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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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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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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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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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A Review of Genetic Algorithms:Principles, Procedures, and Applications in Optimization 认领 引用
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作者 M.A.El-Shorbagy 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期138-184,共47页
This paper provides a thorough examination of Genetic Algorithms(GAs),a category of evolutionary computation methods derived from the concepts of natural selection and genetics.The main concept and operational princip... This paper provides a thorough examination of Genetic Algorithms(GAs),a category of evolutionary computation methods derived from the concepts of natural selection and genetics.The main concept and operational principle of GAs are elucidated,highlighting the evolution of populations of candidate solutions across multiple generations to get optimal or near-optimal solutions for complicated problems.The paper delineates the sequential phases of a conventional GA,encompassing problem formulation,solution encoding,initialization of population,fitness evaluation,selection,crossover,mutation,and termination criteria,so offering a coherent framework for comprehending the algorithm’s functionality.Moreover,numerous prominent genetic operators,including crossover and mutation,are examined,highlighting their distinct forms and processes for fostering diversity and exploration within the search space.Also,the paper emphasizes the benefits of GAs,including their capacity to address nonlinear,multimodal,and high-dimensional optimization challenges without necessitating gradient information,along with their adaptability in resolving both continuous and discrete issues.The limitations and constraints of GAs,such as computing expense,parameter optimization,and the risk of premature convergence,are thoroughly analyzed.The paper examines various applications of GAs across fields,including engineering design,control systems,combinatorial optimization,machine learning,operations research,and multi-objective optimization,demonstrating the versatility and practical significance of this evolutionary method.This work establishes a robust basis for scholars and practitioners seeking to implement GAs in intricate optimization challenges.The review indicates that GAs have greatly progressed from Holland’s original formulation to specialized variations,such as real-valued,permutation,and tree-based encodings,each tailored to certain issue categories.The critical study indicates that although classical GAs are proficient in global exploration,their hybridization with local search techniques(memetic algorithms),swarm intelligence(GA-PSO),and surrogate models significantly improves convergence time and solution accuracy.The study highlights ongoing research deficiencies,such as the disparity between theoretical convergence proofs and the actual performance of algorithms,as well as the necessity for systematic recommendations in the design of hybrid algorithms. 展开更多
关键词 Genetic algorithm evolutionary computation global optimization nonlinear optimization computational intelligence optimization techniques
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An Overall Optimization Model Using Metaheuristic Algorithms for the CNN-Based IoT Attack Detection Problem 认领 引用 被引量：1
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作者 Le Thi Hong Van Le Duc Thuan +1 位作者 Pham Van Huong Nguyen Hieu Minh 《Computers, Materials & Continua》 SCIE EI 2026年第4期1934-1964,共31页
Optimizing convolutional neural networks(CNNs)for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy.This study proposes a unified... Optimizing convolutional neural networks(CNNs)for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy.This study proposes a unified and flexible optimization framework that leverages metaheuristic algorithms to automatically optimize CNN configurations for IoT attack detection.Unlike conventional single-objective approaches,the proposed method formulates a global multi-objective fitness function that integrates accuracy,precision,recall,and model size(speed/model complexity penalty)with adjustable weights.This design enables both single-objective and weightedsum multi-objective optimization,allowing adaptive selection of optimal CNN configurations for diverse deployment requirements.Two representativemetaheuristic algorithms,GeneticAlgorithm(GA)and Particle Swarm Optimization(PSO),are employed to optimize CNNhyperparameters and structure.At each generation/iteration,the best configuration is selected as themost balanced solution across optimization objectives,i.e.,the one achieving themaximum value of the global objective function.Experimental validation on two benchmark datasets,Edge-IIoT and CIC-IoT2023,demonstrates that the proposed GA-and PSO-based models significantly enhance detection accuracy(94.8%–98.3%)and generalization compared with manually tuned CNN configurations,while maintaining compact architectures.The results confirm that the multi-objective framework effectively balances predictive performance and computational efficiency.This work establishes a generalizable and adaptive optimization strategy for deep learning-based IoT attack detection and provides a foundation for future hybrid metaheuristic extensions in broader IoT security applications. 展开更多
关键词 Genetic algorithm(GA) particle swarm optimization(PSO) multi-objective optimization convolutional neural network—CNN IoT attack detection metaheuristic optimization CNN configuration
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A Review of Optimization Methods for Pole-shoe Structures in Large-scale Salient Pole Synchronous Motors 认领 引用
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作者 Pengcheng Ma Jinxiu Chen Yiwei Ding 《CES Transactions on Electrical Machines and Systems》 EI CSCD 2026年第1期28-43,共16页
Optimizing the rotor pole-shoe structure of large salient pole synchronous motors is critical for improving their performance and efficiency,allowing for enhanced responsiveness to grid demands and adjustments in oper... Optimizing the rotor pole-shoe structure of large salient pole synchronous motors is critical for improving their performance and efficiency,allowing for enhanced responsiveness to grid demands and adjustments in operating conditions.This paper provides a comprehensive review of various pole-shoe structures for salient pole synchronous motor rotors and their associated optimization techniques.First,it outlines the role of the pole-shoe structure and examines the theoretical theories of key electromagnetic parameters,including the pole-arc coefficient,voltage waveform coefficient,and armature reaction coefficient.Regarding structural design,this paper explores several configurations,including the threesegment arc,five-segment arc,single eccentric pole-arc combined with two chordal surface sections,and asymmetric poles.The effects of these designs on the air-gap magnetic field distribution and voltage waveform are evaluated.In terms of methodology,this paper reviews the application of numerical solutions to electromagnetic field inverse problems and the use of optimization algorithms for electrical machine structural optimization.This study illustrates the application of improved simulated annealing algorithms,tabu search algorithms,and particle swarm optimization algorithms for single-objective optimization of five-segment arc pole-shoe structures.Additionally,this paper discusses the use of vector tabu search and multi-objective quantum evolutionary algorithms for the multi-objective optimization of five-segment arc pole-shoe structures.The study concludes that multi-objective optimization algorithms are underutilized for pole-shoe structure optimization and suggests that multi-objective particle swarm optimization could be more extensively employed for this purpose.Furthermore,the potential application of topology optimization methods for the design of salient-pole synchronous motor rotor magnetic poles is proposed. 展开更多
关键词 Electromagnetic field inverse problem Fivesegment arc pole-shoe Multi-objective optimization Particle swarm optimization Rotor pole-shoe Structural optimization
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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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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://gffzz188fe103f8f1460asfvobxknfq50k6ob6.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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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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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://gffzze767f4cc5ce545d8sfvobxknfq50k6ob6.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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Entry trajectory optimization for cross-domain morphing vehicles using oscillation-avoidance-based multistage trust-region sequential convex programming 认领 引用
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作者 Zheng LI Zheng FANG +1 位作者 Zhenyue JIA Jianqiao YU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第4期310-333,共24页
This paper develops an Oscillation-avoidance-based Multistage Trust-region Sequential Convex Programming(OMTSCP)method for the highly nonlinear entry trajectory optimization problem of Cross-Domain Morphing Vehicles(C... This paper develops an Oscillation-avoidance-based Multistage Trust-region Sequential Convex Programming(OMTSCP)method for the highly nonlinear entry trajectory optimization problem of Cross-Domain Morphing Vehicles(CDMVs).The decoupling of states and controls for complex nonlinear dynamics is achieved by defining new control and state variables.A series of sub convex problems is formulated by successive linearization and discretization of the constraints.The proposed Trust-region Sequential Convex Programming(TSCP)scheme consists of three stages:an initial guess generation stage,a basic solution stage,and an optimal solution stage.An approach to penalize the dynamic relaxation is firstly developed to obtain an initial guess with considerable accuracy and significantly improve the robustness of the algorithm by overcoming the drawbacks of potential artificial infeasibility.The oscillation phenomenon of the TSCP method under rectangular trust region is then investigated,and a novel N-shape-based oscillation identification method is proposed to identify the oscillation accurately.Finally,an oscillation-avoidance method based on the sort trust-region is proposed to improve the convergence of the TSCP algorithm.Numerical comparisons of the proposed method and a typical TSCP method,as well as the morphing and fixed-morphing vehicles are provided to demonstrate the effectiveness and efficiency of the proposed method and the performance advantages of the morphing vehicle.The robustness of the method is further verified by Monte Carlo simulation. 展开更多
关键词 Convex optimization Cross-domain vehicle Entry trajectory optimization Morphing vehicle Multistage Oscillation phenomenon Trust-region sequential convex programming
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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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Real-Time Optimization of Vertical Roller Mills Using XGBoost Prediction and Q-Learning Control 认领 引用
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作者 Anping Wan Yingchang Gao +2 位作者 Weikang Liu Rui Yin Khalil Al-Bukhaiti 《Computers, Materials & Continua》 SCIE EI 2026年第8期1515-1534,共20页
Vertical roller mills are essential for energy-intensive grinding in cement,minerals,and metallurgy industries,consuming up to 50%of plant electricity and frequently experiencing operational instabilities(including ex... Vertical roller mills are essential for energy-intensive grinding in cement,minerals,and metallurgy industries,consuming up to 50%of plant electricity and frequently experiencing operational instabilities(including excessive vibration and main motor current fluctuations)that drive unplanned downtime,increased wear,and reduced throughput.Despite their importance,real-time autonomous optimization remains challenging due to the nonlinear interactions among grinding pressure,feed rate,separator speed,and aerodynamic factors,which limit traditional control strategies under varying loads.This paper presents a real-time operational optimization system for large-scale vertical roller mills using big industrial data and artificial intelligence(AI).From a 5400 kW Loesche LM56.4 mill,2,764,800 samples were collected at 1 Hz over 32 days of continuous production.A systematic pipeline was developed:quartile-based outlier-robust cleaning;domain-informed feature engineering including Total Current;Random Forest(RF)permutation importance selection of the top 15 parameters;and Extreme Gradient Boosting(XGBoost)regression models with hyperparameters tuned by Tree-structured Parzen Estimator(TPE)Bayesian optimization.The resulting models achieved strong predictive performance,Mean Absolute Percentage Error(MAPE)of 1.3%(95%CI:1.1%–1.5%)for main motor current(R2=0.9997)and 5.8%(95%CI:5.3%–6.3%)for shell vibration(R2=0.9717),representing reductions of 89%and 59%,respectively,relative to the Long Short-Term Memory(LSTM)baseline.These surrogates were embedded into a tabular Q-learning Reinforcement Learning(RL)agent that autonomously adjusts feed rate,grinding pressure,separator speed,and exhaust damper position via a discrete action space and multi-objective reward function,communicating with the Distributed Control System(DCS)via Open Platform Communications Unified Architecture(OPC-UA).Closed-loop evaluation yielded simultaneous reductions of 6.0%in peak current(181.92→170.04 A)and 9.4%in peak vibration(5.51→4.99 mm/s)while maintaining throughput.A PyQt5-based graphical interface enabling real-time monitoring,predictive alerts,and automatic DCS write-back was deployed and operated stably for two weeks. 展开更多
关键词 Vertical roller mill operational optimization XGBoost Bayesian hyperparameter optimization Q-learning energy efficiency vibration reduction real-time control Industry 4.0
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Quantum computing-enhanced topology optimization with stress constraints for truss structures 认领 引用
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作者 Yan Wang Dixiong Yang +1 位作者 Zhenzeng Lei Guohai Chen 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第6期41-57,共17页
Quantum computing,leveraging the properties of quantum physics such as quantum superposition and entanglement,possesses the potential for exponential acceleration compared to classical computing.It can significantly e... Quantum computing,leveraging the properties of quantum physics such as quantum superposition and entanglement,possesses the potential for exponential acceleration compared to classical computing.It can significantly enhance solution efficiency in topology optimization and effectively avoid the entrapment in local optima.This paper proposes a hybrid classical-quantum computing framework to solve the stress-constrained topology optimization problem for truss structures.Initially,structural analyses are performed on a classical computer to determine the stresses of truss members.Then,the optimization problem is formulated through incremental updates of member cross-sectional areas to make it compatible with a quantum annealer.The update strategy consists of a directional-control function and a magnitude-control function.By embedding stress constraints directly into the directional-control function,the original optimization problem is reformulated as a quadratic unconstrained binary optimization model suitable for quantum annealing.To realize a balance between solution accuracy and iteration efficiency,a dynamic strategy for adjusting the magnitude of area increments is proposed.Thus,the quantum annealer can effectively achieve the optimal solutions.When only the access time of the quantum processing unit is considered,the results from 2D and 3D examples of truss topology optimization validate the effectiveness of the proposed framework,and demonstrate the great potential of quantum computing in structural optimization. 展开更多
关键词 Topology optimization Truss structures Quantum computing Quantum annealing algorithm Quadratic unconstrained binary optimization problem
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A collaborative optimization design method of platform location and well trajectory for a complex-structure well factory 认领 引用
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作者 WANG Ge GAO Deli HUANG Wenjun 《Petroleum Exploration and Development》 SCIE 2026年第1期261-271,共11页
Using platform-target matching deviation,anti-collision difficulty,trajectory complexity,and total drilling footage as objective functions,and comprehensively considering constraints such as platform layout area,drill... Using platform-target matching deviation,anti-collision difficulty,trajectory complexity,and total drilling footage as objective functions,and comprehensively considering constraints such as platform layout area,drilling extension limits,underground target distribution and trajectory collision risks,a model of platform location-wellbore trajectory collaborative optimization for a complex-structure well factory is developed.A hybrid heuristic algorithm is proposed by combining an improved sparrow search algorithm(ISSA)for optimizing platform parameters in the outer layer and a directed artificial bee colony algorithm(DABC)for optimizing trajectory parameters in the inner layer.The alternating iteration of ISSA-DABC facilitates the resolution of the collaborative optimization problem.The ISSA-DABC provides an effective solution to the platform-trajectory collaborative optimization problem for complex-structure well factories and overcomes the tendency of the traditional platform-trajectory stepwise optimization workflow to become trapped in local optima and yield inconsistent designs.The ISSA-DABC has a strong global search capability,fast convergence and good robustness,and can simultaneously satisfy multiple engineering constraints on drilling footage,trajectory complexity and collision risk,and enables automated,workflow-wide generation of constraint-compliant,near-globally optimal platform-trajectory configurations.Field applications further demonstrate that ISSA-DABC significantly reduces the objective function value and collision risk,yielding more rational platform layouts and well factory design parameters. 展开更多
关键词 complex-structure well factory ISSA DABC platform-trajectory collaborative optimization well factory parameter optimization
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Painted Wolf Optimization:A Novel Nature-Inspired Metaheuristic Algorithm for Real-World Optimization Problems 认领 引用
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作者 Saeid Sheikhi 《Computers, Materials & Continua》 SCIE EI 2026年第5期243-271,共29页
Metaheuristic optimization algorithms continue to be essential for solving complex real-world problems,yet existingmethods often struggle with balancing exploration and exploitation across diverse problem landscapes.T... Metaheuristic optimization algorithms continue to be essential for solving complex real-world problems,yet existingmethods often struggle with balancing exploration and exploitation across diverse problem landscapes.This paper proposes a novel nature-inspired metaheuristic optimization algorithm named the Painted Wolf Optimization(PWO)algorithm.The main inspiration for the PWO algorithm is the group behavior and hunting strategy of painted wolves,also known as African wild dogs in the wild,particularly their unique consensus-based voting rally mechanism,a behavior fundamentally distinct fromthe social dynamics of grey wolves.In this innovative process,pack members explore different areas to find prey;then,they hold a pre-hunting voting rally based on the alpha member to determine who will begin the hunt and attack the prey.The efficiency of the proposed PWO algorithm is evaluated by a comparison study with other well-known optimization algorithms on 33 test functions,including the Congress on Evolutionary Computation(CEC)2017 suite and different real-world engineering design cases.Furthermore,the algorithm’s performance is further tested across a spectrum of optimization problems with extensive unknown search spaces.This includes its application within the field of cybersecurity,specifically in the context of training a machine learning-based intrusion detection system(ML-IDS),achieving an accuracy of 0.90 and an F-measure of 0.9290.Statistical analyses using the Wilcoxon signed-rank test(all p<0.05)indicate that the PWO algorithm outperforms existing state-of-the-art algorithms,providing superior solutions in diverse and unpredictable optimization landscapes.This demonstrates its potential as a robust method for tackling complex optimization problems in various fields.The source code for thePWOalgorithmis publicly available at http://gffzz188fe103f8f1460asfvobxknfq50k6ob6.ffgz.tsg.suse.edu.cn/saeidsheikhi/Painted-Wolf-Optimization. 展开更多
关键词 Optimization painted wolf optimization algorithm metaheuristic algorithm nature-inspired computing swarm intelligence
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Optimization of Renewable Energy Systems:Comparative Analysis of Advanced Algorithms and Photovoltaic-Electrolyzer Performance for Cost Reduction and Efficiency Enhancement 认领 引用
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作者 Mohamed-Amine Babay Mustapha Adar +1 位作者 Ahmed Chebak Mustapha Mabrouki 《电化学（中英文）》 CAS 北大核心 2026年第5期24-41,共18页
The increasing demand for cost-effective and efficient renewable energy solutions presents significant optimization challenges in hybrid energy systems.This paper addresses these challenges by conducting a comparative... The increasing demand for cost-effective and efficient renewable energy solutions presents significant optimization challenges in hybrid energy systems.This paper addresses these challenges by conducting a comparative analysis of three advanced optimization algorithms-Levy Flight Optimization(LFO),Archimedean Optimization(AO),and Quantum Gorilla Optimization(QGO)-to minimize the Total Net Present Cost(TNPC)and Levelized Cost of Energy(LCOE)in hybrid renewable energy systems.The study integrates critical cost parameters such as Capital Expenditure(CAPEX),Operational Expenditure(OPEX),replacement costs,and salvage values into an advanced optimization framework.Three system configurations are evaluated:Wind Turbines and Fuel Cells(WT/FC),Photovoltaic Systems and Fuel Cells(PV/FC),and a combined system(PV/WT/FC),under varying availability levels(100%,96%,and 92%).The results demonstrate that LFO consistently outperforms the other algorithms,achieving the lowest TNPC of$0.051 for the WT/FC system at 96%availability,compared to$0.719 using QGO.These findings underscore the importance of selecting tailored optimization strategies to balance cost,performance,and system reliability.This research provides valuable insights into designing efficient and economically viable renewable energy systems,particularly,for applications requiring consistent high energy output,such as monocrystalline and polycrystalline PV-based configurations. 展开更多
关键词 Energy optimization Levy flight optimization Quantum gorilla optimization Photovoltaic-electrolyzer system Cost parameter
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Optimization of Truss Structures Using Nature-Inspired Algorithms with Frequency and Stress Constraints 认领 引用 被引量：1
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作者 Sanjog Chhetri Sapkota Liborio Cavaleri +3 位作者 Ajaya Khatri Siddhi Pandey Satish Paudel Panagiotis G.Asteris 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期436-464,共29页
Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises stru... Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises structural weight under stress and frequency constraints.Two new algorithms,the Red Kite Optimization Algorithm(ROA)and Secretary Bird Optimization Algorithm(SBOA),are utilized on five benchmark trusses with 10,18,37,72,and 200-bar trusses.Both algorithms are evaluated against benchmarks in the literature.The results indicate that SBOA always reaches a lighter optimal.Designs with reducing structural weight ranging from 0.02%to 0.15%compared to ROA,and up to 6%–8%as compared to conventional algorithms.In addition,SBOA can achieve 15%–20%faster convergence speed and 10%–18%reduction in computational time with a smaller standard deviation over independent runs,which demonstrates its robustness and reliability.It is indicated that the adaptive exploration mechanism of SBOA,especially its Levy flight–based search strategy,can obviously improve optimization performance for low-and high-dimensional trusses.The research has implications in the context of promoting bio-inspired optimization techniques by demonstrating the viability of SBOA,a reliable model for large-scale structural design that provides significant enhancements in performance and convergence behavior. 展开更多
关键词 Optimization truss structures nature-inspired algorithms meta-heuristic algorithms red kite opti-mization algorithm secretary bird optimization algorithm
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