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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
1
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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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：2
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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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Hybrid genetic algorithm for parametric optimization of surface pipeline networks in underground natural gas storage harmonized injection and production conditions 认领 引用
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作者 Jun Zhou Zichen Li +4 位作者 Shitao Liu Chengyu Li Yunxiang Zhao Zonghang Zhou Guangchuan Liang 《Natural Gas Industry B》 2025年第2期234-250,共17页
The surface injection and production system(SIPS)is a critical component for effective injection and production processes in underground natural gas storage.As a vital channel,the rational design of the surface inject... The surface injection and production system(SIPS)is a critical component for effective injection and production processes in underground natural gas storage.As a vital channel,the rational design of the surface injection and production(SIP)pipeline significantly impacts efficiency.This paper focuses on the SIP pipeline and aims to minimize the investment costs of surface projects.An optimization model under harmonized injection and production conditions was constructed to transform the optimization problem of the SIP pipeline design parameters into a detailed analysis of the injection condition model and the production condition model.This paper proposes a hybrid genetic algorithm generalized reduced gradient(HGA-GRG)method,and compares it with the traditional genetic algorithm(GA)in a practical case study.The HGA-GRG demonstrated significant advantages in optimization outcomes,reducing the initial cost by 345.371×104 CNY compared to the GA,validating the effectiveness of the model.By adjusting algorithm parameters,the optimal iterative results of the HGA-GRG were obtained,providing new research insights for the optimal design of a SIPS. 展开更多
关键词 Underground natural gas storage Surface injection and production pipeline Parameter optimization Hybrid 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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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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Optimization of Operating Parameters for Underground Gas Storage Based on Genetic Algorithm 认领 引用
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作者 Yuming Luo Wei Zhang +7 位作者 Anqi Zhao Ling Gou Li Chen Yaling Yang Xiaoping Wang Shichang Liu Huiqing Qi Shilai Hu 《Energy Engineering》 EI 2025年第8期3201-3221,共21页
This work proposes an optimization method for gas storage operation parameters under multi-factor coupled constraints to improve the peak-shaving capacity of gas storage reservoirs while ensuring operational safety.Pr... This work proposes an optimization method for gas storage operation parameters under multi-factor coupled constraints to improve the peak-shaving capacity of gas storage reservoirs while ensuring operational safety.Previous research primarily focused on integrating reservoir,wellbore,and surface facility constraints,often resulting in broad constraint ranges and slow model convergence.To solve this problem,the present study introduces additional constraints on maximum withdrawal rates by combining binomial deliverability equations with material balance equations for closed gas reservoirs,while considering extreme peak-shaving demands.This approach effectively narrows the constraint range.Subsequently,a collaborative optimization model with maximum gas production as the objective function is established,and the model employs a joint solution strategy combining genetic algorithms and numerical simulation techniques.Finally,this methodology was applied to optimize operational parameters for Gas Storage T.The results demonstrate:(1)The convergence of the model was achieved after 6 iterations,which significantly improved the convergence speed of the model;(2)The maximum working gas volume reached 11.605×108 m3,which increased by 13.78%compared with the traditional optimization method;(3)This method greatly improves the operation safety and the ultimate peak load balancing capability.The research provides important technical support for the intelligent decision of injection and production parameters of gas storage and improving peak load balancing ability. 展开更多
关键词 Underground gas storage operational parameter optimization extreme peak-shaving constraints genetic algorithm model
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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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Cat Swarm Algorithm Generated Based on Genetic Programming Framework Applied in Digital Watermarking 认领 引用 被引量：1
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作者 Shu-Chuan Chu Libin Fu +2 位作者 Jeng-Shyang Pan Xingsi Xue Min Liu 《Computers, Materials & Continua》 SCIE EI 2025年第5期3135-3163,共29页
Evolutionary algorithms have been extensively utilized in practical applications.However,manually designed population updating formulas are inherently prone to the subjective influence of the designer.Genetic programm... Evolutionary algorithms have been extensively utilized in practical applications.However,manually designed population updating formulas are inherently prone to the subjective influence of the designer.Genetic programming(GP),characterized by its tree-based solution structure,is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world problems.This paper introduces a GP-based framework(GPEAs)for the autonomous generation of update formulas,aiming to reduce human intervention.Partial modifications to tree-based GP have been instigated,encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the algorithm.By designing suitable function sets and terminal sets tailored to the selected evolutionary algorithm,and ultimately derive an improved update formula.The Cat Swarm Optimization Algorithm(CSO)is chosen as a case study,and the GP-EAs is employed to regenerate the speed update formulas of the CSO.To validate the feasibility of the GP-EAs,the comprehensive performance of the enhanced algorithm(GP-CSO)was evaluated on the CEC2017 benchmark suite.Furthermore,GP-CSO is applied to deduce suitable embedding factors,thereby improving the robustness of the digital watermarking process.The experimental results indicate that the update formulas generated through training with GP-EAs possess excellent performance scalability and practical application proficiency. 展开更多
关键词 Cat swarm algorithm genetic programming digital watermarking update mode mode generation framework
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Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm 认领 引用 被引量：16
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作者 毛勇 周晓波 +2 位作者 皮道映 孙优贤 WONG Stephen T.C. 《Journal of Zhejiang University-SCIENCE B》 EI CAS 2005年第10期961-973,共13页
In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying result... In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear sta- tistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two repre- sentative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method per- forms well in selecting genes and achieves high classification accuracies with these genes. 展开更多
关键词 Gene selection, Support vector machine (SVM), Recursive feature elimination (RFE), Genetic algorithm (GA),Parameter selection
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Kriging Surrogate-Based Genetic Algorithm Optimization for Blade Design of a Horizontal Axis Wind Turbine 认领 引用 被引量：10
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作者 Nantiwat Pholdee Sujin Bureerat Weerapon Nuantong 《Computer Modeling in Engineering & Sciences》 SCIE EI 2021年第1期261-273,共13页
Horizontal axis wind turbines are some of the most widely used clean energy generators in the world.Horizontal axis wind turbine blades need to be designed for optimization in order to maximize efficiency and simultan... Horizontal axis wind turbines are some of the most widely used clean energy generators in the world.Horizontal axis wind turbine blades need to be designed for optimization in order to maximize efficiency and simultaneously minimize the cost of energy.This work presents the optimization of new MEXICO blades for a horizontal axis wind turbine at the wind speed of 10 m/s.The optimization problem is posed to maximize the power coefficient while the design variables are twist angles on the blade radius and rotating axis positions on a chord length of the airfoils.Computational fluid dynamics was used for the aerodynamic simulation.Surrogate-assisted optimization was applied to reduce computational time.A surrogate model called a Kriging model,using a Gaussian correlation function along with various regression models,was applied while a genetic algorithm was used as an optimizer.The results obtained in this study are discussed and compared with those obtained from the original model.It was found that the Kriging model with linear regression gives better results than the Kriging model with second-order polynomial regression.The optimum blade obtained in this study showed better performance than the original blade at a low wind speed of 10 m/s. 展开更多
关键词 Wind turbine optimization kriging genetic algorithms Gaussian
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Gaussian fitting based optimal design of aircraft mission success space using multi-objective genetic algorithm 认领 引用 被引量：8
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作者 Yuan GAO Yongliang TIAN +1 位作者 Hu LIU Xue SUN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2020年第12期3318-3330,共13页
In order to obtain the optimized aircraft design concept which meets the increasingly complex operation environment at the conceptual design stage,System-of-systems(So S)engineering must be considered.This paper propo... In order to obtain the optimized aircraft design concept which meets the increasingly complex operation environment at the conceptual design stage,System-of-systems(So S)engineering must be considered.This paper proposes a novel optimization method for the design of aircraft Mission Success Space(MSS)based on Gaussian fitting and Genetic Algorithm(GA)in the So S area.First,the concepts in the design and evaluation of MSS are summarized to introduce the Contribution to System-of-Systems(CSS)by using a conventional effectiveness index,Mission Success Rate(MSR).Then,the mathematic modelling of Gaussian fitting technique is noted as the basis of the optimization work.After that,the proposed optimal MSS design is illustrated by the multiobjective optimization process where GA acts as the search tool to find the best solution(via Pareto front).In the case study,a simulation system of penetration mission was built.The simulation results are collected and then processed by two MSS design schemes(contour and neural network)giving the initial variable space to GA optimization.Based on that,the proposed optimization method is implemented under both schemes whose optimal solutions are compared to obtain the final best design in the case study. 展开更多
关键词 Evaluation Gaussian fitting Genetic algorithm Mission success space Neural network System-of-systems
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Porosity Prediction from Well Logs Using Back Propagation Neural Network Optimized by Genetic Algorithm in One Heterogeneous Oil Reservoirs of Ordos Basin, China 认领 引用 被引量：8
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作者 Lin Chen Weibing Lin +3 位作者 Ping Chen Shu Jiang Lu Liu Haiyan Hu 《Journal of Earth Science》 SCIE CAS CSCD 2021年第4期828-838,共11页
A reliable and effective model for reservoir physical property prediction is a key to reservoir characterization and management.At present,using well logging data to estimate reservoir physical parameters is an import... A reliable and effective model for reservoir physical property prediction is a key to reservoir characterization and management.At present,using well logging data to estimate reservoir physical parameters is an important means for reservoir evaluation.Based on the characteristics of large quantity and complexity of estimating process,we have attempted to design a nonlinear back propagation neural network model optimized by genetic algorithm(BPNNGA)for reservoir porosity prediction.This model is with the advantages of self-learning and self-adaption of back propagation neural network(BPNN),structural parameters optimizing and global searching optimal solution of genetic algorithm(GA).The model is applied to the Chang 8 oil group tight sandstone of Yanchang Formation in southwestern Ordos Basin.According to the correlations between well logging data and measured core porosity data,5 well logging curves(gamma ray,deep induction,density,acoustic,and compensated neutron)are selected as the input neurons while the measured core porosity is selected as the output neurons.The number of hidden layer neurons is defined as 20 by the method of multiple calibrating optimizations.Modeling results demonstrate that the average relative error of the model output is 10.77%,indicating the excellent predicting effect of the model.The predicting results of the model are compared with the predicting results of conventional multivariate stepwise regression algorithm,and BPNN model.The average relative errors of the above models are 12.83%,12.9%,and 13.47%,respectively.Results show that the predicting results of the BPNNGA model are more accurate than that of the other two,and BPNNGA is a more applicable method to estimate the reservoir porosity parameters in the study area. 展开更多
关键词 porosity prediction well logs back propagation neural network genetic algorithm Ordos Basin Yanchang Formation
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Improved non-dominated sorting genetic algorithm (NSGA)-II in multi-objective optimization studies of wind turbine blades 认领 引用 被引量：33
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作者 王珑 王同光 罗源 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2011年第6期739-748,共10页
The non-dominated sorting genetic algorithm （NSGA） is improved with the controlled elitism and dynamic crowding distance. A novel multi-objective optimization algorithm is obtained for wind turbine blades. As an exa... The non-dominated sorting genetic algorithm （NSGA） is improved with the controlled elitism and dynamic crowding distance. A novel multi-objective optimization algorithm is obtained for wind turbine blades. As an example, a 5 MW wind turbine blade design is presented by taking the maximum power coefficient and the minimum blade mass as the optimization objectives. The optimal results show that this algorithm has good performance in handling the multi-objective optimization of wind turbines, and it gives a Pareto-optimal solution set rather than the optimum solutions to the conventional multi objective optimization problems. The wind turbine blade optimization method presented in this paper provides a new and general algorithm for the multi-objective optimization of wind turbines. 展开更多
关键词 wind turbine multi-objective optimization Pareto-optimal solution non-dominated sorting genetic algorithm （NSGA）-II
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A prediction model for high-geothermal rockburst intensity using a genetic projection pursuit algorithm 认领 引用
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作者 Meiben Gao Tianbin Li +4 位作者 Shixin Ji Shiming Bai Yan Zhang Zhihao He Liang Zhang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第12期7612-7627,共16页
High-temperature rockbursts pose a critical challenge in deep underground engineering and resource exploitation.Consequently,predicting high-geothermal rockbursts has become a key scientific objective.In this paper,a ... High-temperature rockbursts pose a critical challenge in deep underground engineering and resource exploitation.Consequently,predicting high-geothermal rockbursts has become a key scientific objective.In this paper,a genetic projection pursuit algorithm(GPPA)is proposed for the prediction of high-geothermal rockbursts by introducing the coefficient K,and utilizing multiple empirical criteria(Wet index,σc/σt,σθ/σc,andσ1/σc).Four empirical criteria were statistically analyzed for 147 sets of rockburst cases,yielding accuracies of 40%,39%,46%and 29%,respectively.After the implantation of optimal segmentation,there was an enhancement in accuracy by 12%,9%,6%,and 19%,respectively.Theσθ/σc criterion exhibited superior performance,with a baseline accuracy of 46%.The GPPA model was tested and validated using four characteristic parameters(Wet index,σc/σt,σθ/σc,andσ1/σc)as inputs,revealing that the error ranged between 0.07 and 0.41.Successful validation was performed in the Sangzhuling Tunnel(four slight rockbursts)and Qirehataer Diversion Tunnel(one moderate rockburst),which matched field observations.Consequently,the proposed model offers guidance for predicting high-geothermal rockburst hazards. 展开更多
关键词 Rockburst criterion Thermal impacts Genetic projection pursuit algorithm(GPPA) Sangzhuling tunnel Qirehataer diversion tunnel
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Fuzzy Logic Based Evaluation of Hybrid Termination Criteria in the Genetic Algorithms for the Wind Farm Layout Design Problem 认领 引用
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作者 Salman A.Khan Mohamed Mohandes +2 位作者 Shafiqur Rehman Ali Al-Shaikhi Kashif Iqbal 《Computers, Materials & Continua》 SCIE EI 2025年第7期553-581,共29页
Wind energy has emerged as a potential replacement for fossil fuel-based energy sources.To harness maximum wind energy,a crucial decision in the development of an efficient wind farm is the optimal layout design.This ... Wind energy has emerged as a potential replacement for fossil fuel-based energy sources.To harness maximum wind energy,a crucial decision in the development of an efficient wind farm is the optimal layout design.This layout defines the specific locations of the turbines within the wind farm.The process of finding the optimal locations of turbines,in the presence of various technical and technological constraints,makes the wind farm layout design problem a complex optimization problem.This problem has traditionally been solved with nature-inspired algorithms with promising results.The performance and convergence of nature-inspired algorithms depend on several parameters,among which the algorithm termination criterion plays a crucial role.Timely convergence is an important aspect of efficient algorithm design because an inefficient algorithm results in wasted computational resources,unwarranted electricity consumption,and hardware stress.This study provides an in-depth analysis of several termination criteria while using the genetic algorithm as a test bench,with its application to the wind farm layout design problem while considering various wind scenarios.The performance of six termination criteria is empirically evaluated with respect to the quality of solutions produced and the execution time involved.Due to the conflicting nature of these two attributes,fuzzy logic-based multi-attribute decision-making is employed in the decision process.Results for the fuzzy decision approach indicate that among the various criteria tested,the criterion Phi achieves an improvement in the range of 2.44%to 32.93%for wind scenario 1.For scenario 2,Best-worst termination criterion performed well compared to the other criteria evaluated,with an improvement in the range of 1.2%to 9.64%.For scenario 3,Hitting bound was the best performer with an improvement of 1.16%to 20.93%. 展开更多
关键词 Wind energy wind farm layout design performance evaluation genetic algorithms fuzzy logic multi-attribute decision-making
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Advanced 3D Wind Farm Layout Optimization Framework via Power-Law Perturbation-Based Genetic Algorithm 认领 引用
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作者 Jiaru Yang Yaotong Song +3 位作者 Jun Tang Weiping Ding Zhenyu Lei Shangce Gao 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第11期2314-2328,共15页
The modeling and optimization of wind farm layouts can effectively reduce the wake effect between turbine units,thereby enhancing the expected output power and avoiding negative influence.Traditional wind farm optimiz... The modeling and optimization of wind farm layouts can effectively reduce the wake effect between turbine units,thereby enhancing the expected output power and avoiding negative influence.Traditional wind farm optimization often uses idealized wake models,neglecting the influence of wind shear at different elevations,which leads to a lack of precision in estimating wake effects and fails to meet the accuracy and reliability requirements of practical engineering.To address this,we have constructed a three-dimensional 3D wind farm optimization model that incorporates elevation,utilizing a 3D wake model to better reflect real-world conditions.We aim to assess the optimization state of the algorithm and provide strong incentives at the right moments to ensure continuous evolution of the population.To this end,we propose an evolutionary adaptation degreeguided genetic algorithm based on power-law perturbation(PPGA)to adapt multidimensional conditions.We select the offshore wind power project in Nantong,Jiangsu,China,as a study example and compare PPGA with other well-performing algorithms under this practical project.Based on the actual wind condition data,the experimental results demonstrate that PPGA can effectively tackle this complex problem and achieve the best power efficiency. 展开更多
关键词 3D wake model China’s southeastern coast metaheuristic offshore wind farm power-law perturbation-based genetic algorithm(PPGA)
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