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RRT*-GSQ:A hybrid sampling path planning algorithm for complex orchard scenarios 认领 引用
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作者 ZHU Qingzhen ZHAO Jiamuyang +1 位作者 DAI Xu YU Yang 《农业工程学报》 EI CAS CSCD 北大核心 2026年第3期13-25,共13页
Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT*),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narr... Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT*),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narrow passages,slow convergence,and high computational costs.To address these challenges,this paper proposes a novel hybrid global path planning algorithm integrating Gaussian sampling and quadtree optimization(RRT*-GSQ).This methodology aims to enhance path planning by synergistically combining a Gaussian mixture sampling strategy to improve node generation in critical regions,an adaptive step-size and direction optimization mechanism for enhanced obstacle avoidance,a Quadtree-AABB collision detection framework to lower computational complexity,and a dynamic iteration control strategy for more efficient convergence.In obstacle-free and obstructed scenarios,compared with the conventional RRT*,the proposed algorithm reduced the number of node evaluations by 67.57%and 62.72%,and decreased the search time by 79.72%and 78.52%,respectively.In path tracking tests,the proposed algorithm achieved substantial reductions in RMSE of the final path compared to the conventional RRT*.Specifically,the lateral RMSE was reduced by 41.5%in obstacle-free environments and 59.3%in obstructed environments,while the longitudinal RMSE was reduced by 57.2%and 58.5%,respectively.Furthermore,the maximum absolute errors in both lateral and longitudinal directions were constrained within 0.75 m.Field validation experiments in an operational orchard confirmed the algorithm's practical effectiveness,showing reductions in the mean tracking error of 47.6%(obstacle-free)and 58.3%(with obstructed),alongside a 5.1%and 7.2%shortening of the path length compared to the baseline method.The proposed algorithm effectively enhances path planning efficiency and navigation accuracy for robots,presenting a superior solution for high-precision autonomous navigation of agricultural robots in orchard environments and holding significant value for engineering applications. 展开更多
关键词 robot path planning orchard improved RRT*algorithm Gaussian sampling autonomous navigation
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基于改进APF-IRRT*的机械臂路径规划研究 认领 引用 被引量：1
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作者 戈海龙 董学富 +1 位作者 孙嘉宾 成巍 《制造技术与机床》 CAS 北大核心 2026年第6期75-82,共8页
针对传统Informed-RRT*(informing rapidly-exploring random trees with path distance lower bounds)算法在机械臂避障路径规划中目标导向性不足、随机性大、规划效率低以及路径冗余造成运动不平滑等问题,提出一种改进的APF-Inform... 针对传统Informed-RRT*(informing rapidly-exploring random trees with path distance lower bounds)算法在机械臂避障路径规划中目标导向性不足、随机性大、规划效率低以及路径冗余造成运动不平滑等问题,提出一种改进的APF-Informed RRT*算法。在采样阶段,引入人工势场法引导采样点,提高随机树扩展的目标导向性。在树生长阶段,通过碰撞检测缓存键和自适应变步长策略,缓存碰撞检测结果,并生成唯一标识为缓存键,通过与障碍物的距离动态调整步长,减少碰撞检测次数,提高搜索效率。在优化阶段,采用分段式贪心算法和三次B样条曲线优化路径。通过与Informed-RRT*、GoalBias-RRT*和RRT*算法在三维环境下的仿真对比实验。结果表明,相较于Informed-RRT*算法,APF-Informed RRT*算法路径长度减少14.08%,时间节约96.17%;与RRT*和GoalBias-RRT*算法相比,APF-Informed RRT*算法路径长度分别减少19.89%和12.24%,时间分别节约88.52%和12.5%,算法的搜索效率得到显著的提升。将算法应用到AUBO i5机械臂平台,机械臂能够成功避开障碍物并准确到达目标点,且各关节的运动曲线平滑,进一步证明了改进算法的有效性。 展开更多
关键词 机械臂 路径规划 Informed-RRT*算法 缓存键策略 分段贪心策略 三次B样条曲线
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改进RRT*算法的移动机器人路径规划 认领 引用
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作者 池吕庭 鲁宇明 黎政秀 《机械科学与技术》 CSCD 北大核心 2026年第1期131-140,共10页
针对RRT*算法的随机性强、收敛速度慢、路径质量不佳等问题,提出了一种双向启发式自适应搜索RRT*算法。首先,通过双向搜索策略,分别从起止位置各生长一棵随机树,在工作空间中进行搜索,使算法收敛的更快。其次,引入了人工势场中引... 针对RRT*算法的随机性强、收敛速度慢、路径质量不佳等问题,提出了一种双向启发式自适应搜索RRT*算法。首先,通过双向搜索策略,分别从起止位置各生长一棵随机树,在工作空间中进行搜索,使算法收敛的更快。其次,引入了人工势场中引力场的思想,使随机树在生成新节点时,偏向目标位置进行生长,以减小随机树采样时的强随机性。最后,采用了自适应步长策略,提高了算法的避障能力以及路径的平滑度。实验证明,改进策略的综合作用使得改进后的RRT*算法能够快速、有效地完成路径规划任务。 展开更多
关键词 移动机器人 路径规划 RRT*算法 随机采样 目标引力
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基于人工势场法改进RRT*算法的无人船路径规划 认领 引用
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作者 陈小龙 李明智 +3 位作者 汪雅琴 张橙橙 陈禹成 张赫轩 《舰船科学技术》 北大核心 2026年第5期151-157,共7页
针对无人船在障碍物环境中路径规划效率低、路径冗余大、平滑性差等问题,提出一种融合人工势场法的改进RRT*算法(Artificial Potential Field Rapidly-exploring Random Tree*,APF-RRT*)。该算法以人工势场模型为引导机制,构... 针对无人船在障碍物环境中路径规划效率低、路径冗余大、平滑性差等问题,提出一种融合人工势场法的改进RRT*算法(Artificial Potential Field Rapidly-exploring Random Tree*,APF-RRT*)。该算法以人工势场模型为引导机制,构建包含信息激励势场、引力势场和斥力势场的复合势能函数,从而有目的性地引导采样点朝向目标区域并远离障碍物,提高算法采样效率;同时引入动态步长策略加快扩展速度,最后结合贪心剪枝和路径平滑机制优化初始路径。在2种典型环境下进行仿真对比实验,结果表明文中改进RRT*算法在规划效率、路径长度、平滑度以及稳定性方面优于传统算法,平均规划时长、路径长度、初始路径节点数与改进前相比分别减少了50.88%、6.24%、30.29%。改进算法为无人船在障碍物环境下生成安全、高效的航行路径提供了有力保障。 展开更多
关键词 路径规划 RRT*算法 人工势场模型 复合势能函数 无人船
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基于改进APF-Bi-RRT* 算法的移动机器人路径规划研究 认领 引用
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作者 韦伟 陈宇 齐文 《南京信息工程大学学报》 CAS 北大核心 2026年第3期362-371,共10页
针对移动机器人路径规划中的双向快速拓展随机树(Bi-RRT*)算法存在采样点冗余、路径规划质量不高和路径曲折的问题,本文提出一种改进的APF-Bi-RRT*算法。首先,在Bi-RRT*的基础上引入动态目标偏置函数,减少采样点,提高路径规划... 针对移动机器人路径规划中的双向快速拓展随机树(Bi-RRT*)算法存在采样点冗余、路径规划质量不高和路径曲折的问题,本文提出一种改进的APF-Bi-RRT*算法。首先,在Bi-RRT*的基础上引入动态目标偏置函数,减少采样点,提高路径规划的效率;其次,在人工势场方面引入动态斥力系数,实时调节斥力大小,提高路径规划质量;最后,对剪枝算法进行改进,通过引入安全距离检测策略,结合三次B样条曲线,改善路径曲折的问题。将算法应用于多种环境进行仿真实验,结果表明,相较于APF-Bi-RRT*、Bi-RRT*和RRT*算法,规划效率分别提升16.46%、22.52%和62.68%,改进后的算法在耗时、采样点上大幅减少,路径长度更短、平滑度更高。 展开更多
关键词 路径规划 Bi-RRT*算法 人工势场 剪枝算法 三次B样条
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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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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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基于改进RRT*算法的桥式起重机智能路径规划 认领 引用
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作者 王全伟 季子亚 +3 位作者 刘旭莹 侯佳怡 王晓洋 文豪 《机电工程》 CAS 北大核心 2026年第4期761-771,共11页
针对桥式起重机无人化智能运行场景下智能路径优化和决策问题,提出了一种改进RRT*算法,即一种融合了人工势场法(APF)和粒子群优化算法(PSO)的双向探索快速扩展随机树星(APF-BI-PSO-RRT*)算法。首先,RRT*算法融合了APF,智能地... 针对桥式起重机无人化智能运行场景下智能路径优化和决策问题,提出了一种改进RRT*算法,即一种融合了人工势场法(APF)和粒子群优化算法(PSO)的双向探索快速扩展随机树星(APF-BI-PSO-RRT*)算法。首先,RRT*算法融合了APF,智能地引导随机树的生长方向,有效地解决了传统RRT*算法的盲目性、效率低的问题;然后,采用了双向探索策略(BI)加快了搜索速率,在保持算法概率完备性的同时,能够更快地收敛到最优解;接着,引入了PSO和贪心算法平滑路径,并剔除了冗余节点,减少了路径代价,降低了能耗并提高了运行效率;最后,为评估该算法的有效性,采用了MATLAB仿真实验,对比了人工操作路径1、路径2、路径3、路径4,及4种算法RRT*、APF-RRT*、APF-BI-RRT*和APF-BI-PSO-RRT*,共8种方法的性能;并以路径长度、能耗、起重机沿生成路径完成了一次工作循环所用时间、总节点数量、路径节点数量和规划时间这6个关键指标作为评价标准。研究结果表明:APF-BI-PSO-RRT*算法在所有评估标准上均优于所对比的其他方法;与传统算法相比,该算法在路径长度上缩减了34.8%,平均能耗减少了74.2%,时间代价降低了48.3%,平均总节点数量减少了83%,平均路径节点数量减少了97.1%,平均规划时间减少了90.5%。该算法具有高效性和实用性,其规划的路径支持桥式起重机的多机构协同运行,能够实现无人化场景下的智能路径规划,提升工业现场的安全性和可靠性。 展开更多
关键词 起重机械 快速扩展随机树星算法 改进RRT*算法 路径规划 人工势场法 双向探索策略 粒子群优化算法 贪心算法
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Low-complexity APSK demodulation algorithm based on K-means clustering in LEO satellite communication systems 认领 引用
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作者 Guangfu Wu Xiangrui Meng +1 位作者 Changlin Chen Biqun Xiang 《Digital Communications and Networks》 SCIE EI CSCD 2026年第2期343-353,共11页
Amplitude Phase Shift Keying(APSK)is more suitable for the nonlinear channels of Low Earth Orbit(LEO)satellite communication systems compared to Quadrature Amplitude Modulation(QAM).To tackle challenges posed by Direc... Amplitude Phase Shift Keying(APSK)is more suitable for the nonlinear channels of Low Earth Orbit(LEO)satellite communication systems compared to Quadrature Amplitude Modulation(QAM).To tackle challenges posed by Direct Current(DC)interference and high demodulation complexity,we propose an APSK demodulation algorithm based on K-means clustering.Initially,static DC components are calculated and removed from the received APSK signals.Subsequently,the estimated APSK constellation points serve as initial centers for K-means clustering.These centers are refined through the K-means process and act as theoretical APSK constellation points for the Max-Log-MAP demodulation algorithm,effectively eliminating residual DC.We then introduce a low-complexity APSK demodulation algorithm that utilizes the symmetry of constellation points along with the Euclidean distance between DC-eliminated signals and these constellation points to minimize the set of constellation points.Simulation results indicate that for 32-APSK,our proposed demodulation submodule reduces computational complexity to approximately one-third that of the Max-Log-MAP algorithm while improving Bit Error Rate(BER)performance by about 0.23 dB.Furthermore,end-to-end simulation experiments conducted within LEO satellite communication systems demonstrate that our approach not only maintains this complexity advantage but also enhances BER performance by approximately 1.1 dB. 展开更多
关键词 DC elimination APSK demodulation LEO satellite communication K-means algorithm Max-Log-MAP algorithm
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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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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 a self-tuning force control system for the milling process using a dynamic enhanced genetic algorithm 认领 引用
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作者 Yao Li Zhengcai Zhao +3 位作者 Ning Qian Lei Zhang Wenfeng Ding Yucan Fu 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第2期33-43,共11页
When milling structural components with varying axial depths and widths,cutting forces tend to fluctuate,negatively impacting tool life and machining accuracy.To mitigate the force fluctuations and enhance tool longev... When milling structural components with varying axial depths and widths,cutting forces tend to fluctuate,negatively impacting tool life and machining accuracy.To mitigate the force fluctuations and enhance tool longevity,developing a simple,reliable,and easy-to-implement force control system for milling is essential,which is an important step toward advancing intelligent manufacturing.This paper explores the use of genetic algorithms(GA) for powerful optimization capabilities in developing self-tuning milling force controllers.A comprehensive framework for optimizing a fuzzy logic controller using an enhanced GA is specifically designed for the milling process.The optimization integrates the GA with a simulation model,fine-tuning membership functions and optimizing fuzzy rule selection.The enhanced GA incorporates the Integral of Time-weighted Absolute Error(ITAE) as the fitness criterion to improve the robustness and responsiveness of the controller.The optimized fuzzy logic controller is implemented within a computer numerical control system,adjusting feed rates in real-time to control milling forces.The performance of the proposed controller is validated through step and slope milling tests,demonstrating an average control accuracy of 95.52%.Comparative evaluations with other controllers show that the proposed system offers a significant improvement,achieving up to 4.58% better control accuracy in step milling tests. 展开更多
关键词 Optimization Self-tuning Force control system Milling process Genetic algorithm
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基于改进Informed RRT*的移动机器人路径规划算法 认领 引用
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作者 张振利 谢飞 +1 位作者 袁达凯 韩树人 《河北大学学报（自然科学版）》 CAS 北大核心 2026年第4期424-436,共13页
针对传统的Informed RRT*算法在路径规划中存在采样随机性强、随机树扩展效率低和路径不平滑等问题,提出一种改进Informed RRT*算法.该算法采用高斯偏置与均匀随机相结合的混合采样策略来降低采样随机性和提高初始路径搜索效率,... 针对传统的Informed RRT*算法在路径规划中存在采样随机性强、随机树扩展效率低和路径不平滑等问题,提出一种改进Informed RRT*算法.该算法采用高斯偏置与均匀随机相结合的混合采样策略来降低采样随机性和提高初始路径搜索效率,并利用贝叶斯学习自适应调整采样权重与高斯标准差;在节点扩展阶段融合改进的人工势场与贪婪步长扩展策略,以增强随机树扩展的目的性并提高扩展速度;在路径后处理中,采用双尺度滑动窗口裁剪与欧拉螺线-圆弧-欧拉螺线(Clothoid-Arc-Clothoid,CAC)曲线平滑方法,以减少冗余节点并提高路径平滑性.通过仿真实验对比表明,改进Informed RRT*算法获取初始路径时间更短,算法收敛效率更高,所得路径的长度更短且更平滑. 展开更多
关键词 Informed RRT* 路径规划 高斯偏置 人工势场 CAC曲线
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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://gffzz188fe103f8f1460asxkfq6kq0xppo6bbp.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 algorithm-assisted high-resolution D-TOF imaging system with reconfigurable macropixel-based SPAD image sensor 认领 引用
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作者 Zhe Wang Jia-xing Song +8 位作者 Na Tian Xing-jia Ni Xu Yang Run-jiang Dou Peng Feng Jian Liu Nan-jian Wu Li-yuan Liu Shuang-ming Yu 《Journal of Semiconductors》 EI CAS CSCD 2026年第7期61-71,共11页
Single-photon avalanche diode(SPAD)image sensors are widely used in direct time-of-flight(D-TOF)imaging,but their ranging performance is often constrained by limited laser power.This article presents a SPAD-based D-TO... Single-photon avalanche diode(SPAD)image sensors are widely used in direct time-of-flight(D-TOF)imaging,but their ranging performance is often constrained by limited laser power.This article presents a SPAD-based D-TOF imaging system that combines a reconfigurable macro-pixel sensor architecture with a lightweight depth completion algorithm to achieve long-range depth imaging with enhanced spatial resolution under low optical power.The proposed sensor adopts a back-side illuminated(BSI)3D-stacked architecture with programmable macro-pixels that enhance detection sensitivity and enable flexible sensitivity–resolution trade-offs.An injection-locked ring-oscillator-based time-to-digital converter(RO-TDC)array achieves a time resolution of 152.5 ps,enabling accurate TOF measurement at an optical power of 10 mW.To compensate for macropixel-induced resolution loss,a probabilistic normalized convolutional neural network(pNCNN)is employed for depth completion using sparse depth inputs only.Experimental results demonstrate that up to 30×effective resolution enhancement of the system can be achieved via the depth completion algorithm without changing the physical resolution of the sensor.Additionally,the proposed system achieves a maximum ranging distance of 90 m and a range-to-power figure-of-merit(FOM)of9 m/mW,which validates the effectiveness of the system. 展开更多
关键词 SPAD reconfigurable macro-pixel time-to-digital converter(TDC) depth completion algorithm
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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
基于PTB-VSS-Informed-RRT*的煤矸分选机械臂轨迹规划研究 认领 引用
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作者 闵振辉 王妍玮 张琛阳 《煤矿机械》 2026年第7期7-14,共8页
针对煤矸分选作业中机械臂常用的改进快速扩展随机树(RRT*)轨迹规划方法的长耗时、冗余路径和低成功率的问题,提出了一种融合了概率目标偏置(PTB)、变步长(VSS)策略和信息增强(Informed)的轨迹规划PTB-VSS-Informed-RRT*算法。首... 针对煤矸分选作业中机械臂常用的改进快速扩展随机树(RRT*)轨迹规划方法的长耗时、冗余路径和低成功率的问题,提出了一种融合了概率目标偏置(PTB)、变步长(VSS)策略和信息增强(Informed)的轨迹规划PTB-VSS-Informed-RRT*算法。首先搜索PTB的数据作为初始路径的值,再扩展VSS优化节点,最后平滑三次B样条曲线的拟合轨迹。仿真和实验结果表明:在无障碍环境下,PTB-VSS-Informed-RRT*算法相较于传统RRT*和Informed-RRT*路径长度分别缩短了21.2%和2.3%,运算时间降低60.16%,成功率提升至99%;在避障的轨迹规划中,路径长度分别缩短了14.85%和3.3%,运算效率提高63.37%。PTB-VSS-Informed-RRT*算法为煤矸智能分选的轨迹规划提供了一种高效且稳定的策略,也可推广到其他的分选作业的轨迹规划中。 展开更多
关键词 煤矸分选 机械臂 轨迹规划 RRT*
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A Deep-Learning-Based Constitutive Method for Geomaterials Using a Neural Cutting Plane Algorithm 认领 引用
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作者 Qingxiang Meng Zijie He +1 位作者 Yajun Cao Weijiang Chu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期158-180,共23页
Constitutive modeling for geomaterials remains challenging because of limited data availability,strong nonlinearity,pressure sensitivity,and the non-smooth characteristics of commonly used yield surfaces.This study pr... Constitutive modeling for geomaterials remains challenging because of limited data availability,strong nonlinearity,pressure sensitivity,and the non-smooth characteristics of commonly used yield surfaces.This study presents a deep-learning-based constitutive method for geomaterials that incorporates a neural stress-integration procedure based on the cutting plane algorithm(CPA).Two compact fully connected networks are trained to learn the yield function and its stress gradient from an augmented stress-state dataset.The trained networks are then incorporated into a cutting plane return-mapping procedure,in which only first-order information is required for the plastic stress return.This avoids explicit analytical yield expressions and second-derivative evaluations and is therefore more naturally compatible with non-smooth Mohr-Coulomb-type yield-surface representations in a first-order returnmapping sense.Numerical results show that the proposed method reproduces the reference Mohr-Coulomb response along the examined monotonic triaxial compression paths.Compared with the finite-difference closest-point projection method(CPPM)implementation considered in this study,the CPA-based neural stress-update procedure requires fewer network calls per update,indicating a more economical implementation for the present learned constitutive framework. 展开更多
关键词 Geomaterials constitutive modeling deep learning cutting plane algorithm stress integration
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