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Structured Random Cycle-Guided Algorithm (SRCA): An Adaptive Metaheuristic Combining Directionally-Guided and Stochastic Search Strategies 认领 引用
1
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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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
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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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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Intelligent identification for discrete memristive neuron map:An adaptive chaos game optimization algorithm studied from the perspectives of different sample sizes and objective functions 认领 引用
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作者 Yuexi Peng Xinyi Luo +2 位作者 Zhijun Li Mengjiao Wang Minglin Ma 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第6期276-291,共16页
Discrete memristive neuron systems have attracted considerable attention due to their nonlinear dynamical properties,low computational overhead,and ease of hardware implementation.For the practical engineering applica... Discrete memristive neuron systems have attracted considerable attention due to their nonlinear dynamical properties,low computational overhead,and ease of hardware implementation.For the practical engineering applications of discrete memristive neuron systems,effective control remains a key issue.Parameter identification using intelligent optimization algorithms is an important approach for controlling complex nonlinear systems.However,classical algorithms are prone to falling into local optima and often exhibit high computational complexity,resulting in slow convergence.Therefore,a new algorithm named adaptive chaos game optimization(ACGO)is proposed to address these issues.By introducing a differential evolution mutation strategy and a Cauchy adaptive parameter mechanism,the ACGO algorithm can effectively balance global exploration and local exploitation capabilities.To verify the effectiveness of the proposed algorithm,it is applied to parameter identification in five discrete memristive neuron maps(DMNMs)and compared with seven intelligent optimization algorithms.Simulation results demonstrate that the ACGO algorithm achieves higher accuracy and faster convergence.In addition,an in-depth investigation is conducted into the effects of sample size and objective function on identification performance.The results indicate that setting the sample size to 4 and selecting the mean squared error(MSE)as the objective function can achieve better identification performance and a high level of robustness. 展开更多
关键词 discrete memristive neuron map parameter identification chaos game optimization algorithm sample size
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An improved Alpha-shape algorithm for extracting section contours of the super-high steel bridge tower using point clouds 认领 引用
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作者 ZHANG Yiming ZHAO Tianhao +2 位作者 LIAO Ruixuan LI Haoqing WANG Hao 《Journal of Southeast University(English Edition)》 EI CAS 2026年第1期26-35,共10页
The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,a... The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,and temperature fluctuations can compromise the accuracy of contour extraction.To address these limitations,an improved Alpha-shape-based point cloud contour extraction method is proposed.The proposed approach uses a hierarchical strategy to process three-dimensional laser scanning point clouds.The processed data are then subjected to curvatureadaptive voxel filtering to reduce acquisition noise.In addition,an enhanced iterative closest point(ICP)variant with correspondence validation accurately aligns the discrete point cloud segments.The proposed curvature-responsive Alpha-shape framework enables multiscale contour delineation through topology-adaptive threshold modulation,which resolves boundary ambiguities in geometrically complex cross-sections.The method was experimentally validated using field-acquired measurement datasets from the Zhangjinggao Yangtze River Bridge tower segments,confirming its capability to reconstruct noncanonical cross-sectional geometries.Three contour extraction methods,including Poisson reconstruction,the conventional Alpha-shape algorithm,and random sample consensus with ICP(RANSAC-ICP),were compared to evaluate the performance of the proposed Alpha-shape algorithm.The results demonstrate that the proposed method achieves superior contour extraction accuracy and data reduction efficiency,highlighting its effectiveness in contour extraction tasks. 展开更多
关键词 super-high steel bridge tower point cloud contour extraction improved Alpha-shape algorithm
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Identification of small impact craters in Chang’e-4 landing areas using a new multi-scale fusion crater detection algorithm 认领 引用
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作者 FangChao Liu HuiWen Liu +7 位作者 Li Zhang Jian Chen DiJun Guo Bo Li ChangQing Liu ZongCheng Ling Ying-Bo Lu JunSheng Yao 《Earth and Planetary Physics》 EI CSCD 2026年第1期92-104,共13页
Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious an... Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy. 展开更多
关键词 impact craters Chang’e-4 landing area multi-scale automatic detection YOLO11 Fusion algorithm
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SSA*-PDWA:A Hierarchical Path Planning Framework with Enhanced A*Algorithm and Dynamic Window Approach for Mobile Robots 认领 引用
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作者 Lishu Qin Yu Gao Xinyuan Lu 《Computers, Materials & Continua》 SCIE EI 2026年第4期2069-2094,共26页
With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper pro... With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper proposes an intelligent path planning framework based on grid map modeling.First,an improved Safe and Smooth A*(SSA*)algorithm is employed for global path planning.By incorporating obstacle expansion and cornerpoint optimization,the proposed SSA*enhances the safety and smoothness of the planned path.Then,a Partitioned Dynamic Window Approach(PDWA)is integrated for local planning,which is triggered when dynamic or sudden static obstacles appear,enabling real-time obstacle avoidance and path adjustment.A unified objective function is constructed,considering path length,safety,and smoothness comprehensively.Multiple simulation experiments are conducted on typical port grid maps.The results demonstrate that the improved SSA*significantly reduces the number of expanded nodes and computation time in static environmentswhile generating smoother and safer paths.Meanwhile,the PDWA exhibits strong real-time performance and robustness in dynamic scenarios,achieving shorter paths and lower planning times compared to other graph search algorithms.The proposedmethodmaintains stable performance across maps of different scales and various port scenarios,verifying its practicality and potential for wider application. 展开更多
关键词 Dynamic window approach improved A*algorithm dynamic path planning trajectory optimization
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Data-Driven Algorithms for Finite-Horizon and Infinite-Horizon Indefinite Linear Quadratic Stochastic Optimal Control Problems 认领 引用
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作者 Guangchen Wang Heng Zhang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1459-1469,共11页
This paper is devoted to devising data-driven algorithms for finite-horizon and infinite-horizon linear quadratic stochastic optimal control(LQSOC)problems.In our study,the diffusion terms of system dynamics are permi... This paper is devoted to devising data-driven algorithms for finite-horizon and infinite-horizon linear quadratic stochastic optimal control(LQSOC)problems.In our study,the diffusion terms of system dynamics are permitted to hinge upon both control and state variables,and the weighting matrices of cost functionals are allowed to be indefinite.It is acknowledged that the optimal controls of finite-horizon and infinite-horizon indefinite LQSOC problems are correlated with a generalized differential Riccati equation(GDRE)and a generalized algebraic Riccati equation(GARE).Herein,we propose two data-driven algorithms to approximate the solutions of these Riccati equations,and thereby determine optimal controls,without leveraging the information of all system parameters.Additionally,we prove the convergence of these algorithms and examine the impact of computational errors.Finally,we validate the performance of these data-driven algorithms via three simulation examples. 展开更多
关键词 Data-driven algorithm generalized algebraic Riccati equation (GARE) generalized differential Riccati equation (GDRE) indefinite problem linear quadratic stochastic optimal control (LQSOC)
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Gravitational Dynamic Radius Nearest Neighbor Trained by Fuzzy Enhanced Hiking Optimization Algorithm for Imbalanced Data Classification 认领 引用
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作者 Zahra Beheshti 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第3期1826-1848,共23页
Imbalanced data classification is one of the most critical challenges in machine learning.Standard classifiers do not provide adequate accuracy for detecting minority samples because these classifiers are biased towar... Imbalanced data classification is one of the most critical challenges in machine learning.Standard classifiers do not provide adequate accuracy for detecting minority samples because these classifiers are biased toward the majority samples.To overcome this drawback,many methods have been proposed.One prominent method is the Gravitational Fixed Radius Nearest Neighbor(GFRNN)algorithm,which applies Newton’s law of universal gravitation to determine the class of a test sample based on two parameters:mass and radius.Although GFRNN shows good performance on some imbalanced datasets,it faces several fundamental problems,including ignoring the data distribution and the improper calculation of radius and mass.In this study,a Gravitational Dynamic Radius Nearest Neighbor trained by a Fuzzy Enhanced Hiking Optimization Algorithm(FEHOA-GDRNN)is proposed to improve GFRNN performance.In FEHOA-GDRNN,Enhanced Hiking Optimization Algorithm(EHOA)applies a new spider web search to find better solution based on a Mamdani Fuzzy Inference System(FIS).FEHOA-GDRNN is evaluated on 40 imbalanced datasets and its results are compared with GDRNN trained by Fuzzy HOA(FHOA-GDRNN),GFRNN trained by HOA(HOA-GFRNN),GFRNN and its various versions(IGFRNN,I-GFRNN,and EGDRNN),Cost-Sensitive Support Vector Machine with an RBF kernel(CS-SVM-RBF),Cost-Sensitive Support Vector Machine with a Linear kernel(CS-SVM-Linear),Cost-Sensitive Naïve Bayes(CS-NB),Binary Decision tree(BDT),Random Forest(RF),Gaussian-Probabilistic Neural Network(GaussianPNN)and Skew-Probabilistic Neural Network(SkewPNN).Moreover,the results of the proposed classifier are compared with those of several Fuzzy K-Nearest Neighbor(FKNN).The results demonstrate that FEHOA-GDRNN outperforms other methods in key metrics,including Average Accuracy(AAcc)and Geometric Mean(GM). 展开更多
关键词 Imbalanced data Classification Gravitational Fixed Radius Nearest Neighbor(GFRNN) Mamdani Fuzzy Inference System Hiking Optimization Algorithm(HOA)
Efficient Gerchberg–Saxton algorithm deep unrolling for phase retrieval with a complex forward path 认领 引用
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作者 Shengyuan Yan Mike Holenderski Nirvana Meratnia 《Advanced Photonics Nexus》 CSCD 2026年第2期63-89,共27页
Phase retrieval problems occur in a wide range of optical systems characterized by different forward path complexities.The Gerchberg–Saxton algorithm deep unrolling technique is a state-of-the-art phase retrieval met... Phase retrieval problems occur in a wide range of optical systems characterized by different forward path complexities.The Gerchberg–Saxton algorithm deep unrolling technique is a state-of-the-art phase retrieval method.Its inference speed is determined by the complexity of the forward path.We propose FourierGSNet,an efficient Gerchberg–Saxton algorithm deep unrolling method,to achieve faster phase retrieval for applications with high forward path complexities.FourierGSNet does not directly unroll Gerchberg–Saxton iterations with the forward path of the system.Instead,it extracts physics knowledge from unrolled iterations using the Fourier transform as a simplified forward path and injects the knowledge into a cascaded neural network for phase retrieval for the actual system.We evaluated FourierGSNet on three applications with three degrees of complexities:(i)coherent diffractive imaging with Fourier transform as a simple forward path,(ii)near-field X-ray imaging with Fresnel diffraction as a medium-complexity forward path,and(iii)laser beam shaping with the entire simulated optical train as a complex forward path.We compare FourierGSNet with direct unrolling,two fitting methods,and state-of-the-art data-driven methods.Experiments show that FourierGSNet is significantly faster in inference than direct unrolling on highcomplexity applications while achieving equal or higher accuracy than compared methods. 展开更多
关键词 phase retrieval Gerchberg–Saxton algorithm deep algorithm unrolling beam shaping diffractive imaging
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Optimizing Resource Allocation in Blockchain Networks Using Neural Genetic Algorithm 认领 引用
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作者 Malvinder Singh Bali Weiwei Jiang +2 位作者 Saurav Verma Kanwalpreet Kour Ashwini Rao 《Computers, Materials & Continua》 SCIE EI 2026年第2期1580-1598,共19页
In recent years,Blockchain Technology has become a paradigm shift,providing Transparent,Secure,and Decentralized platforms for diverse applications,ranging from Cryptocurrency to supply chain management.Nevertheless,t... In recent years,Blockchain Technology has become a paradigm shift,providing Transparent,Secure,and Decentralized platforms for diverse applications,ranging from Cryptocurrency to supply chain management.Nevertheless,the optimization of blockchain networks remains a critical challenge due to persistent issues such as latency,scalability,and energy consumption.This study proposes an innovative approach to Blockchain network optimization,drawing inspiration from principles of biological evolution and natural selection through evolutionary algorithms.Specifically,we explore the application of genetic algorithms,particle swarm optimization,and related evolutionary techniques to enhance the performance of blockchain networks.The proposed methodologies aim to optimize consensus mechanisms,improve transaction throughput,and reduce resource consumption.Through extensive simulations and real-world experiments,our findings demonstrate significant improvements in network efficiency,scalability,and stability.This research offers a thorough analysis of existing optimization techniques,introduces novel strategies,and assesses their efficacy based on empirical outputs. 展开更多
关键词 Blockchain technology energy efficiency environmental impact evolutionary algorithms optimization
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Design of Consensus Algorithm for UAV Swarm Identity Authentication Based on Lightweight Blockchain 认领 引用
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作者 Yuji Sang Lijun Liu +2 位作者 Long Lv Husheng Wu Hemin Yin 《Computers, Materials & Continua》 SCIE EI 2026年第5期639-663,共25页
Aiming at the challenges of low throughput,excessive consensus latency and high communication complexity in the Practical Byzantine Fault Tolerance(PBFT)algorithm in blockchain networks,its application in identity ver... Aiming at the challenges of low throughput,excessive consensus latency and high communication complexity in the Practical Byzantine Fault Tolerance(PBFT)algorithm in blockchain networks,its application in identity verification for distributed networking of a drone cluster is limited.Therefore,a lightweight blockchainbased identity authentication model for UAV swarms is designed,and a Credit-score and Grouping-mechanism Practical Byzantine Fault Tolerance(CG-PBFT)algorithm is proposed.CG-PBFT introduces a reputation score evaluation mechanism,classifies the reputation levels of nodes in the network,and optimizes the consensus process based on grouping consensus and BLS aggregate signature technology.Experimental results demonstrate that under identical experimental conditions,compared with the PBFT algorithm,CG-PBFT achieves a 250%increase in average throughput,a 70%reduction in average latency,and simultaneous enhancement in security,thus making it more suitable for UAV swarm networks. 展开更多
关键词 UAV swarm network blockchain PBFT consensus algorithm credit score
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Predictive modeling for mechanical properties of cold-rolled strip steel based on random forest regression and whale optimization algorithm 认领 引用
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作者 Hong-Lei Cai Yi-Ming Fang +3 位作者 Le Liu Li-Hui Ren Zhen-Dong Liu Xiao-Dong Zhao 《Journal of Iron and Steel Research International》 SCIE EI CSCD 2026年第3期73-87,共15页
In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method n... In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method named RFR-WOA is developed based on random forest regression(RFR)and whale optimization algorithm(WOA).Firstly,using Pearson and Spearman correlation analysis and Gini coefficient importance ranking on an actual production dataset containing 37,878 samples,22 key variables are selected as model inputs from 112 variables that affect mechanical properties.Subsequently,an RFR-based predictive model for the mechanical properties of cold-rolled strip steel is constructed.Then,with the combination of the coefficient of determination(R2)and root mean square error as the optimization objective,the hyperparameters of RFR model are iteratively optimized using WOA,and better predictive effectiveness is obtained.Finally,the mechanical properties prediction model based on RFR-WOA is compared with models established using deep neural networks,convolutional neural networks,and other methods.The test results on 9469 samples of actual production data show that the model developed present has better predictive accuracy and generalization capability. 展开更多
关键词 Cold-rolled strip steel Mechanical property Predictive modeling Random forest regression Whale optimization algorithm
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Algorithmically Enhanced Data-Driven Prediction of Shear Strength for Concrete-Filled Steel Tubes 认领 引用
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作者 Shengkang Zhang Yong Jin +5 位作者 Soon Poh Yap Haoyun Fan Shiyuan Li Ahmed El-Shafie Zainah Ibrahim Amr El-Dieb 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期374-398,共25页
Concrete-filled steel tubes(CFST)are widely utilized in civil engineering due to their superior load-bearing capacity,ductility,and seismic resistance.However,existing design codes,such as AISC and Eurocode 4,tend to ... Concrete-filled steel tubes(CFST)are widely utilized in civil engineering due to their superior load-bearing capacity,ductility,and seismic resistance.However,existing design codes,such as AISC and Eurocode 4,tend to be excessively conservative as they fail to account for the composite action between the steel tube and the concrete core.To address this limitation,this study proposes a hybrid model that integrates XGBoost with the Pied Kingfisher Optimizer(PKO),a nature-inspired algorithm,to enhance the accuracy of shear strength prediction for CFST columns.Additionally,quantile regression is employed to construct prediction intervals for the ultimate shear force,while the Asymmetric Squared Error Loss(ASEL)function is incorporated to mitigate overestimation errors.The computational results demonstrate that the PKO-XGBoost model delivers superior predictive accuracy,achieving a Mean Absolute Percentage Error(MAPE)of 4.431%and R2 of 0.9925 on the test set.Furthermore,the ASEL-PKO-XGBoost model substantially reduces overestimation errors to 28.26%,with negligible impact on predictive performance.Additionally,based on the Genetic Algorithm(GA)and existing equation models,a strength equation model is developed,achieving markedly higher accuracy than existing models(R2=0.934).Lastly,web-based Graphical User Interfaces(GUIs)were developed to enable real-time prediction. 展开更多
关键词 Asymmetric squared error loss genetic algorithm machine learning pied kingfisher optimizer quantile regression
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MCPSFOA:Multi-Strategy Enhanced Crested Porcupine-Starfish Optimization Algorithm for Global Optimization and Engineering Design 认领 引用
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作者 Hao Chen Tong Xu +2 位作者 Yutian Huang Dabo Xin Changting Zhong 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期494-545,共52页
Optimization problems are prevalent in various fields of science and engineering,with several real-world applications characterized by high dimensionality and complex search landscapes.Starfish optimization algorithm(... Optimization problems are prevalent in various fields of science and engineering,with several real-world applications characterized by high dimensionality and complex search landscapes.Starfish optimization algorithm(SFOA)is a recently optimizer inspired by swarm intelligence,which is effective for numerical optimization,but it may encounter premature and local convergence for complex optimization problems.To address these challenges,this paper proposes the multi-strategy enhanced crested porcupine-starfish optimization algorithm(MCPSFOA).The core innovation of MCPSFOA lies in employing a hybrid strategy to improve SFOA,which integrates the exploratory mechanisms of SFOA with the diverse search capacity of the Crested Porcupine Optimizer(CPO).This synergy enhances MCPSFOA’s ability to navigate complex and multimodal search spaces.To further prevent premature convergence,MCPSFOA incorporates Lévy flight,leveraging its characteristic long and short jump patterns to enable large-scale exploration and escape from local optima.Subsequently,Gaussian mutation is applied for precise solution tuning,introducing controlled perturbations that enhance accuracy and mitigate the risk of insufficient exploitation.Notably,the population diversity enhancement mechanism periodically identifies and resets stagnant individuals,thereby consistently revitalizing population variety throughout the optimization process.MCPSFOA is rigorously evaluated on 24 classical benchmark functions(including high-dimensional cases),the CEC2017 suite,and the CEC2022 suite.MCPSFOA achieves superior overall performance with Friedman mean ranks of 2.208,2.310 and 2.417 on these benchmark functions,outperforming 11 state-of-the-art algorithms.Furthermore,the practical applicability of MCPSFOA is confirmed through its successful application to five engineering optimization cases,where it also yields excellent results.In conclusion,MCPSFOA is not only a highly effective and reliable optimizer for benchmark functions,but also a practical tool for solving real-world optimization problems. 展开更多
关键词 Global optimization starfish optimization algorithm crested porcupine optimizer metaheuristic Gaussian mutation population diversity enhancement
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Lightweight Airborne Vision Abnormal Behavior Detection Algorithm Based on Dual-Path Feature Optimization 认领 引用
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作者 Baixuan Han Yueping Peng +5 位作者 Zecong Ye Hexiang Hao Xuekai Zhang Wei Tang Wenchao Kang Qilong Li 《Computers, Materials & Continua》 SCIE EI 2026年第2期754-784,共31页
Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm,a lightweight multi-category abnormal behavior detection algorithm based on ... Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm,a lightweight multi-category abnormal behavior detection algorithm based on improved YOLOv11n is designed.By integrating multi-head grouped self-attention mechanism and Partial-Conv,a two-way feature grouping fusion module(DFPF)was designed,which carried out effective channel segmentation and fusion strategies to reduce redundant calculations andmemory access.C3K2 module was improved,and then unstructured pruning and feature distillation technologywere used.The algorithmmodel is lightweight,and the feature extraction ability for airborne visual abnormal behavior targets is strengthened,and the computational efficiency of the model is improved.Finally,we test the generalization of the baseline model and the improved model on the VisDrone2019 dataset.The results show that com-pared with the baseline model,the detection accuracy of the final improved model on the airborne visual abnormal behavior dataset is improved from 90.2% to 94.8%,and the model parameters are reduced by 50.9% to meet the detection requirements of high efficiency and high precision.The detection accuracy of the improved model on the Vis-Drone2019 public dataset is 1.3% higher than that of the baseline model,indicating the effectiveness of the improved method in this paper. 展开更多
关键词 YOLOv11 algorithm multi-class abnormal behavior detection feature extraction UAV aerial photography datasets
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Two-stage wind-solar-storage capacity allocation method research for active distribution network based on whale migrating algorithm 认领 引用
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作者 Wu Zheng Ting Tang +1 位作者 Xianggang He Jierui Yang 《Global Energy Interconnection》 EI CSCD 2026年第3期555-566,共12页
With the integration of clean energy,the increasing penetration of distributed power sources,controllable loads,and energy-storage resources in smart grids is causing substantial difficulties in the safe,stable,and co... With the integration of clean energy,the increasing penetration of distributed power sources,controllable loads,and energy-storage resources in smart grids is causing substantial difficulties in the safe,stable,and cost-effective operation of power systems.Existing research often fails to consider the interconnections among optimal dispatching and the distribution capacities of wind,solar,and energy-storage systems(ESSs).This increases the costs and dispatching difficulties.In response to this situation,a two-stage capacity-allocation approach for wind and solar power and storage in an active distribution network(ADN)is proposed in this paper.This approach is founded based on the whale migration algorithm(WMA).First,an optimization dispatch model that considers controllable loads and energy storage is formulated to minimize the dispatch operation costs of the ADN.In this optimization model,the overall cost of the ADN is taken as the objective function.The optimal configuration for wind–solar–storage capacities is obtained through the WMA.Simulation results confirm that the WMA effectively balances the solution accuracy and computational efficiency,while the proposed scheme enhances the economic performance of active distribution grids. 展开更多
关键词 Active distribution network(ADN) Optimization dispatching Capacity allocation Two-stage Whale migration algorithm(WMA)
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Prediction of Asphalt Pavement Rutting Depth Based on Multi-Model Fusion of Stacking Algorithm 认领 引用
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作者 Chenhui Peng Jinbiao Tang Derun Zhang 《Structural Durability & Health Monitoring》 EI 2026年第3期156-171,共16页
Rutting is a serious issue in asphalt pavement,which may reduce the pavement driving quality and safety.Accurately predicting rutting depth is a crucial task in pavement engineering,providing crucial decision support ... Rutting is a serious issue in asphalt pavement,which may reduce the pavement driving quality and safety.Accurately predicting rutting depth is a crucial task in pavement engineering,providing crucial decision support for asphalt pavement design and maintenance.However,accurate prediction of pavement rutting still remains a significant challenge for pavement engineers.This research first selects the loading number,temperature,dynamic modulus,asphalt layer thickness,and base layer type and thickness as candidate features.Data preprocessing,including outlier handling and feature selection,is then performed.Finally,based on the stacking algorithm,a multi-model fusion approach for predicting rutting depth in asphalt pavements is proposed,using ridge regression(RidgeR),K-nearest neighbor(KNN),multilayer perceptron(MLP),and random forest(RF)models as base models,and support vector machine(SVM)as a meta-model.The model is optimized using a Bayesian optimization model.Results demonstrate the feasibility of using correlation analysis for feature selection.Seven features,including axle weight,upper layer temperature,and middle layer modulus,were selected as predictive features.While all the basic models achieved good prediction accuracy,the stacking ensemble model exhibited lower variance and bias,demonstrating superior generalization capability.The asphalt pavement rutting depth prediction method based on the stacking algorithm multi-model fusion proposed in this research can accurately predict the rutting depth. 展开更多
关键词 Rutting prediction asphalt pavement stacking algorithm Bayesian optimization model
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