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A Linear Domain System Identification for Small Unmanned Aerial Rotorcraft Based on Adaptive Genetic Algorithm 认领 引用 被引量：13
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作者 Xusheng Lei Yuhu Du 《Journal of Bionic Engineering》 SCIE EI 2010年第2期142-149,共8页
This paper proposes a new adaptive linear domain system identification method for small unmanned aerial rotorcraft.Byusing the flash memory integrated into the micro guide navigation control module,system records the ... This paper proposes a new adaptive linear domain system identification method for small unmanned aerial rotorcraft.Byusing the flash memory integrated into the micro guide navigation control module,system records the data sequences of flighttests as inputs(control signals for servos)and outputs(aircraft’s attitude and velocity information).After data preprocessing,thesystem constructs the horizontal and vertical dynamic model for the small unmanned aerial rotorcraft using adaptive geneticalgorithm.The identified model is verified by a series of simulations and tests.Comparison between flight data and the one-stepprediction data obtained from the identification model shows that the dynamic model has a good estimation for real unmannedaerial rotorcraft system.Based on the proposed dynamic model,the small unmanned aerial rotorcraft can perform hovering,turning,and straight flight tasks in real flight tests. 展开更多
关键词 small unmanned aerial rotorcraft dynamic space model model identification adaptive genetic algorithm
Electro-Hydraulic Servo System Identification of Continuous Rotary Motor Based on the Integration Algorithm of Genetic Algorithm and Ant Colony Optimization 认领 引用 被引量：1
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作者 王晓晶 李建英 +1 位作者 李平 修立威 《Journal of Donghua University(English Edition)》 EI CAS 2012年第5期428-433,共6页
In order to increase the robust performance of electro-hydraulic servo system, the system transfer function was identified by the intergration algorithm of genetic algorithm and ant colony optimization(GA-ACO), which ... In order to increase the robust performance of electro-hydraulic servo system, the system transfer function was identified by the intergration algorithm of genetic algorithm and ant colony optimization(GA-ACO), which was based on standard genetic algorithm and combined with positive feedback mechanism of ant colony algorithm. This method can obtain the precise mathematic model of continuous rotary motor which determines the order of servo system. Firstly, by constructing an appropriate fitness function, the problem of system parameters identification is converted into the problem of system parameter optimization. Secondly, in the given upper and lower bounds a set of optimal parameters are selected to meet the best approximation of the actual system. And the result shows that the identification output can trace the sampling output of actual system, and the error is very small. In addition, another set of experimental data are used to test the identification result. The result shows that the identification parameters can approach the actual system. The experimental results verify the feasibility of this method. And it is fit for the parameter identification of general complex system using the integration algorithm of GA-ACO. 展开更多
关键词 continuous rotary motor system identification genetic algorithm and ant colony optimization （GA-ACO） 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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Application of Genetic Algorithms in Identification ofLinear Time-Varying System 认领 引用 被引量：4
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作者 Zhichun Mu KeLiu +4 位作者 Zichao Wang Datai Yu D. Koshal D. Pearce Information Engineering School, University of Science & Technology Beijing, Beijing 100083, China School of Engineering, University of Brighton, Brighton, UK 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS 2000年第1期58-62,共5页
By applying genetic algorithms (GA) to on-line identification of linear time-varying systems; a number of modifications are made to the Simple Genetic Algorithm to improve the performance of the algorithm in identific... By applying genetic algorithms (GA) to on-line identification of linear time-varying systems; a number of modifications are made to the Simple Genetic Algorithm to improve the performance of the algorithm in identification applications. The simulation results indicate that the method is not only capable of following the changing parameters of the system, but also has improved the identification accuracy compared with that using the least square method. 展开更多
关键词 genetic algorithm, system identification linear system
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Experience-guided optimization of jacket foundations for offshore wind turbines in varying water depths based on finite element analysis and the genetic algorithm 认领 引用
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作者 Jiajia HUANG Tao JIN +6 位作者 Jianwu HUANG Shasha SONG Wei DAI Chaoqun ZUO Lizhong WANG Lilin WANG Zhen GUO 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2026年第3期183-199,共17页
Structural optimization plays a crucial role in reducing the cost of offshore wind power,particularly in deep-water regions where the weight of jacket foundations increases substantially.However,there is ongoing debat... Structural optimization plays a crucial role in reducing the cost of offshore wind power,particularly in deep-water regions where the weight of jacket foundations increases substantially.However,there is ongoing debate regarding the water-depth range that is suitable for jacket foundations,and the threshold where floating foundations become more viable.Existing studies have not quantitatively analyzed how water depth affects jacket foundation mass,and have often struggled to handle the high dimensionality and stringent constraints inherent in jacket foundation optimization problems.In this study,we propose an optimization framework that couples parametric finite element analysis with a genetic algorithm to minimize the mass of jacket foundations based on three actual engineering projects at varying water depths.A novel population initialization strategy incorporating engineering experience-based solutions is introduced to improve convergence efficiency and solution quality.Comparative analysis against preliminary designs and existing offshore wind projects demonstrates the model’s ability to achieve cost-effective solutions,specifically reducing required jacket masses by 18.66%,20.98%,and 17.22%at depths of 30.06,60.23,and 89.81 m,respectively.The results reveal a 122.94%increase in jacket mass—from 1431.28 to 3190.90 t—as water depth increases from 30.06 to 89.81 m.The jacket foundation demonstrates superior cost effectiveness in shallow to moderate water depths,as the unit weight per megawatt(MW)of floating foundations is 97.51%and 35.74%higher at water depths of 60.23 and 89.81 m,respectively.Accordingly,the applicable water-depth threshold between the jacket and floating foundations is estimated to be approximately 100 m.The proposed optimization model offers a novel methodology and practical insights for the optimal design of offshore wind turbine support structures in varying marine environments. 展开更多
关键词 Structural optimization Jacket foundation Genetic algorithm Offshore wind power Population initialization Parametric modeling
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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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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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PID Steering Control Method of Agricultural Robot Based on Fusion of Particle Swarm Optimization and Genetic Algorithm 认领 引用 被引量：4
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作者 ZHAO Longlian ZHANG Jiachuang +2 位作者 LI Mei DONG Zhicheng LI Junhui 《农业机械学报》 EI CAS CSCD 北大核心 2026年第1期358-367,共10页
Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion... Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots. 展开更多
关键词 agricultural robot steering PID control particle swarm optimization algorithm genetic algorithm
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Mimicry of whole-body blood circulation through genetic algorithm in reduced-order hemodynamic model 认领 引用
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作者 Minghao Liao Taoping Bai +2 位作者 Ming Zhang Zhongyou Li Wentao Jiang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第3期1-14,共14页
Physics-based reduced-order hemodynamic models have garnered significant interest because of their ability to capture whole-body cardiovascular fluctuations.However,coordinating the numerous interdependent parameters ... Physics-based reduced-order hemodynamic models have garnered significant interest because of their ability to capture whole-body cardiovascular fluctuations.However,coordinating the numerous interdependent parameters within these models remains a long-standing challenge,and the demand for the personalization of these models persists.We constructed a complex whole-body model of blood circulation(containing the heart,arterial trunk,and branches)and utilized genetic algorithms to automatically and efficiently coordinate the model parameters.Additionally,we introduced a“pseudo-distance”metric by updating the derivative dynamic time-warping algorithm to evaluate the similarity between the simulated waveforms and the target waveforms.After 40 rapid iterations,a complete match was achieved with the target in terms of the blood pressure and flow waveforms amplitude as well as the time domain,resulting in highly realistic waveform mimicry(i.e.,the pseudo-distance approached zero).This model takes about 40 min,far less than the manual modeling that usually takes several months.These results indicate that GAs significantly improve the modeling efficiency of reduced-order models,thus lowering the user threshold. 展开更多
关键词 Reduced-order model Genetic algorithm Hemodynamics Cardiovascular Pseudo-distance
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A Hybrid Mashup Platform Based on Structured and Unstructured Peer-to-Peer Networks Empowered with Genetic Algorithms 认领 引用
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作者 Osama Al-Haj Hassan Ammar Odeh +1 位作者 Abdullah Aref Ghassan Samara 《Computers, Materials & Continua》 SCIE EI 2026年第9期1335-1357,共23页
Mashups are among the key web technologies that provide end-users with customizable and personalized tools.Most mashup platforms are based on centralized architectures or do not employ fully decentralized architecture... Mashups are among the key web technologies that provide end-users with customizable and personalized tools.Most mashup platforms are based on centralized architectures or do not employ fully decentralized architectures;therefore,in this paper,we propose a decentralized architecture for mashups that combines the strengths of structured and unstructured peer-to-peer networks.For the structured part,we rely on the Chord lookup protocol,and for the unstructured part,we build groups of nodes via two flavors of network flooding,namely,sequence number flooding and reverse path flooding.Brokers in the unstructured part would be responsible for hosting and executing mashups,such that deciding which brokers should host a given mashup is determined by utilizing genetic algorithms.We compare our work against several approaches that rely on random and greedy mashup placement.We also assess our proposed approach to pure structured and pure unstructured approaches.We evaluate our system using simulations,and results show that executing mashups using the version of our scheme that relies on reverse path flooding generates at least 25%lower delays than the other approaches. 展开更多
关键词 Mashup peer to peer Chord genetic algorithm
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Optimization Configuration Method for Grid-Side Grid-Forming Energy Storage System Based on Genetic Algorithm 认领 引用 被引量：1
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作者 Yuqian Qi Yanbo Che +2 位作者 Liangliang Liu Jiayu Ni Shangyuan Zhang 《Energy Engineering》 EI 2025年第10期3999-4017,共19页
The process of including renewable energy sources in power networks is moving quickly,so the need for innovative configuration solutions for grid-side ESS has grown.Among the new methods presented in this paper is GA-... The process of including renewable energy sources in power networks is moving quickly,so the need for innovative configuration solutions for grid-side ESS has grown.Among the new methods presented in this paper is GA-OCESE,which stands for Genetic Algorithm-based Optimization Configuration for Energy Storage in Electric Networks.This is one of the methods suggested in this study,which aims to enhance the sizing,positioning,and operational characteristics of structured ESS under dynamic grid conditions.Particularly,the aim is to maximize efficiency.A multiobjective genetic algorithm,the GA-OCESE framework,considers all these factors simultaneously.Besides considering cost-efficiency,response time,and energy use,the system also considers all these elements simultaneously.This enables it to effectively react to load uncertainty and variations in inputs connected to renewable sources.Results of an experimental assessment conducted on a standardized grid simulation platform indicate that by increasing energy use efficiency by 17.6%and reducing peak-load effects by 22.3%,GA-OCESE outperforms previous heuristic-based methods.This was found by contrasting the outcomes of the assessment with those of the evaluation.The results of the assessment helped to reveal this.The proposed approach will provide utility operators and energy planners with a decision-making tool that is both scalable and adaptable.This technology is particularly well-suited for smart grids,microgrid systems,and power infrastructures that heavily rely on renewable energy.Every technical component has been carefully recorded to ensure accuracy,reproducibility,and relevance across all power systems engineering software uses.This was done to ensure the program’s relevance. 展开更多
关键词 Energy storage system(ESS) genetic algorithm(GA) grid optimization smart grid renewable energy integration multi-objective optimization
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Sparse optimization and multi-performance enhancement of a high frequency to very high frequency wideband radio array using a genetic algorithm 认领 引用
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作者 Yifeng Qin Jiarui Di +8 位作者 Liang Dong Zhang Ning Yan Liu Lesheng He Abidin Zamri Zainal Huanhuan Xie Latef Abdul Tarik Wei He Othman Mohamadariff 《Astronomical Techniques and Instruments》 CSCD 2026年第3期235-249,共15页
The High Frequency(HF, 3–30 MHz) to Very High Frequency(VHF, 30–300 MHz) band is a critical observational window in radio astronomy, playing a key role in the study of early-universe reionization, space weather moni... The High Frequency(HF, 3–30 MHz) to Very High Frequency(VHF, 30–300 MHz) band is a critical observational window in radio astronomy, playing a key role in the study of early-universe reionization, space weather monitoring, and solar physics. We determine whether a genetic algorithm-optimized sparse configuration of a 64-element planar radio antenna array can minimize the peak sidelobe level and enhance performance within the 10–90 MHz frequency range, compared with a regular configuration. The sparse-optimized array achieves a 1.04 dB reduction in peak sidelobe level across the frequency band compared with the regular array. Sensitivity improves significantly at all frequency points, with increases of up to 56% at 10 MHz and 45% at 50 MHz. At 90 MHz, the sensitivity matches that of the regular array. At three representative frequencies(50 MHz, 60 MHz, and 70 MHz), grating lobe suppression tests at different scan angles show that the regular array shows prominent grating lobes at specific scan angles(θ =53° at 50 MHz, θ = 30° at 60 MHz, and θ = 15° at 70 MHz). By contrast, the sparse array shows no observable grating lobes, confirming its superior suppression capability. At wide bandwidths, a sparse array optimized with a genetic algorithm outperforms a regular array in peak sidelobe level, sensitivity, and scanning range, supporting its use as a better technical solution for radio astronomical observations. 展开更多
关键词 HF-VHF band Sparse array Genetic algorithm Peak side lobe level Sensitivity
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A Genetic Algorithm-Based Double Auction Framework for Secure and Scalable Resource Allocation in Cloud-Integrated Intrusion Detection Systems 认领 引用
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作者 Siraj Un Muneer Ihsan Ullah +1 位作者 Zeshan Iqbal Rajermani Thinakaran 《Computers, Materials & Continua》 SCIE EI 2025年第12期4959-4975,共17页
The complexity of cloud environments challenges secure resource management,especially for intrusion detection systems(IDS).Existing strategies struggle to balance efficiency,cost fairness,and threat resilience.This pa... The complexity of cloud environments challenges secure resource management,especially for intrusion detection systems(IDS).Existing strategies struggle to balance efficiency,cost fairness,and threat resilience.This paper proposes an innovative approach to managing cloud resources through the integration of a genetic algorithm(GA)with a“double auction”method.This approach seeks to enhance security and efficiency by aligning buyers and sellers within an intelligent market framework.It guarantees equitable pricing while utilizing resources efficiently and optimizing advantages for all stakeholders.The GA functions as an intelligent search mechanism that identifies optimal combinations of bids from users and suppliers,addressing issues arising from the intricacies of cloud systems.Analyses proved that our method surpasses previous strategies,particularly in terms of price accuracy,speed,and the capacity to manage large-scale activities,critical factors for real-time cybersecurity systems,such as IDS.Our research integrates artificial intelligence-inspired evolutionary algorithms with market-driven methods to develop intelligent resource management systems that are secure,scalable,and adaptable to evolving risks,such as process innovation. 展开更多
关键词 Cloud computing combinatorial double auction genetic algorithm optimization resource allocation intrusion detection system(IDS) cloud security
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Optimal Planning of Multiple PV-DG in Radial Distribution Systems Using Loss Sensitivity Analysis and Genetic Algorithm 认领 引用
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作者 A. Elkholy 《Journal of Power and Energy Engineering》 2025年第2期1-22,共22页
This paper introduces an optimized planning approach for integrating photovoltaic as distributed generation (PV-DG) into the radial distribution power systems, utilizing exhaustive load flow (ELF), loss sensitivity fa... This paper introduces an optimized planning approach for integrating photovoltaic as distributed generation (PV-DG) into the radial distribution power systems, utilizing exhaustive load flow (ELF), loss sensitivity factor (LSF), genetic algorithms (GA) methods, and numerical method based on LSF. The methodology aims to determine the optimal allocation and sizing of multiple PV-DG to minimize power loss through time series power flow analysis. An approach utilizing continuous sensitivity analysis is developed and inherently leverages power flow and loss equations to compute LSF of all buses in the system towards employing a dynamic PV-DG model for more accurate results. The algorithm uses a numerical grid search method to optimize PV-DG placement in a power distribution system, focusing on minimizing system losses. It combines iterative analysis, sensitivity assessment, and comprehensive visualization to identify and present the optimal PV-DG configurations. The present-ed algorithms are verified through co-simulation framework combining MATLAB and OpenDSS to carry out analysis for 12-bus radial distribution test system. The proposed numerical method is compared with other algorithms, such as ELF, LSF methods, and Genetic Algorithms (GA). Results show that the proposed numerical method performs well in comparison with LSF and ELF solutions. 展开更多
关键词 Photovoltaic Systems Distributed Generation Multiple Allocation and Sizing Power Losses Radial Distribution System Genetic Algorithm
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Identification Simulation for Dynamical System Based on Genetic Algorithm and Recurrent Multilayer Neural Network 认领 引用 被引量：2
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作者 鄢田云 张翠芳 靳蕃 《Journal of Southwest Jiaotong University(English Edition)》 2003年第1期9-15,共7页
Identification simulation for dynamical system which is based on genetic algorithm (GA) and recurrent multilayer neural network (RMNN) is presented. In order to reduce the inputs of the model, RMNN which can remember ... Identification simulation for dynamical system which is based on genetic algorithm (GA) and recurrent multilayer neural network (RMNN) is presented. In order to reduce the inputs of the model, RMNN which can remember and store some previous parameters is used for identifier. And for its high efficiency and optimization, genetic algorithm is introduced into training RMNN. Simulation results show the effectiveness of the proposed scheme. Under the same training algorithm, the identification performance of RMNN is superior to that of nonrecurrent multilayer neural network (NRMNN). 展开更多
关键词 genetic algorithm recurrent multilayer neural network identification simulation
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Real-Time Programmable Nonlinear Wavefront Shaping with Si Metasurface Driven by Genetic Algorithm 认领 引用 被引量：1
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作者 Ze Zheng Gabriel Sanderson +4 位作者 Soheil Sotoodeh Chris Clifton Cuifeng Ying Mohsen Rahmani Lei Xu 《Engineering》 SCIE EI CSCD 2025年第6期90-95,共6页
Nonlinear wavefront shaping is crucial for advancing optical technologies,enabling applications in optical computation,information processing,and imaging.However,a significant challenge is that once a metasurface is f... Nonlinear wavefront shaping is crucial for advancing optical technologies,enabling applications in optical computation,information processing,and imaging.However,a significant challenge is that once a metasurface is fabricated,the nonlinear wavefront it generates is fixed,offering little flexibility.This limitation often necessitates the fabrication of different metasurfaces for different wavefronts,which is both time-consuming and inefficient.To address this,we combine evolutionary algorithms with spatial light modulators(SLMs)to dynamically control wavefronts using a single metasurface,reducing the need for multiple fabrications and enabling the generation of arbitrary nonlinear wavefront patterns without requiring complicated optical alignment.We demonstrate this approach by introducing a genetic algorithm(GA)to manipulate visible wavefronts converted from near-infrared light via third-harmonic generation(THG)in a silicon metasurface.The Si metasurface supports multipolar Mie resonances that strongly enhance light-matter interactions,thereby significantly boosting THG emission at resonant positions.Additionally,the cubic relationship between THG emission and the infrared input reduces noise in the diffractive patterns produced by the SLM.This allows for precise experimental engineering of the nonlinear emission patterns with fewer alignment constraints.Our approach paves the way for self-optimized nonlinear wavefront shaping,advancing optical computation and information processing techniques. 展开更多
关键词 Nonlinear metasurface Genetic algorithm Wavefront manipulation
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An Asynchronous Genetic Algorithm for Multi-agent Path Planning Inspired by Biomimicry 认领 引用
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作者 Bin Liu Shikai Jin +3 位作者 Yuzhu Li Zhuo Wang Donglai Zhao Wenjie Ge 《Journal of Bionic Engineering》 SCIE EI CSCD 2025年第2期851-865,共15页
To address the shortcomings of traditional Genetic Algorithm (GA) in multi-agent path planning, such as prolonged planning time, slow convergence, and solution instability, this paper proposes an Asynchronous Genetic ... To address the shortcomings of traditional Genetic Algorithm (GA) in multi-agent path planning, such as prolonged planning time, slow convergence, and solution instability, this paper proposes an Asynchronous Genetic Algorithm (AGA) to solve multi-agent path planning problems effectively. To enhance the real-time performance and computational efficiency of Multi-Agent Systems (MAS) in path planning, the AGA incorporates an Equal-Size Clustering Algorithm (ESCA) based on the K-means clustering method. The ESCA divides the primary task evenly into a series of subtasks, thereby reducing the gene length in the subsequent GA process. The algorithm then employs GA to solve each subtask sequentially. To evaluate the effectiveness of the proposed method, a simulation program was designed to perform path planning for 100 trajectories, and the results were compared with those of State-Of-The-Art (SOTA) methods. The simulation results demonstrate that, although the solutions provided by AGA are suboptimal, it exhibits significant advantages in terms of execution speed and solution stability compared to other algorithms. 展开更多
关键词 Multi-agent path planning Asynchronous genetic algorithm Equal-size clustering Genetic algorithm
Improved Ant Colony-Genetic Algorithm for Information Transmission Path Optimization in Remanufacturing Service System 认领 引用 被引量：12
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作者 Lei Wang Xu-Hui Xia +2 位作者 Jian-Hua Cao Xiang Liu Jun-Wei Liu 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2018年第6期106-117,共12页
The information transmission path optimization(ITPO) can often a ect the e ciency and accuracy of remanufactur?ing service. However, there is a greater degree of uncertainty and complexity in information transmission ... The information transmission path optimization(ITPO) can often a ect the e ciency and accuracy of remanufactur?ing service. However, there is a greater degree of uncertainty and complexity in information transmission of remanu?facturing service system, which leads to a critical need for designing planning models to deal with this added uncer?tainty and complexity. In this paper, a three?dimensional(3D) model of remanufacturing service information network for information transmission is developed, which combines the physic coordinate and the transmitted properties of all the devices in the remanufacturing service system. In order to solve the basic ITPO in the 3D model, an improved 3D ant colony algorithm(Improved AC) was put forward. Moreover, to further improve the operation e ciency of the algorithm, an improved ant colony?genetic algorithm(AC?GA) that combines the improved AC and genetic algorithm was developed. In addition, by taking the transmission of remanufacturing service demand information of certain roller as example, the e ectiveness of AC?GA algorithm was analyzed and compared with that of improved AC, and the results demonstrated that AC?GA algorithm was superior to AC algorithm in aspects of information transmission delay, information transmission cost, and rate of information loss. 展开更多
关键词 Remanufacturing service Information transmission Path optimization Ant colony algorithm Genetic algorithm
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Application of Improved Genetic Algorithm in Network Fault Diagnosis Expert System 认领 引用 被引量：6
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作者 苏利敏 侯朝桢 +1 位作者 戴忠健 张雅静 《Journal of Beijing Institute of Technology》 EI CAS 2003年第3期225-229,共5页
Knowledge acquisition is the “bottleneck” of building an expert system. Based on the optimization model, an improved genetic algorithm applied to knowledge acquisition of a network fault diagnostic expert system is ... Knowledge acquisition is the “bottleneck” of building an expert system. Based on the optimization model, an improved genetic algorithm applied to knowledge acquisition of a network fault diagnostic expert system is proposed. The algorithm applies operators such as selection, crossover and mutation to evolve an initial population of diagnostic rules. Especially, a self adaptive method is put forward to regulate the crossover rate and mutation rate. In the end, a knowledge acquisition problem of a simple network fault diagnostic system is simulated, the results of simulation show that the improved approach can solve the problem of convergence better. 展开更多
关键词 expert system knowledge acquisition fault diagnosis genetic algorithm
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A quantum inspired genetic algorithm for multimodal optimization of wind disturbance alleviation flight control system 认领 引用 被引量：4
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作者 Qi BIAN Brett NENER Xinmin WANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2019年第11期2480-2488,共9页
This paper develops a Quantum-inspired Genetic Algorithm(QGA) to find the sets of optimal parameters for the wind disturbance alleviation Flight Control System(FCS). To search the problem domain more evenly and unifor... This paper develops a Quantum-inspired Genetic Algorithm(QGA) to find the sets of optimal parameters for the wind disturbance alleviation Flight Control System(FCS). To search the problem domain more evenly and uniformly, the lattice rule based stratification method is used to create new chromosomes. The chromosomes are coded and updated according to quantuminspired strategies. A niching method is used to ensure every chromosome can converge to its corresponding local minimum in the optimization process. A parallel archive system is adopted to monitor the chromosomes on-line and save all potential feasible solutions in the optimization process. An adaptive search strategy is used to gradually adjust the search domain of each niche to finally approach the local minima. The solutions found by the QGA are compared with some other Multimodal Optimization(MO) algorithms and are tested on the FCS of the Boeing 747 to demonstrate the effectiveness of the proposed algorithm. 展开更多
关键词 Flight control system Genetic algorithm Multimodal optimization Quantum inspired algorithm Wind disturbance alleviation
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