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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)
Memristor devices for next-generation computing:from performance optimization to application-specific co-design 认领 引用 被引量：1
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作者 Zhaorui Liu Caifang Gao +5 位作者 Jingbo Yang Zuxin Chen Enlong Li Jun Li Mengjiao Li Jianhua Zhang 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2026年第1期119-146,共28页
Memristors have emerged as a transformative technology in the realm of electronic devices,offering unique advantages such as fast switching speeds,low power consumption,and the ability to sensor-memory-compute.The app... Memristors have emerged as a transformative technology in the realm of electronic devices,offering unique advantages such as fast switching speeds,low power consumption,and the ability to sensor-memory-compute.The applications span across non-volatile memory,neuromorphic computing,hardware security,and beyond,prompting memristors to become a versatile solution for next-generation computing and data storage systems.Despite enormous potential of memristors,the transition from laboratory prototypes to large-scale applications is challenging in terms of material stability,device reproducibility,and array scalability.This review systematically explores recent advancements in high-performance memristor technologies,focusing on performance enhancement strategies through material engineering,structural design,pulse protocol optimization,and algorithm control.We provide an in-depth analysis of key performance metrics tailored to specific applications,including non-volatile memory,neuromorphic computing,and hardware security.Furthermore,we propose a co-design framework that integrates device-level optimizations with operational-level improvements,aiming to bridge the gap between theoretical models and practical implementations. 展开更多
关键词 memristor performance optimization device design neuromorphic computing
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Optimization of throttling windows to improve flow control of three-way control combiner valves 认领 引用 被引量：1
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作者 Jin-yuan QIAN Zhe-hui MA +7 位作者 Shi-jie LIN Chuang LIU Yu-wei WANG Fei LING Liang ZHANG Man-man CUI Tian-zuo QU Zhi-jiang JIN 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2026年第3期275-287,共13页
Three-way control combiner valves(TCCVs)are critical components used in nuclear power plants to regulate the concentration of boron acid for neutron absorption and reactor safety.However,current TCCV designs often suf... Three-way control combiner valves(TCCVs)are critical components used in nuclear power plants to regulate the concentration of boron acid for neutron absorption and reactor safety.However,current TCCV designs often suffer from suboptimal control performance and high flow resistance,leading to control deviations and reduced operational efficiency.In this paper,a numerical model based on the standard K–ωturbulence model is established and validated against experimental data to analyze the flow characteristics and local flow resistance of a TCCV.A parametric design method for the throttling windows is proposed,establishing relationships between shape parameters and performance indexes,including control performance and flow resistance.The adaptive non-dominated sorting genetic algorithm(ANSGA-II)is used to optimize the shape parameters of the throttling windows.The optimization results show an improvement in the performance indexes of the TCCV,with the adjustable operating range increasing by 31.0%and the maximum local resistance decreasing by 18.3%.We also introduce the concepts of effective and controllable domains to characterize the inlet backflow phenomena and regulation dead zones,which are crucial for ensuring the reliability and effectiveness of control valves.These findings provide insights for enhancing the design and performance of TCCVs in nuclear power plants. 展开更多
关键词 Three-way valve Intersection flow Throttling windows Multi-objective optimization
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Improved Artificial Rabbit Optimization Algorithm Fused with Particle Swarm Optimization for Wireless Sensor Network Coverage Optimization 认领 引用 被引量：1
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作者 WU Jin SU Zhengdong 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第2期375-389,共15页
Aiming at the problem of low node coverage during node deployment in wireless sensor network(WSN),an improved artificial rabbit optimization algorithm incorporating particle swarm optimization(ARO-PSO)is proposed for ... Aiming at the problem of low node coverage during node deployment in wireless sensor network(WSN),an improved artificial rabbit optimization algorithm incorporating particle swarm optimization(ARO-PSO)is proposed for network coverage optimization.ARO-PSO successfully combines the stochastic characteristics of ARO and the global characteristics of PSO.Firstly,to optimize the quality of the initial population,Sine chaos mapping is introduced to initialize the population;secondly,to better balance the exploration and exploitation,adaptive settings are made;finally,combined with the characteristics of the ARO energy factor,a population decreasing strategy is introduced to further accelerate the convergence speed of the algorithm.Experimental and analytical comparisons are made with ARO and PSO and 6 other excellent optimizers on 13 benchmark functions.The results show that ARO-PSO largely outperforms the original algorithm.Finally,ARO-PSO is applied to WSN coverage optimization experiments in 2D and 3D environments,and the proposed algorithm exhibits higher network coverage and improves the monitoring quality of the network compared to standard ARO and PSO and other state-of-the-art algorithms.The experimental results fully demonstrate the superiority of the ARO-PSO-based WSN node deployment optimization method. 展开更多
关键词 wireless sensor network(WSN) swarm intelligence optimization artificial rabbits optimization(ARO) particle swarm optimization(PSO) coverage optimization
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A novel Angle-Constrained Optimization method of Conformal Lattice Structures 认领 引用 被引量：1
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作者 Jun Yan Weibin Xu +2 位作者 Fuhao Wang Sixu Huo Kun Yan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期269-295,共27页
Conformal truss-like lattice structures face significant manufacturability challenges in additive manufac-turing due to overhang angle limitations.To address this problem,we propose a novel angle-constrained optimizat... Conformal truss-like lattice structures face significant manufacturability challenges in additive manufac-turing due to overhang angle limitations.To address this problem,we propose a novel angle-constrained optimization method grounded in the global adjustment of nodal coordinates.First,a build direction is selected to minimize the number of violating struts.Then,an angular-constraint matrix is assembled from strut direction vectors,and analytical sensitivities with respect to nodal coordinates are derived to enable efficient constrained optimization under nonlinear angular inequality constraints.Numerical studies on two complex curved-surface lattices demonstrate that all overhang violations are eliminated while only minor changes are induced in global stiffness and strength.In particular,the maximum displacement of an ergonomic insole varies by only 2.87%after optimization.The results confirm the method’s versatility and engineering robustness,providing a practical approach for additive manufacturing-oriented lattice structure design. 展开更多
关键词 Conformal lattice structures additive manufacturing structural optimization complex structures
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Structural optimization of stress-bearing structures of nearly incompressible problems under design-dependent pressure loads 认领 引用 被引量：1
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作者 T.T.BANH N.T.Y.NGUYEN +1 位作者 H.P.BAN D.LEE 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2026年第4期905-926,共22页
An efficient and innovative method is presented for the stress-related structural topology optimization(TO)in coupled mechanical-pressure systems by leveraging flexible polygonal meshes.With a polytopal composite fini... An efficient and innovative method is presented for the stress-related structural topology optimization(TO)in coupled mechanical-pressure systems by leveraging flexible polygonal meshes.With a polytopal composite finite element approach,the volumetric locking in nearly incompressible materials is reduced.A fluid-flow-based model is built,in which a design-dependent pressure variable is introduced to capture the loading conditions within the system.The P-norm approach consolidates the stress metrics into a global measure,while the clustered regional scaling and adaptive techniques enhance the solutions for stress-limited cases.The primary contributions of this work include a novel framework for addressing the stress challenges in coupled mechanical-pressure systems via flow-based modeling,the adaptability to both compressible and nearly incompressible materials,and the compatibility with diverse mesh types,including triangular,quadrilateral,and polygonal elements.The numerical examples demonstrate,for the first time,optimized topologies for nearly incompressible materials under stress constraints in coupled mechanical-pressure environments,emphasizing the unique strength of this approach. 展开更多
关键词 topology optimization(TO) stress-related problem design-dependent load near incompressibility mechanical-pressure system polytopal composite finite element
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OptimizationDesign and Numerical Simulation of Variable Tube Diameter Heat Exchanger for Split Air Conditioning Indoor Unit 认领 引用 被引量：1
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作者 Zheming Cheng Xinping Ou Yang +2 位作者 Leren Tao Zihao Wang Ke Sun 《Frontiers in Heat and Mass Transfer》 EI CAS 2026年第1期288-313,共26页
Energy shortage has become one of themost concerning issues in the world today,and improving energy utilization efficiency is a key area of research for experts and scholars worldwide.Small-diameter heat exchangers of... Energy shortage has become one of themost concerning issues in the world today,and improving energy utilization efficiency is a key area of research for experts and scholars worldwide.Small-diameter heat exchangers offer advantages such as reduced material usage,lower refrigerant charge,and compact structure.However,they also face challenges,including increased refrigerant pressure drop and smaller heat transfer area inside the tubes.This paper combines the advantages and disadvantages of both small and large-diameter tubes and proposes a combined-diameter heat exchanger,consisting of large and small diameters,for use in the indoor units of split-type air conditioners.There are relatively few studies in this area.In this paper,A theoretical and numerical computation method is employed to establish a theoretical-numerical calculation model,and its reliability is verified through experiments.Using this model,the optimal combined diameters and flow path design for a combined-diameter heat exchanger using R32 as the working fluid are derived.The results show that the heat transfer performance of all combined diameter configurations improves by 2.79%to 8.26%compared to the baseline design,with the coefficient of performance(COP)increasing from 4.15 to 4.27~4.5.These designs can save copper material,but at the cost of an increase in pressure drop by 66.86%to 131.84%.The scheme IIIH,using R32,is the optimal combined-diameter and flow path configuration that balances both heat transfer performance and economic cost. 展开更多
关键词 Split air condition variable tube diameter enhanced heat transfer numerical simulation structural design optimization
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An Efficient Evolutionary Algorithm for Few-for-Many Optimization 认领 引用
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作者 Ke Shang Hisao Ishibuchi +1 位作者 Zexuan Zhu Qingfu Zhang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1362-1377,共16页
Few-for-many(F4M)optimization,recently introduced as a novel paradigm in multi-objective optimization,aims to find a small set of solutions that effectively handle a large number of conflicting objectives.Unlike tradi... Few-for-many(F4M)optimization,recently introduced as a novel paradigm in multi-objective optimization,aims to find a small set of solutions that effectively handle a large number of conflicting objectives.Unlike traditional many-objective optimization methods,which typically attempt comprehensive coverage of the Pareto front,F4M optimization emphasizes finding a small representative solution set to efficiently address highdimensional objective spaces.Motivated by the computational complexity and practical relevance of F4M optimization,this paper proposes a new evolutionary algorithm explicitly tailored for efficiently solving F4M optimization problems.Inspired by Smetric selection evolutionary multi-objective optimization algorithm(SMS-EMOA),our proposed approach employs a(μ+1)-evolution strategy guided by the objective of F4M optimization.Furthermore,to facilitate rigorous performance assessment,we propose a novel benchmark test suite specifically designed for F4M optimization by leveraging the similarity betw een the R2indicator and F4M formulations.Our test suite is highly flexible,allowing any existing multi-objective optimization problem to be transformed into a corresponding F4M instance via scalarization using the weighted Tchebycheff function.Comprehensive experimental evaluations on benchmarks demonstrate the superior performance of our algorithm compared to existing state-of-the-art algorithms,especially on instances involving a large number of objectives.The source code of the proposed algorithm will be released publicly.Source code is available at http://gffzz188fe103f8f1460asn5qpxbk9u05o6nou.ffgz.tsg.suse.edu.cn/MOL-SZU/SoM-EMOA. 展开更多
关键词 Evolutionary algorithm few-for-many optimization many-objective optimization (MOO) multi-objective optimization
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Integrated topology optimization method for crashworthiness of metal-FRP hybrid thin-walled tubes:A review and analysis 认领 引用
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作者 Lele Zhang Yanzhao Guo +2 位作者 Zhizhong Cheng Weiyuan Dou Sebastian Stichel 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第1期508-525,共18页
Based on the demands for crashworthiness and lightweight in the passive safety of transportation vehicles,metal-fiber reinforced polymer(FRP)hybrid thin-walled tubes(MFHTWTs)integrate the toughness,strength and lightw... Based on the demands for crashworthiness and lightweight in the passive safety of transportation vehicles,metal-fiber reinforced polymer(FRP)hybrid thin-walled tubes(MFHTWTs)integrate the toughness,strength and lightweight of two distinct material characteristics.MFHTWTs can achieve energy absorption through the coupling of material plastic deformation and fracture,demonstrating significant engineering value in passive safety.This review provides a comprehensive examination of the crashworthiness topology optimization of MFHTWTs,aiming to demonstrate that a deeply integrated approach combining topology and parameter opti-mization can realize an optimal design method for MFHTWTs,thereby maximizing the functional utilization of limited material.Firstly,the review highlights the crashworthiness topology optimization methods(CTOMs)based on thin-walled structures.With a particular focus on metal,the review discusses both the practical ap-plicability and limitations of CTOMs under crash conditions.Additionally,based on the methodology of the equivalent static load method(ESLM),the review emphasizes that topology optimization methods considering continuous fiber paths and multi-material interface connections are also applicable to the crashworthiness op-timization of MFHTWTs.Furthermore,to couple structural parameters and configuration characteristics,in-tegrated topology optimization methods,including parameter optimization,are proposed to provide a valuable reference for the global optimization of MFHTWTs.Thus,these methods can establish the mapping relationship between key parameters and the structural energy absorption capacity. 展开更多
关键词 Metal-FRP hybrid thin-walled tube Topology optimization Parameter optimization Crashworthiness Integrated optimization scheme
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A satellite layout-structure integrated optimization method based on thermal metamaterials 认领 引用
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作者 Senlin HUO Bingxiao DU +2 位作者 Wei CONG Yong ZHAO Xianqi CHEN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第2期328-340,共13页
In the conceptual design phase of the satellite thermal management system,components layout optimization and structural topology optimization of satellite panel can meet global and local thermal management requirement... In the conceptual design phase of the satellite thermal management system,components layout optimization and structural topology optimization of satellite panel can meet global and local thermal management requirements,respectively.However,achieving non-interfering coupling between these two optimization processes remains a challenge.An integrated layout-structure design method based on thermal metamaterials is proposed,which comprises two design stages.In the first stage,components layout optimization is conducted to maximize temperature uniformity within the satellite module,yielding a globally optimized layout with balanced thermal characteristics.In the second stage,topology optimization guided by the design principle of thermal metamaterials is implemented in critical local panel regions to satisfy differentiated heat transfer requirements of components with diverse functional and thermal sensitivity properties.The key innovation lies in utilizing thermal metamaterials as a mediator to synergistically couple global components layout optimization with local structural topology optimization,which enables customized local heat flux manipulation without interfering with the globally optimized temperature field derived from the layout optimization.The method introduces neither additional mass nor special materials,offering advantages of low cost,high reliability,and strong versatility.It provides a new solution paradigm for the design of passive thermal management systems in satellites. 展开更多
关键词 Layout optimization Metamaterials Satellites Structure design Thermal management Topology optimization
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Optimization of the frequency offset increment of FDA-MIMO based on cuckoo search algorithm 认领 引用 被引量：1
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作者 WANG Bo ZHAO Yu +2 位作者 LI Yonglin YANG Rennong XUE Junjie 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第1期157-170,共14页
Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic e... Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic environments.The effectiveness of interference suppression by FDA-MIMO is limited by the inherent range-angle coupling issue in the FDA beampattern.Existing literature primarily focuses on control methods for FDA-MIMO radar beam direction under the assumption of static beampatterns,with insufficient exploration of techniques for managing nonstationary beam directions.To address this gap,this paper initially introduces the FDA-MIMO signal model and the calculation formula for the FDA-MIMO array output using the minimum variance distortionless response(MVDR)beamformer.Building on this,the problem of determining the optimal frequency offset for the FDA is rephrased as a convex optimization problem,which is then resolved using the cuckoo search(CS)algorithm.Simulations confirm the effectiveness of the proposed approach,showing that the frequency offsets obtained through the CS algorithm can create a dot-shaped beam direction at the target location while effectively suppressing interference signals within the mainlobe. 展开更多
关键词 frequency diverse array multiple-input multiple-output(FDA-MIMO) convex optimization cuckoo search algorithm beampattern
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Optimal abort guidance and online trajectory optimization algorithm for Mars vehicles 认领 引用
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作者 Yuan LI Huaiyi WANG +3 位作者 Tuo HAN Qinglei HU Yueyang LIU Dongyu LI 《Science China(Technological Sciences)》 SCIE EI CAS CSCD 2026年第7期263-277,共15页
When the Mars vehicles encounter a storm or other failure situations,the ability to abort and rise to a predetermined orbit in the powered descent flight phase is critical.Unlike the abort guidance mission of lunar ex... When the Mars vehicles encounter a storm or other failure situations,the ability to abort and rise to a predetermined orbit in the powered descent flight phase is critical.Unlike the abort guidance mission of lunar exploration,Mars has an atmosphere and stronger gravity,leading to high requirements on the efficiency,autonomy,reliability,and robustness of the guidance system.To this end,this paper proposes a safety abort guidance algorithm to stop the descent of the Mars vehicle with fast responses in the initial stage of abort.Then,to meet the requirements of rendezvous and docking with the orbiter,the constraints of terminal orbit parameters and orbit insertion time and position in the perifocal coordinate system are explicitly defined.To achieve a higher terminal orbit,a“powered-coast-powered”trajectory onboard optimization problem is established,which allows the trajectory optimization problem to be handled by convex optimization rapidly and accurately.Moreover,a switching strategy between the online trajectory optimization and terminal iterative guidance is detailed to ensure the accuracy of terminal orbit insertion.Finally,numerical experiments with Monte Carlo tests under various conditions are conducted to verify the efficiency,robustness,and onboard application performance of the proposed method. 展开更多
关键词 abort guidance trajectory optimization convex optimization Mars vehicles
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Review of Metaheuristic Optimization Techniques for Enhancing E-Health Applications 认领 引用
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作者 Qun Song Chao Gao +3 位作者 Han Wu Zhiheng Rao Huafeng Qin Simon Fong 《Computers, Materials & Continua》 SCIE EI 2026年第2期185-233,共49页
Metaheuristic algorithms,renowned for strong global search capabilities,are effective tools for solving complex optimization problems and show substantial potential in e-Health applications.This review provides a syst... Metaheuristic algorithms,renowned for strong global search capabilities,are effective tools for solving complex optimization problems and show substantial potential in e-Health applications.This review provides a systematic overview of recent advancements in metaheuristic algorithms and highlights their applications in e-Health.We selected representative algorithms published between 2019 and 2024,and quantified their influence using an entropy-weighted method based on journal impact factors and citation counts.CThe Harris Hawks Optimizer(HHO)demonstrated the highest early citation impact.The study also examined applications in disease prediction models,clinical decision support,and intelligent health monitoring.Notably,the Chaotic Salp Swarm Algorithm(CSSA)achieved 99.69% accuracy in detecting Novel Coronavirus Pneumonia.Future research should progress in three directions:improving theoretical reliability and performance predictability in medical contexts;designing more adaptive and deployable mechanisms for real-world systems;and integrating ethical,privacy,and technological considerations to enable precision medicine,digital twins,and intelligent medical devices. 展开更多
关键词 Metaheuristic optimization E-Health disease diagnosis medical resource optimization complex optimization
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Deep Reinforcement Learning Based on Search Space Independent Operators for Black-Box Continuous Optimization 认领 引用
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作者 Ye Tian Yisai Liu +1 位作者 Shangshang Yang Xingyi Zhang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第4期913-925,共13页
Deep reinforcement learning(DRL)has demonstrated exceptional capabilities in combinatorial optimization,which automatically devises policies for solution construction and optimizer refinement.DRL is particularly adept... Deep reinforcement learning(DRL)has demonstrated exceptional capabilities in combinatorial optimization,which automatically devises policies for solution construction and optimizer refinement.DRL is particularly adept in generating training samples by itself,thereby providing the flexibility to solve a variety of combinatorial optimization problems without supervision.While DRL takes actions according to states extracted from problem-specific information,it cannot be directly applied to black-box continuous optimization lacking explicit information.To address this issue,this paper proposes a search space independent operator based DRL method for black-box continuous optimization.It conceptualizes the optimization process driven by search space independent operators as a Markov decision process,wherein actions are defined as operators and states are extracted from solutions generated by operators.In contrast to other DRLassisted metaheuristics,the proposed method does not rely on any existing metaheuristic.Instead,it innovates by creating totally new operators,able to surpass the performance boundaries of existing metaheuristics.Compared with state-of-the-art metaheuristics and DRL methods,the proposed method shows significantly faster convergence speed on challenging continuous optimization problems. 展开更多
关键词 Black-box optimization continuous optimization metaheuristic reinforcement learning search operator
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Theory Evolution Optimization:A Metaheuristic Algorithm BaSed on Evolution Process of Theory 认领 引用
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作者 Jiacong Liu Jiaze Tu +5 位作者 Chunguang Bi Huiling Chen Ali Asghar Heidari Hao Xie Lei Liu Yi Chen 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第2期1015-1060,共46页
Metaheuristic algorithms have emerged as indispensable tools for solving NP-hard optimization problems that defy traditional methods.To advance the field’s focus on algorithmic performance,this study introduces the T... Metaheuristic algorithms have emerged as indispensable tools for solving NP-hard optimization problems that defy traditional methods.To advance the field’s focus on algorithmic performance,this study introduces the Theory Evolution Optimization(TEO)–an efficient metaheuristic inspired by the evolution of scientific theory.TEO simulates the competitive,accumulative,and replacement processes among scientific hypotheses,mirroring the evolution from a hypothesis to an established scientific theory.The performance of TEO is validated through extensive experimental simulations and benchmarked against 28 popular algorithms,including highly competitive champions such as EBOwithCMAR,LSHADE_cnEpSi,and LSHADE.Pairwise comparisons between TEO and the latest algorithms are conducted using the Wilcoxon signed-rank test,with multiple comparisons managed by the Friedman test.Initially,TEO is tested on the classical IEEE CEC2017 and the latest IEEE CEC2022 benchmark functions.TEO successfully addresses four prominent engineering design problems in constrained continuous space for practical applications.Additionally,a binary TEO(BTEO)variant is introduced and applied to feature selection tasks in discrete space.Experimental results consistently demonstrate that TEO proposes highly competitive outcomes in optimization problems.The source codes for this research are accessible to the public at http://gffzze767f4cc5ce545d8sn5qpxbk9u05o6nou.ffgz.tsg.suse.edu.cn/TEO.html. 展开更多
关键词 Optimization,metaheuristic algorithms Evolution of scientific theory Theory evolution optimizers Engineering design optimization Feature selection
Robust powered descent guidance considering mass and fuel consumption uncertainties:A convex optimization approach 认领 引用
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作者 Duozhi GAO Yanning GUO +3 位作者 Edoardo FADDA Youmin GONG Chuanjiang LI Paolo BRANDIMARTE 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第3期546-570,共25页
This paper addresses the challenge of mass uncertainty during the powered descent phase of a Mars lander and proposes a robust powered descent guidance algorithm that accounts for uncertainties in mass and fuel consum... This paper addresses the challenge of mass uncertainty during the powered descent phase of a Mars lander and proposes a robust powered descent guidance algorithm that accounts for uncertainties in mass and fuel consumption.First,the traditional trajectory optimization method based on convex optimization is improved by developing a fast and accurate solution approach using sequential convex optimization.Second,the effects of mass uncertainty on position are modeled and analyzed,with corresponding computational methods provided for different scenarios.Third,the worst-case scenario under mass uncertainty is analyzed through both geometric and theoretical approaches,and a modified glide-slope constraint method is proposed to ensure safe landing even in adverse conditions.Moreover,a closed-loop receding horizon based guidance is developed to further mitigate the effects of mass uncertainty and improve terminal landing accuracy.Finally,the proposed improved convex optimization algorithm and robust trajectory optimization algorithm are validated through simulation cases and compared with a probabilistic approach.The simulations further test various initial positions,velocities,and glide-slope angles,demonstrating that the solutions are both accurate and robust. 展开更多
关键词 Uncertainty Trajectory optimization Convex optimization Powered descent Closed-loop guidance
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Multi-objective topology optimization for cutout design in deployable composite thin-walled structures 认领 引用
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作者 Hao JIN Ning AN +3 位作者 Qilong JIA Chun SHAO Xiaofei MA Jinxiong ZHOU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第1期674-694,共21页
Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structu... Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:http://gffzz188fe103f8f1460asn5qpxbk9u05o6nou.ffgz.tsg.suse.edu.cn/jinhao-ok1/Topo-for-DCTWS.git. 展开更多
关键词 Composite laminates Deployable structures Multi-objective optimization Thin-walled structures Topology optimization
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Novel Sea Otter Optimization Algorithm for WSN Coverage Intelligence Optimization 认领 引用
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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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An Optimal Acceleration Control for Collision Avoidance in VANETs Using Convex Optimization 认领 引用
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作者 Awais Ahmad Fakhri Alam Khan +4 位作者 Awais Ahmad Gautam Srivastava Syed Atif Moqurrab Abdul Razaque Dina S.M.Hassan 《Computers, Materials & Continua》 SCIE EI 2026年第6期551-581,共31页
Collision avoidance is recognized as a critical challenge in Vehicular Ad-Hoc Networks(VANETs),which demand real-time decision-making.It plays a vital role in ensuring road safety and traffic efficiency.Traditional ap... Collision avoidance is recognized as a critical challenge in Vehicular Ad-Hoc Networks(VANETs),which demand real-time decision-making.It plays a vital role in ensuring road safety and traffic efficiency.Traditional approaches like rule-based systems and heuristic methods fail to provide optimal solutions in dynamic and unpredictable traffic scenarios.They cannot balance multiple objectives like minimizing collision risk,ensuring passenger comfort,and optimizing fuel efficiency,leading to suboptimal performance in real-world conditions.To tackle collision avoidance,this paper introduces a novel approach by defining the issue as an optimal control problem and solving it using the Convex Optimization model,which results in autonomous braking and acceleration control in smart vehicles.The model begins by collecting real-time vehicle data from its environment,which includes speed,position,and distance.It then uses this information to generate optimal driving profiles for each vehicle and calculate optimal responses.The informational data guides the vehicle to adjust its braking and acceleration autonomously,ensuring that safe distances are maintained among vehicles without compromising comfort.This approach empowers individual vehicles to make real-time decisions independently and adapt swiftly to changing traffic conditions.The performance of the proposed method is evaluated using a simulation study in MATLAB using the CVX optimization toolbox,comparing Adaptive Cruise Control(ACC)and Fuzzy Logic-Based Collision Avoidance(FLCA)using various performance metrics.Compared to these baseline protocols,our approach achieves a higher success rate in preventing collisions by maintaining smoother acceleration profiles and lower fuel consumption,yielding up to a 32%reduction in collision likelihood and a 19%improvement in fuel efficiency.Additionally,unlike existing rule-based and model-predictive approaches,the proposed method introduces a unified cost function that jointly minimizes collision risk,acceleration jerk,and energy consumption in decentralized VANET scenarios.This integration enables real-time decision-making while maintaining safety,comfort,and efficiency across varying traffic densities. 展开更多
关键词 Vehicular ad hoc networks(VANETs) collision avoidance convex optimization autonomous vehicles optimal control
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Concurrent topology and fiber distribution optimization of continuous fiber-reinforced polymer(CFRP)structures under thermal-mechanical coupling 认领 引用
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作者 Yongjia Dong Hongling Ye +1 位作者 Jicheng Li Sujun Wang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第5期326-346,共21页
Continuous fiber-reinforced polymers(CFRPs)have been extensively utilized in aerospace industries,making it imperative for CFRP structural optimization to consider the effects of extreme service environments.In this p... Continuous fiber-reinforced polymers(CFRPs)have been extensively utilized in aerospace industries,making it imperative for CFRP structural optimization to consider the effects of extreme service environments.In this paper,a thermalmechanical coupling concurrent topology and fiber distribution optimization(TM-CTFDO)method is proposed,specifically tailored for CFRP structures subjected to extreme environmental conditions.The mapping relationships between fiber design variables and material properties are deduced based on the rule of mixture to realize the analysis of thermoelastic CFRP structures.The integrated optimization model for CFRP structures is established with minimizing structural compliance,adhering to the volume constraints of structural and fiber under mechanical and temperature loads.Sensitivity analysis and optimization solution are realized by adopting the adjoint method and the method of moving asymptotes,respectively.This approach culminates in the determination of the optimal topology,fiber orientation,and content.In the post-processing phase,a fiber path planning algorithm is investigated to achieve the continuous fiber path based on the optimization results,which also effectively controls the distribution of dense and sparse fiber.Several examples under uniform and varying temperature fields are provided to verify the effectiveness of the TM-CTFDO method.The influence of temperature and mechanical loads on the optimization results is discussed,which will provide guidance on CFRP structural design and fiber path planning under thermal-mechanical coupling. 展开更多
关键词 Continuous fiber-reinforced polymers Topology optimization Fiber distribution optimization Thermalmechanical coupling Continuous fiber path planning
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