杂环境适应性差等核心问题,提出一种融合分层启发式引导与强化学习的机械臂路径规划算法H-RRT-C.该方法构建多策略协同优化体系:上层利用改进A*算法生成全局粗粒度路径骨架,并采用自适应权重机制指导双向搜索树优先采样关键节点,有效减少随机探索的盲目性;下层引入Dijkstra局部搜索机制,依据障碍物分布密度动态调整搜索范围,实现局部路径精细化处理.同时引入双Q网络强化学习策略,设计包含路径长度、节点分布多样性及避障安全性的多目标奖励函数,以实现扩展方向的智能决策.最后,通过Matlab仿真实验验证该算法在各种复杂场景中的路径规划效果,并通过ROS平台以及实体机械臂测试验证了其工程实用性. 展开更多
关键词 机械臂 改进RRT-Connect 路径规划 分层启发式引导 强化学习
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A Metaheuristic Football Optimization Algorithm Integrated with Large Language Models for Automated Seismic Time-Series Modeling 认领 引用
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作者 Amal H.Alharbi Marwa M.Eid +2 位作者 Nima Khodadadi Ebrahim A.Mattar Sayed Elkenawy 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期947-987,共41页
Seismic time series forecasting remains challenging due to the nonlinearity,non-stationarity,and noise of earthquake data,and because deep learning models are sensitive to preprocessing and hyperparameter settings.Alt... Seismic time series forecasting remains challenging due to the nonlinearity,non-stationarity,and noise of earthquake data,and because deep learning models are sensitive to preprocessing and hyperparameter settings.Although recent studies have improved neural architectures and optimization techniques,preprocessing is often treated as a fixed or manually designed stage,with limited integration into model optimization.To address this,this paper proposes an integrated,data-driven modelling framework that combines guided preprocessing with systematic hyperparameter optimization for seismic prediction,specifically forecasting earthquake magnitude from seismic catalog time-series data,with experiments conducted on Canadian seismic records.The method uses a Large Language Model to guide data preparation and feature engineering,rather than fully automate them,and applies deep learning-based forecasting with the N-HITS architecture,optimized via metaheuristic-assisted feature selection and hyperparameter tuning.The Football Optimization Algorithm(FbOA),employed as a metaheuristic optimization strategy in this study,is evaluated and compared with several well-known optimizers under identical conditions.The results show significant performance gains,with FbOA achieving superior accuracy,robustness,and convergence compared to baseline and competing methods.Notably,error metrics are reduced(MSE 3.10×10-7,RMSE 5.57×103),with high performance indicators(r=0.982,R2=0.979,NSE=0.981,WI=0.985).These results highlight the value of integrating guided preprocessing with optimization and demonstrate a scalable framework for high-precision time-series prediction in geophysical and related domains. 展开更多
关键词 Seismic time-series forecasting large language models metaheuristic algorithms football optimization algorithm earthquake modeling
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A Quantum-Inspired Algorithm for Clustering and Intrusion Detection 认领 引用
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作者 Gang Xu Lefeng Wang +5 位作者 Yuwei Huang Yong Lu Xin Liu Weijie Tan Zongpeng Li Xiu-Bo Chen 《Computers, Materials & Continua》 SCIE EI 2026年第4期1180-1215,共36页
The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,convention... The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,conventional clustering-based methods face notable drawbacks,including poor scalability in handling high-dimensional datasets and a strong dependence of outcomes on initial conditions.To overcome the performance limitations of existing methods,this study proposes a novel quantum-inspired clustering algorithm that relies on a similarity coefficient-based quantum genetic algorithm(SC-QGA)and an improved quantum artificial bee colony algorithm hybrid K-means(IQABC-K).First,the SC-QGA algorithmis constructed based on quantum computing and integrates similarity coefficient theory to strengthen genetic diversity and feature extraction capabilities.For the subsequent clustering phase,the process based on the IQABC-K algorithm is enhanced with the core improvement of adaptive rotation gate and movement exploitation strategies to balance the exploration capabilities of global search and the exploitation capabilities of local search.Simultaneously,the acceleration of convergence toward the global optimum and a reduction in computational complexity are facilitated by means of the global optimum bootstrap strategy and a linear population reduction strategy.Through experimental evaluation with multiple algorithms and diverse performance metrics,the proposed algorithm confirms reliable accuracy on three datasets:KDD CUP99,NSL_KDD,and UNSW_NB15,achieving accuracy of 98.57%,98.81%,and 98.32%,respectively.These results affirm its potential as an effective solution for practical clustering applications. 展开更多
关键词 Intrusion detection clustering quantum artificial bee colony algorithm K-means quantum genetic algorithm
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TWO PARALLEL ALGORITHMS FOR A CLASS OF SPLIT COMMON SOLUTION PROBLEMS 认领 引用
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作者 Truong Minh TUYEN Nguyen Thi TRANG Tran Thi HUONG 《Acta Mathematica Scientia》 SCIE CSCD 2026年第1期505-518,共14页
We study the split common solution problem with multiple output sets for monotone operator equations in Hilbert spaces.To solve this problem,we propose two new parallel algorithms.We establish a weak convergence theor... We study the split common solution problem with multiple output sets for monotone operator equations in Hilbert spaces.To solve this problem,we propose two new parallel algorithms.We establish a weak convergence theorem for the first and a strong convergence theorem for the second. 展开更多
关键词 iterative algorithm Hilbert space metric projection proximal point algorithm
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Structured Random Cycle-Guided Algorithm (SRCA): An Adaptive Metaheuristic Combining Directionally-Guided and Stochastic Search Strategies 认领 引用
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作者 Giuseppe Marannano Antonino Cirello Tommaso Ingrassia 《Computers, Materials & Continua》 SCIE EI 2026年第6期1873-1898,共26页
In response to the growing need for adaptive optimization algorithms capable of handling complex,multimodal,and high-dimensional search spaces,this paper introduces the Structured Random Cycle-guided Algorithm(SRCA).S... In response to the growing need for adaptive optimization algorithms capable of handling complex,multimodal,and high-dimensional search spaces,this paper introduces the Structured Random Cycle-guided Algorithm(SRCA).SRCA is not presented as a fundamentally new optimization paradigm,but rather as an architectural synthesis and a unified adaptive framework for dynamic operator selection.Based on a cycle-structured architecture,directional and stochastic search behaviors are dynamically selected at the individual level.The algorithm orchestrates well-established structured movements with a diverse pool of stochastic exploration strategies,enabling a coherent and adaptive balance between exploration and exploitation throughout the optimization process.Unlike traditional metaheuristics that rely on fixed behavioral roles or static movement schemes,SRCA allows each individual to adapt its search strategy based on real-time population feedback,monitored through convergence and dispersion indicators.The performance of SRCA is quantitatively assessed under strictly identical experimental conditions on a comprehensive set of 23 benchmark functions,including multimodal and high-dimensional problems,as well as on six classical constrained engineering design problems.Numerical results demonstrate competitive convergence reliability and robustness across diverse optimization tasks,confirming the effectiveness of the proposed adaptive cycle-based framework. 展开更多
关键词 Metaheuristic algorithms optimization constrained optimization benchmark functions structured random cycle-guided algorithm
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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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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://gffzz188fe103f8f1460asbxqxcwqwwvoo6ww0.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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Flood predictions from metrics to classes by multiple machine learning algorithms coupling with clustering-deduced membership degree 认领 引用
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作者 ZHAI Xiaoyan ZHANG Yongyong +5 位作者 XIA Jun ZHANG Yongqiang TANG Qiuhong SHAO Quanxi CHEN Junxu ZHANG Fan 《Journal of Geographical Sciences》 SCIE CSCD 2026年第1期149-176,共28页
Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting... Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach. 展开更多
关键词 flood regime metrics class prediction machine learning algorithms hydrological model
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An Enhanced Genetic Algorithm via an Innovative Elite Retention Strategy for Task Offloading in MEC Scenarios 认领 引用
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作者 Chengyu Hou Wenzao Li +3 位作者 Hanyun Li Kui Liu Zhuoning Zhao Hongping Shu 《Computers, Materials & Continua》 SCIE EI 2026年第8期2198-2218,共21页
The rapid growth of Internet of Things(IoT)and 5G technologies has led to a sharp increase in computing demands from wireless devices,making efficient task offloading a critical challenge.Key issues include reducing a... The rapid growth of Internet of Things(IoT)and 5G technologies has led to a sharp increase in computing demands from wireless devices,making efficient task offloading a critical challenge.Key issues include reducing application latency,lowering the energy consumption of terminal devices,and improving overall system performance,all of which directly affect user experience.Traditional genetic algorithms(GA),inspired by biological evolution,have been widely used in task offloading,but they often suffer from slow convergence and a tendency to fall into local optima in complex scenarios,limiting their effectiveness.To address these drawbacks,this paper proposes a task offloading strategy based on a refined elite mechanism in a GA.The algorithm introduces multi-point variation in both crossover and mutation operations to enhance population diversity,avoid local optima,and accelerate convergence.This design leverages the GA’s strength in multi-objective optimization,which outperforms other bionic heuristic algorithms that excel in single domains.Comparative experiments with GA,ant colony optimization,Deep Q-Network,Greedy algorithms,simulated annealing algorithm and particle swarm optimization,show that the proposed algorithm improves convergence speed by 35%,reduces task completion time by 6%,and optimizes energy consumption by approximately 18%. 展开更多
关键词 Task offloading genetic algorithm bandwidth constraint mobile edge computing
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Phased-Enhancement Marine Predators Algorithm for Global Optimization and Medical Insurance Fraud Detection 认领 引用
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作者 Wen Long Yujia Wang +2 位作者 Qinghua Long Yang Yang Ming Xu 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第2期1088-1111,共24页
The Marine Predators Algorithm(MPA),while promising for complex optimization,suffers from limited solution precision,imbalanced exploration–exploitation,and premature convergence.To address these shortcomings,this pa... The Marine Predators Algorithm(MPA),while promising for complex optimization,suffers from limited solution precision,imbalanced exploration–exploitation,and premature convergence.To address these shortcomings,this paper proposes a phased-enhancement variant named PEMPA,which integrates three novel strategies into distinct phases of MPA:1)embedding historical best positions in the high-velocity ratio phase to refine solution quality;2)introducing an adaptive inertia weight based on an inverted Sigmoid function in the unit-velocity ratio phase to systematically balance exploration and exploitation;and 3)designing a two-stage opposition-based learning operator in the low-velocity ratio phase to prevent premature convergence.The performance of PEMPA is comprehensively evaluated across 23 classical benchmark functions,the IEEE Congress on Evolutionary Computation(CEC)2017 test suite,21 feature selection tasks,and a real-world medical insurance fraud detection problem.Experimental results confirm that the proposed strategies significantly enhance the efficiency and robustness of MPA.Furthermore,PEMPA demonstrates highly competitive performance compared with several state-of-the-art metaheuristic algorithms,validating its effectiveness and scalability for diverse optimization challenges. 展开更多
关键词 Marine predators algorithm Opposite-based learning Inertia weight Numerical optimization Feature selection
Experience-guided optimization of jacket foundations for offshore wind turbines in varying water depths based on finite element analysis and the genetic algorithm 认领 引用
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作者 Jiajia HUANG Tao JIN +6 位作者 Jianwu HUANG Shasha SONG Wei DAI Chaoqun ZUO Lizhong WANG Lilin WANG Zhen GUO 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2026年第3期183-199,共17页
Structural optimization plays a crucial role in reducing the cost of offshore wind power,particularly in deep-water regions where the weight of jacket foundations increases substantially.However,there is ongoing debat... Structural optimization plays a crucial role in reducing the cost of offshore wind power,particularly in deep-water regions where the weight of jacket foundations increases substantially.However,there is ongoing debate regarding the water-depth range that is suitable for jacket foundations,and the threshold where floating foundations become more viable.Existing studies have not quantitatively analyzed how water depth affects jacket foundation mass,and have often struggled to handle the high dimensionality and stringent constraints inherent in jacket foundation optimization problems.In this study,we propose an optimization framework that couples parametric finite element analysis with a genetic algorithm to minimize the mass of jacket foundations based on three actual engineering projects at varying water depths.A novel population initialization strategy incorporating engineering experience-based solutions is introduced to improve convergence efficiency and solution quality.Comparative analysis against preliminary designs and existing offshore wind projects demonstrates the model’s ability to achieve cost-effective solutions,specifically reducing required jacket masses by 18.66%,20.98%,and 17.22%at depths of 30.06,60.23,and 89.81 m,respectively.The results reveal a 122.94%increase in jacket mass—from 1431.28 to 3190.90 t—as water depth increases from 30.06 to 89.81 m.The jacket foundation demonstrates superior cost effectiveness in shallow to moderate water depths,as the unit weight per megawatt(MW)of floating foundations is 97.51%and 35.74%higher at water depths of 60.23 and 89.81 m,respectively.Accordingly,the applicable water-depth threshold between the jacket and floating foundations is estimated to be approximately 100 m.The proposed optimization model offers a novel methodology and practical insights for the optimal design of offshore wind turbine support structures in varying marine environments. 展开更多
关键词 Structural optimization Jacket foundation Genetic algorithm Offshore wind power Population initialization Parametric modeling
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A game theoretic model and a double oracle algorithm for the heterogeneous weapon target assignment problem 认领 引用
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作者 MA Yingying LUO He +2 位作者 WANG Guoqiang ZHU Waiming HU Xiaoxuan 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第2期548-566,共19页
Weapon target assignment(WTA)problem is a critical problem in multiplatform confrontation.This paper studies a static WTA problem with heterogeneous weapons in multi-platform air combat scenarios,called heterogeneous ... Weapon target assignment(WTA)problem is a critical problem in multiplatform confrontation.This paper studies a static WTA problem with heterogeneous weapons in multi-platform air combat scenarios,called heterogeneous WTA(HWTA)problem.Heterogeneous indicates that the engagement platforms carry multiple kinds of weapons for different tactical purposes.The targets assigned and the weapons used by one side’s platforms will affect the survival probability and capability of the other side’s platforms.The goal of each side in HWTA is to find a solution to determine the kind of weapon used and the target assigned for each platform,so as to maximize their combat effectiveness.The problem is formulated as a two-player noncooperative game model with considering the conflicts between the engaged sides.The Nash equilibrium is an effective solution to the game in which no player has an incentive to deviate.However,the number of pure strategies in HWTA increases exponentially with the engagement platforms.To improve computing efficiency,a double oracle algorithm with constructive heuristic(DOCH)is developed,within which the constructive heuristic is embedded to solve the oracle subproblems efficiently.Numerical experiments are conducted to verify the effectiveness of the DOCH.The results show that the DOCH can find effective strategies for platforms to improve combat effectiveness.Moreover,the DOCH can find high-quality solutions in seconds,significantly outperforming the state-of-the-art algorithms in terms of computational efficiency,especially for large-scale problems. 展开更多
关键词 weapon target assignment noncooperative game double oracle algorithm constructive heuristic
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