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MODIFIED GENETIC ALGORITHM APPLIED TO SOLVE PRODUCT FAMILY OPTIMIZATION PROBLEM 被引量:8
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作者 CHEN Chunbao WANG Liya 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2007年第4期106-111,共6页
The product family design problem solved by evolutionary algorithms is discussed. A successful product family design method should achieve an optimal tradeoff among a set of competing objectives, which involves maximi... The product family design problem solved by evolutionary algorithms is discussed. A successful product family design method should achieve an optimal tradeoff among a set of competing objectives, which involves maximizing commonality across the family of products and optimizing the performances of each product in the family. A 2-level chromosome structured genetic algorithm (2LCGA) is proposed to solve this class of problems and its performance is analyzed in comparing its results with those obtained with other methods. By interpreting the chromosome as a 2-level linear structure, the variable commonality genetic algorithm (GA) is constructed to vary the amount of platform commonality and automatically searches across varying levels of commonality for the platform while trying to resolve the tradeoff between commonality and individual product performance within the product family during optimization process. By incorporating a commonality assessing index to the problem formulation, the 2LCGA optimize the product platform and its corresponding family of products in a single stage, which can yield improvements in the overall performance of the product family compared with two-stage approaches (the first stage involves determining the best settings for the platform variables and values of unique variables are found for each product in the second stage). The scope of the algorithm is also expanded by introducing a classification mechanism to allow mul- tiple platforms to be considered during product family optimization, offering opportunities for superior overall design by more efficacious tradeoffs between commonality and performance. The effectiveness of 2LCGA is demonstrated through the design of a family of universal electric motors and comparison against previous results. 展开更多
关键词 Product family design Product platform genetic algorithm optimization
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Optimization and Simulation of Plastic Injection Process using Genetic Algorithm and Moldflow 被引量:13
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作者 Sigit Yoewono Martowibowo Agung Kaswadi 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2017年第2期398-406,共9页
The use of plastic-based products is continuously increasing. The increasing demands for thinner products, lower production costs, yet higher product quality has triggered an increase in the number of research project... The use of plastic-based products is continuously increasing. The increasing demands for thinner products, lower production costs, yet higher product quality has triggered an increase in the number of research projects on plastic molding processes. An important branch of such research is focused on mold cooling system. Conventional cooling systems are most widely used because they are easy to make by using conventional machining processes. However, the non-uniform cooling processes are considered as one of their weaknesses. Apart from the conven- tional systems, there are also conformal cooling systems that are designed for faster and more uniform plastic mold cooling. In this study, the conformal cooling system is applied for the production of bowl-shaped product made of PP AZ564. Optimization is conducted to initiate machine setup parameters, namely, the melting temperature, injection pressure, holding pressure and holding time. The genetic algorithm method and Moldflow were used to optimize the injection process parameters at a minimum cycle time. It is found that, an optimum injection molding processes could be obtained by setting the parameters to the following values: TM=180℃; Pinj = 20MPa; Phold= 16MPa and thold=8s, with a cycle time of 14.11 s. Experiments using the conformal cooling system yielded an average cycle time of 14.19 s. The studied conformal cooling system yielded a volumetric shrinkage of 5.61% and the wall shear stress was found at 0.17 MPa. The difference between the cycle time obtained through simulations and experiments using the conformal cooling system was insignificant (below 1%). Thus, combining process parameters optimization and simulations by using genetic algorithm method with Moldflow can be considered as valid. 展开更多
关键词 Conformal cooling Parameters optimization genetic algorithm MOLDFLOW Cycle time
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A novel adaptive mutative scale optimization algorithm based on chaos genetic method and its optimization efficiency evaluation 被引量:5
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作者 王禾军 鄂加强 邓飞其 《Journal of Central South University》 SCIE EI CAS 2012年第9期2554-2560,共7页
By combing the properties of chaos optimization method and genetic algorithm,an adaptive mutative scale chaos genetic algorithm(AMSCGA) was proposed by using one-dimensional iterative chaotic self-map with infinite co... By combing the properties of chaos optimization method and genetic algorithm,an adaptive mutative scale chaos genetic algorithm(AMSCGA) was proposed by using one-dimensional iterative chaotic self-map with infinite collapses within the finite region of [-1,1].Some measures in the optimization algorithm,such as adjusting the searching space of optimized variables continuously by using adaptive mutative scale method and making the most circle time as its control guideline,were taken to ensure its speediness and veracity in seeking the optimization process.The calculation examples about three testing functions reveal that AMSCGA has both high searching speed and high precision.Furthermore,the average truncated generations,the distribution entropy of truncated generations and the ratio of average inertia generations were used to evaluate the optimization efficiency of AMSCGA quantificationally.It is shown that the optimization efficiency of AMSCGA is higher than that of genetic algorithm. 展开更多
关键词 chaos genetic optimization algorithm CHAOS genetic algorithm optimization efficiency
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An adaptive reanalysis method for genetic algorithm with application to fast truss optimization 被引量:3
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作者 Tao Xu Wenjie Zuo +2 位作者 Tianshuang Xu Guangcai Song Ruichuan Li 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2010年第2期225-234,共10页
Although the genetic algorithm (GA) for structural optimization is very robust, it is very computationally intensive and hence slower than optimality criteria and mathematical programming methods. To speed up the de... Although the genetic algorithm (GA) for structural optimization is very robust, it is very computationally intensive and hence slower than optimality criteria and mathematical programming methods. To speed up the design process, the authors present an adaptive reanalysis method for GA and its applications in the optimal design of trusses. This reanalysis technique is primarily derived from the Kirsch's combined approximations method. An iteration scheme is adopted to adaptively determine the number of basis vectors at every generation. In order to illustrate this method, three classical examples of optimal truss design are used to validate the proposed reanalysis-based design procedure. The presented numerical results demonstrate that the adaptive reanalysis technique affects very slightly the accuracy of the optimal solutions and does accelerate the design process, especially for large-scale structures. 展开更多
关键词 Truss structure Adaptive reanalysis ·genetic algorithm ·Fast optimization
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DOA and Power Estimation Using Genetic Algorithm and Fuzzy Discrete Particle Swarm Optimization 被引量:3
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作者 Jia-Zhou Liu Zhi-Qin Zhao +1 位作者 Zi-Yuan He Qing-Huo Liu 《Journal of Electronic Science and Technology》 CAS 2014年第1期71-75,共5页
Aiming to reduce the computational costs and converge to global optimum, a novel method is proposed to solve the optimization of a cost function in the estimation of direction of arrival (DOA). In this method, a gen... Aiming to reduce the computational costs and converge to global optimum, a novel method is proposed to solve the optimization of a cost function in the estimation of direction of arrival (DOA). In this method, a genetic algorithm (GA) and fuzzy discrete particle swarm optimization (FDPSO) are applied to optimize the direction of arrival and power parameters of the mode simultaneously. Firstly, the GA algorithm is applied to make the solution fall into the global searching. Secondly, the FDPSO method is utilized to narrow down the search field. In FDPSO, a chaotic factor and a crossover method are added to speed up the convergence. This approach has been demonstrated through some computational simulations. It is shown that the proposed algorithm can estimate both the DOA and the powers accurately. It is more efficient than some present methods, such as the Newton-like algorithm, Akaike information critical (AIC), particle swarm optimization (PSO), and genetic algorithm with particle swarm optimization (GA-PSO). 展开更多
关键词 Direction of arrival genetic algorithm particle swarm optimization.
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Electro-Hydraulic Servo System Identification of Continuous Rotary Motor Based on the Integration Algorithm of Genetic Algorithm and Ant Colony Optimization 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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Gas exchange optimization in aircraft engines using sustainable aviation fuel:A design of experiment and genetic algorithm approach
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作者 Zheng Xu Jinze Pei +12 位作者 Shuiting Ding Longfei Chen Shuai Zhao Xiaowei Shen Kun Zhu Longtao Shao Zhiming Zhong Huansong Yan Farong Du Xueyu Li Pengfei Yang Shenghui Zhong Yu Zhou 《Energy and AI》 EI 2024年第3期261-280,共20页
The poppet valves two-stroke(PV2S)aircraft engine fueled with sustainable aviation fuel is a promising option for general aviation and unmanned aerial vehicle propulsion due to its high power-to-weight ratio,uniform t... The poppet valves two-stroke(PV2S)aircraft engine fueled with sustainable aviation fuel is a promising option for general aviation and unmanned aerial vehicle propulsion due to its high power-to-weight ratio,uniform torque output,and flexible valve timings.However,its high-altitude gas exchange performance remains unexplored,presenting new opportunities for optimization through artificial intelligence(AI)technology.This study uses validated 1D+3D models to evaluate the high-altitude gas exchange performance of PV2S aircraft engines.The valve timings of the PV2S engine exhibit considerable flexibility,thus the Latin hypercube design of experiments(DoE)methodology is employed to fit a response surface model.A genetic algorithm(GA)is applied to iteratively optimize valve timings for varying altitudes.The optimization process reveals that increasing the intake duration while decreasing the exhaust duration and valve overlap angles can significantly enhance high-altitude gas exchange performance.The optimal valve overlap angle emerged as 93°CA at sea level and 82°CA at 4000 m altitude.The effects of operating parameters,including engine speed,load,and exhaust back pressure,on the gas exchange process at varying altitudes are further investigated.The higher engine speed increases trapping efficiency but decreases the delivery ratio and charging efficiency at various altitudes.This effect is especially pronounced at elevated altitudes.The increase in exhaust back pressure will significantly reduce the delivery ratio and increase the trapping efficiency.This study demonstrates that integrating DoE with AI algorithms can enhance the high-altitude performance of aircraft engines,serving as a valuable reference for further optimization efforts. 展开更多
关键词 Poppet valves two-stroke Design of experiment genetic algorithm optimization Heavy fuel aircraft engine High altitude gas exchange performance
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Low side lobe pattern synthesis using projection method with genetic algorithm for truncated cone conformal phased arrays 被引量:7
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作者 Guoqi Zeng Siyin Li +1 位作者 Yan Zhang Shanwei L 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2014年第4期554-559,共6页
A hybrid method for synthesizing antenna's three dimensional (3D) pattern is proposed to obtain the low sidelobe feature of truncated cone conformal phased arrays. In this method, the elements of truncated cone con... A hybrid method for synthesizing antenna's three dimensional (3D) pattern is proposed to obtain the low sidelobe feature of truncated cone conformal phased arrays. In this method, the elements of truncated cone conformal phased arrays are projected to the tangent plane in one generatrix of the truncated cone. Then two dimensional (2D) Chebyshev amplitude distribution optimization is respectively used in two mutual vertical directions of the tangent plane. According to the location of the elements, the excitation current amplitude distribution of each element on the conformal structure is derived reversely, then the excitation current amplitude is further optimized by using the genetic algorithm (GA). A truncated cone problem with 8x8 elements on it, and a 3D pattern desired side lobe level (SLL) up to 35 dB, is studied. By using the hybrid method, the optimal goal is accomplished with acceptable CPU time, which indicates that this hybrid method for the low sidelobe synthesis is feasible. 展开更多
关键词 conformal phased array low side lobe pattern synthe-sis projection method genetic algorithm optimization.
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Optimizing combination of aircraft maintenance tasks by adaptive genetic algorithm based on cluster search 被引量:5
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作者 Huaiyuan Li Hongfu Zuo +3 位作者 Kun Liang Juan Xu Jing Cai Junqiang Liu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2016年第1期140-156,共17页
It is significant to combine multiple tasks into an optimal work package in decision-making of aircraft maintenance to reduce cost,so a cost rate model of combinatorial maintenance is an urgent need.However,the optima... It is significant to combine multiple tasks into an optimal work package in decision-making of aircraft maintenance to reduce cost,so a cost rate model of combinatorial maintenance is an urgent need.However,the optimal combination under various constraints not only involves numerical calculations but also is an NP-hard combinatorial problem.To solve the problem,an adaptive genetic algorithm based on cluster search,which is divided into two phases,is put forward.In the first phase,according to the density,all individuals can be homogeneously scattered over the whole solution space through crossover and mutation and better individuals are collected as candidate cluster centres.In the second phase,the search is confined to the neighbourhood of some selected possible solutions to accurately solve with cluster radius decreasing slowly,meanwhile all clusters continuously move to better regions until all the peaks in the question space is searched.This algorithm can efficiently solve the combination problem.Taking the optimization on decision-making of aircraft maintenance by the algorithm for an example,maintenance which combines multiple parts or tasks can significantly enhance economic benefit when the halt cost is rather high. 展开更多
关键词 cluster search genetic algorithm combinatorial optimization multi-part maintenance grouping maintenance.
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APPLICATION OF INTEGER CODING ACCELERATING GENETIC ALGORITHM IN RECTANGULAR CUTTING STOCK PROBLEM 被引量:3
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作者 FANG Hui YIN Guofu LI Haiqing PENG Biyou 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2006年第3期335-339,共5页
An improved genetic algorithm and its application to resolve cutting stock problem arc presented. It is common to apply simple genetic algorithm (SGA) to cutting stock problem, but the huge amount of computing of SG... An improved genetic algorithm and its application to resolve cutting stock problem arc presented. It is common to apply simple genetic algorithm (SGA) to cutting stock problem, but the huge amount of computing of SGA is a serious problem in practical application. Accelerating genetic algorithm (AGA) based on integer coding and AGA's detailed steps are developed to reduce the amount of computation, and a new kind of rectangular parts blank layout algorithm is designed for rectangular cutting stock problem. SGA is adopted to produce individuals within given evolution process, and the variation interval of these individuals is taken as initial domain of the next optimization process, thus shrinks searching range intensively and accelerates the evaluation process of SGA. To enhance the diversity of population and to avoid the algorithm stagnates at local optimization result, fixed number of individuals are produced randomly and replace the same number of parents in every evaluation process. According to the computational experiment, it is observed that this improved GA converges much sooner than SGA, and is able to get the balance of good result and high efficiency in the process of optimization for rectangular cutting stock problem. 展开更多
关键词 Accelerating genetic algorithm Efficiency of optimization Cutting stock problem
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THE EFFECTIVENESS OF GENETIC ALGORITHM IN CAPTURING CONDITIONAL NONLINEAR OPTIMAL PERTURBATION WITH PARAMETERIZATION “ON-OFF” SWITCHES INCLUDED BY A MODEL 被引量:2
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作者 方昌銮 郑琴 《Journal of Tropical Meteorology》 SCIE 2009年第1期13-19,共7页
In the typhoon adaptive observation based on conditional nonlinear optimal perturbation (CNOP), the ‘on-off’ switch caused by moist physical parameterization in prediction models prevents the conventional adjoint me... In the typhoon adaptive observation based on conditional nonlinear optimal perturbation (CNOP), the ‘on-off’ switch caused by moist physical parameterization in prediction models prevents the conventional adjoint method from providing correct gradient during the optimization process. To address this problem, the capture of CNOP, when the "on-off" switches are included in models, is treated as non-smooth optimization in this study, and the genetic algorithm (GA) is introduced. After detailed algorithm procedures are formulated using an idealized model with parameterization "on-off" switches in the forcing term, the impacts of "on-off" switches on the capture of CNOP are analyzed, and three numerical experiments are conducted to check the effectiveness of GA in capturing CNOP and to analyze the impacts of different initial populations on the optimization result. The result shows that GA is competent for the capture of CNOP in the context of the idealized model with parameterization ‘on-off’ switches in this study. Finally, the advantages and disadvantages of GA in capturing CNOP are analyzed in detail. 展开更多
关键词 dynamic meteorology typhoon adaptive observation genetic algorithm conditional nonlinear optimal perturbation switches moist physical parameterization
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Genetic algorithm for short-term scheduling of make-and-pack batch production process 被引量:1
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作者 Wuthichai Wongthatsanekorn Busaba Phruksaphanrat 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2015年第9期1475-1483,共9页
This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage ti... This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage time, batch splitting, partial equipment connectivity and transfer time. The objective is to make a production plan to satisfy all constraints while meeting demand requirement of packed products from various product families. This problem is NP-hard and the problem size is exponentially large for a realistic-sized problem. Therefore,we propose a genetic algorithm to handle this problem. Solutions to the problems are represented by chromosomes of product family sequences. These sequences are decoded to assign the resource for producing packed products according to forward assignment strategy and resource selection rules. These techniques greatly reduce unnecessary search space and improve search speed. In addition, design of experiment is carefully utilized to determine appropriate parameter settings. Ant colony optimization and Tabu search are also implemented for comparison. At the end of each heuristics, local search is applied for the packed product sequence to improve makespan. In an experimental analysis, all heuristics show the capability to solve large instances within reasonable computational time. In all problem instances, genetic algorithm averagely outperforms ant colony optimization and Tabu search with slightly longer computational time. 展开更多
关键词 genetic algorithm Ant colony optimization Tabu search Batch scheduling Make-and-pack production Forward assignment strategy
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Satellite constellation design with genetic algorithms based on system performance
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作者 Xueying Wang Jun Li +2 位作者 Tiebing Wang Wei An Weidong Sheng 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2016年第2期379-385,共7页
Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optic... Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optical system by taking into account the system tasks(i.e., target detection and tracking). We then propose a new non-dominated sorting genetic algorithm(NSGA) to maximize the system surveillance performance. Pareto optimal sets are employed to deal with the conflicts due to the presence of multiple cost functions. Simulation results verify the validity and the improved performance of the proposed technique over benchmark methods. 展开更多
关键词 space optical system non-dominated sorting genetic algorithm(NSGA) Pareto optimal set satellite constellation design surveillance performance
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Optimization of press bend forming path of aircraft integral panel 被引量:6
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作者 阎昱 万敏 +1 位作者 王海波 黄霖 《Transactions of Nonferrous Metals Society of China》 SCIE EI CAS CSCD 2010年第2期294-301,共8页
In order to design the press bend forming path of aircraft integral panels,a novel optimization method was proposed, which integrates FEM equivalent model based on previous study,the artificial neural network response... In order to design the press bend forming path of aircraft integral panels,a novel optimization method was proposed, which integrates FEM equivalent model based on previous study,the artificial neural network response surface,and the genetic algorithm.First,a multi-step press bend forming FEM equivalent model was established,with which the FEM experiments designed with Taguchi method were performed.Then,the BP neural network response surface was developed with the sample data from the FEM experiments.Furthermore,genetic algorithm was applied with the neural network response surface as the objective function. Finally,verification was carried out on a simple curvature grid-type stiffened panel.The forming error of the panel formed with the optimal path is only 0.098 39 and the calculating efficiency has been improved by 77%.Therefore,this novel optimization method is quite efficient and indispensable for the press bend forming path designing. 展开更多
关键词 aircraft integral panel press bend forming path neural network response surface genetic algorithm optimization
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Genetic Based Approach for Optimal Power and Channel Allocation to Enhance D2D Underlaied Cellular Network Capacity in 5G 被引量:1
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作者 Ahmed.A.Rosas Mona Shokair M.I.Dessouky 《Computers, Materials & Continua》 SCIE EI 2022年第8期3751-3762,共12页
With the obvious throughput shortage in traditional cellular radio networks,Device-to-Device(D2D)communications has gained a lot of attention to improve the utilization,capacity and channel performance of nextgenerati... With the obvious throughput shortage in traditional cellular radio networks,Device-to-Device(D2D)communications has gained a lot of attention to improve the utilization,capacity and channel performance of nextgeneration networks.In this paper,we study a joint consideration of power and channel allocation based on genetic algorithm as a promising direction to expand the overall network capacity for D2D underlaied cellular networks.The genetic based algorithm targets allocating more suitable channels to D2D users and finding the optimal transmit powers for all D2D links and cellular users efficiently,aiming to maximize the overall system throughput of D2D underlaied cellular network with minimum interference level,while satisfying the required quality of service QoS of each user.The simulation results show that our proposed approach has an advantage in terms of maximizing the overall system utilization than fixed,random,BAT algorithm(BA)and Particle Swarm Optimization(PSO)based power allocation schemes. 展开更多
关键词 5G D2D communication spectrum allocation power allocation genetic algorithm optimization BAT-optimization particle swarm optimization
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Multi-objective modeling and optimization for scheduling of cracking furnace systems 被引量:8
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作者 Peng Jiang Wenli Du 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2017年第8期992-999,共8页
Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multip... Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer non- linear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables (MDNSGA-II) is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and muta- tion strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model. 展开更多
关键词 Cracking furnace systems Feed scheduling Multi-objective mixed integer nonlinear optimization genetic algorithm
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PART BUILDING ORIENTATION OPTIMIZATION METHOD IN STEREOLITHOGRAPHY 被引量:7
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作者 HONG Jun WANG Wei TANG Yiping 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2006年第1期14-18,共5页
Aiming at the part quality and building time problems in stereolithography (SL) caused by unreasonable building orientation, a part building orientation decision method in SL rapid prototyping (RP) is carried out.... Aiming at the part quality and building time problems in stereolithography (SL) caused by unreasonable building orientation, a part building orientation decision method in SL rapid prototyping (RP) is carried out. Bringing into full consideration of the deformation, stair-stepping effect, overcure effect and building time related to the part fabrication orientation, and using evaluation function method, a multi-objective optimization model for the building orientation is defined. According to the difference in the angles between normal vectors of triangular facets in standard triangulation language (STL) model and z axis, the expressions of deformation area, stair-stepping area, overcure area are established. According to the characteristics in SL process, part building time is divided into four sections, that is, hatching scanning time, outline scanning time, support building time and layer waiting time. Expressions of each building time section are given. Considering the features of this optimization model, genetic algorithm (GA) is used to derive the optimization objective, related software is developed and optimization results are tested through experiments. Application shows that this method can effectively solve the quality and efficiency troubles caused by unreasonable part building orientation, an automatic orientation-determining program is developed and verified through test. 展开更多
关键词 Stereolithography (SL) Rapid prototyping (RP) Orientation optimization genetic algorithm (GA)
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Optimization and Control of Extractive Distillation with Heat Integration for Separating Benzene/Cyclohexane Mixtures 被引量:3
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作者 Li Lumin Tu Yangqin +2 位作者 Guo Lianjie Sun Lanyi Tian Yuanyu 《China Petroleum Processing & Petrochemical Technology》 SCIE CAS 2016年第4期117-127,共11页
In this work, the extractive distillation with heat integration process is extended to separate the pressure-insensitive benzene-cyclohexane azeotrope by using furfural as the entrainer. The optimal design of extracti... In this work, the extractive distillation with heat integration process is extended to separate the pressure-insensitive benzene-cyclohexane azeotrope by using furfural as the entrainer. The optimal design of extractive distillation process is established to achieve minimum energy requirement using the multi-objective genetic algorithm, and the results show that energy saving for this heat integration process is 15.7%. Finally, the control design is performed to investigate the system's dynamic performance, and three control structures are studied. The pressure-compensated temperature control scheme is proposed based on the first two control structures, and the dynamic responses reveal that the feed disturbances in both flow rate and benzene composition can be mitigated well. 展开更多
关键词 extractive distillation heat integration optimization genetic algorithm dynamic simulation
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Genetic-fuzzy HEV control strategy based on driving cycle recognition 被引量:1
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作者 邢杰 He Hongwen Zhang Xiaowei 《High Technology Letters》 EI CAS 2010年第1期39-44,共6页
A genetic-fuzzy HEV control strategy based on driving cycle recognition (DCR) was built. Six driving cycles were selected to represent different traffic conditions e.g. freeway, urban, suburb. A neural algorithm was... A genetic-fuzzy HEV control strategy based on driving cycle recognition (DCR) was built. Six driving cycles were selected to represent different traffic conditions e.g. freeway, urban, suburb. A neural algorithm was used for traffic condition recognition based on ten parameters of each driving cycle. The DCR was utilized for optimization of the HEV control parameters using a genetic-fuzzy approach. A fuzzy logic controller (FLC) was designed to be intelligent to manage the engine to work in the vicinity of its optimal condition. The fuzzy membership function parameters were optimized using the genetic algorithm (GA) for each driving cycle. The result is that the DCR_ fuzzy controller can reduce the fuel consumption by 1. 9%, higher than only CYC _ HWFET optimized fuzzy (0.2%) or CYC _ WVUSUB optimized fuzzy (0.7%). The DCR_ fuzzy method can get the better result than only optimizing one cycle on the complex real traffic conditions. 展开更多
关键词 HEV control strategy driving cycle recognition (DCR) fuzzy logic control (FLC) neural algorithm optimization genetic algorithm (GA) optimization
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Optimization of multi-color laser waveform for high-order harmonic generation 被引量:1
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作者 金成 林启东 《Chinese Physics B》 SCIE EI CAS CSCD 2016年第9期157-168,共12页
With the development of laser technologies,multi-color light-field synthesis with complete amplitude and phase control would make it possible to generate arbitrary optical waveforms.A practical optimization algorithm ... With the development of laser technologies,multi-color light-field synthesis with complete amplitude and phase control would make it possible to generate arbitrary optical waveforms.A practical optimization algorithm is needed to generate such a waveform in order to control strong-field processes.We review some recent theoretical works of the optimization of amplitudes and phases of multi-color lasers to modify the single-atom high-order harmonic generation based on genetic algorithm.By choosing different fitness criteria,we demonstrate that:(i) harmonic yields can be enhanced by 10 to 100 times,(ii) harmonic cutoff energy can be substantially extended,(iii) specific harmonic orders can be selectively enhanced,and(iv) single attosecond pulses can be efficiently generated.The possibility of optimizing macroscopic conditions for the improved phase matching and low divergence of high harmonics is also discussed.The waveform control and optimization are expected to be new drivers for the next wave of breakthrough in the strong-field physics in the coming years. 展开更多
关键词 high-order harmonic generation waveform optimization genetic algorithm single-attosecond pulse
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