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
The learning algorithms of causal discovery mainly include score-based methods and genetic algorithms(GA).The score-based algorithms are prone to searching space explosion.Classical GA is slow to converge,and prone to...The learning algorithms of causal discovery mainly include score-based methods and genetic algorithms(GA).The score-based algorithms are prone to searching space explosion.Classical GA is slow to converge,and prone to falling into local optima.To address these issues,an improved GA with domain knowledge(IGADK)is proposed.Firstly,domain knowledge is incorporated into the learning process of causality to construct a new fitness function.Secondly,a dynamical mutation operator is introduced in the algorithm to accelerate the convergence rate.Finally,an experiment is conducted on simulation data,which compares the classical GA with IGADK with domain knowledge of varying accuracy.The IGADK can greatly reduce the number of iterations,populations,and samples required for learning,which illustrates the efficiency and effectiveness of the proposed algorithm.展开更多
Project construction and development are an impor-tant part of future army designs.In today’s world,intelligent war-fare and joint operations have become the dominant develop-ments in warfare,so the construction and ...Project construction and development are an impor-tant part of future army designs.In today’s world,intelligent war-fare and joint operations have become the dominant develop-ments in warfare,so the construction and development of the army need top-down,top-level design,and comprehensive plan-ning.The traditional project development model is no longer suf-ficient to meet the army’s complex capability requirements.Projects in various fields need to be developed and coordinated to form a joint force and improve the army’s combat effective-ness.At the same time,when a program consists of large-scale project data,the effectiveness of the traditional,precise mathe-matical planning method is greatly reduced because it is time-consuming,costly,and impractical.To solve above problems,this paper proposes a multi-stage program optimization model based on a heterogeneous network and hybrid genetic algo-rithm and verifies the effectiveness and feasibility of the model and algorithm through an example.The results show that the hybrid algorithm proposed in this paper is better than the exist-ing meta-heuristic algorithm.展开更多
Thinning of antenna arrays has been a popular topic for the last several decades.With increasing computational power,this optimization task acquired a new hue.This paper suggests a genetic algorithm as an instrument f...Thinning of antenna arrays has been a popular topic for the last several decades.With increasing computational power,this optimization task acquired a new hue.This paper suggests a genetic algorithm as an instrument for antenna array thinning.The algorithm with a deliberately chosen fitness function allows synthesizing thinned linear antenna arrays with low peak sidelobe level(SLL)while maintaining the half-power beamwidth(HPBW)of a full linear antenna array.Based on results from existing papers in the field and known approaches to antenna array thinning,a classification of thinning types is introduced.The optimal thinning type for a linear thinned antenna array is determined on the basis of a maximum attainable SLL.The effect of thinning coefficient on main directional pattern characteristics,such as peak SLL and HPBW,is discussed for a number of amplitude distributions.展开更多
An adaptive technique adopting quantum genetic algorithm （QGA） for antenna impedance tuning is presented. Three examples are given with different types of antenna impedance. The frequency range of the dual standards...An adaptive technique adopting quantum genetic algorithm （QGA） for antenna impedance tuning is presented. Three examples are given with different types of antenna impedance. The frequency range of the dual standards is from 1.7 to 2.2 GHz. Simulation results show that the proposed tuning technique can achieve good accuracy of impedance matching and load power. The reflection coefficient and VSWR obtained are also very close to their ideal values. Comparison of the proposed QGA tuning method with conventional genetic algorithm based tuning method is Moreover, the proposed method can be useful for software wireless bands. also given, which shows that the QGA tuning algorithm is much faster. defined radio systems using a single antenna for multiple mobile and展开更多
This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different he...This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different heuristic optimization techniques including PID-PSO, Fuzzy-PSO and GA-PSO to improve the DSIM speed controlled loop behavior. The GA and PSO algorithms are developed and implemented into MATLAB. As a result, fuzzy-PSO is the most appropriate scheme. The main performance of fuzzy-PSO is reducing high torque ripples, improving rise time and avoiding disturbances that affect the drive performance.展开更多
To obtain the optimal process parameters of stamping forming, finite element analysis and optimization technique were integrated via transforming multi-objective issue into a single-objective issue. A Pareto-based gen...To obtain the optimal process parameters of stamping forming, finite element analysis and optimization technique were integrated via transforming multi-objective issue into a single-objective issue. A Pareto-based genetic algorithm was applied to optimizing the head stamping forming process. In the proposed optimal model, fracture, wrinkle and thickness varying are a function of several factors, such as fillet radius, draw-bead position, blank size and blank-holding force. Hence, it is necessary to investigate the relationship between the objective functions and the variables in order to make objective functions varying minimized simultaneously. Firstly, the central composite experimental(CCD) with four factors and five levels was applied, and the experimental data based on the central composite experimental were acquired. Then, the response surface model(RSM) was set up and the results of the analysis of variance(ANOVA) show that it is reliable to predict the fracture, wrinkle and thickness varying functions by the response surface model. Finally, a Pareto-based genetic algorithm was used to find out a set of Pareto front, which makes fracture, wrinkle and thickness varying minimized integrally. A head stamping case indicates that the present method has higher precision and practicability compared with the "trial and error" procedure.展开更多
A simple control structure in servo system is occasionally needed for simple industrial application which precise and high control performance is not exessively important so that the cost production can be reduced eff...A simple control structure in servo system is occasionally needed for simple industrial application which precise and high control performance is not exessively important so that the cost production can be reduced efficiently. Simplified vector control, which has simple control structure, is utilized as the permanent magnet synchronous motor control algorithm and genetic algorithm is used to tune three PI controllers used in simplified vector control. The control performance is obtained from simulation and investigated to verify the feasibility of the algorithm to be applied in the real application. Simulation results show that the speed and torque responses of the system in both continuous time and discrete time can achieve good performances. Furthermore, simplified vector control combined with genetic algorithm has a similar perfofmance with conventional field oriented control algorithm and possible to be realized into the real simple application in the future.展开更多
The wear rate between the rotors of a hypotrochoidal gear pump is characterized.Using the knowledge of shape design on the rotors,the contact stresses without hydrodynamic effect between the rotor teeth were evaluated...The wear rate between the rotors of a hypotrochoidal gear pump is characterized.Using the knowledge of shape design on the rotors,the contact stresses without hydrodynamic effect between the rotor teeth were evaluated through the calculation of the Hertzian contact stress.Based on the above results and the sliding velocity between the rotors,a genetic algorithm (GA) was used as an optimization technique forminimizing the wear rate proportional factor (WRPF).The result shows that the wear rate or the WRPF can be reduced considerably,e.g.approximately 12.8%,throughout the optimization using GA.展开更多
Aiming at dealing with the difficulty for traditional emergency rescue vehicle(ECV)to enter into limited rescue scenes,the electro-hydraulic steer-by-wire(SBW)system is introduced to achieve the multi-mode steering of...Aiming at dealing with the difficulty for traditional emergency rescue vehicle(ECV)to enter into limited rescue scenes,the electro-hydraulic steer-by-wire(SBW)system is introduced to achieve the multi-mode steering of the ECV.The overall structure and mathematical model of the SBW system are described at length.The fractional order proportional-integral-derivative(FOPID)controller based on fractional calculus theory is designed to control the steering cylinder’s movement in SBW system.The anti-windup problem is considered in the FOPID controller design to reduce the bad influence of saturation.Five parameters of the FOPID controller are optimized using the genetic algorithm by maximizing the fitness function which involves integral of time by absolute value error(ITAE),peak overshoot,as well as settling time.The time-domain simulations are implemented to identify the performance of the raised FOPID controller.The simulation results indicate the presented FOPID controller possesses more effective control properties than classical proportional-integral-derivative(PID)controller on the part of transient response,tracking capability and robustness.展开更多
A local improvement procedure based on tabu search(TS) was incorporated into a basic genetic algorithm(GA) and a global optimal algorithm,i.e.,hybrid genetic algorithm(HGA) approach was used to search the circular and...A local improvement procedure based on tabu search(TS) was incorporated into a basic genetic algorithm(GA) and a global optimal algorithm,i.e.,hybrid genetic algorithm(HGA) approach was used to search the circular and noncircular slip surfaces associated with their minimum safety factors.The slope safety factors of circular and noncircular critical slip surfaces were calculated by the simplified Bishop method and an improved Morgenstern-Price method which can be conveniently programmed,respectively.Comparisons with other methods were made which indicate the high efficiency and accuracy of the HGA approach.The HGA approach was used to calculate one case example and the results demonstrated its applicability to practical engineering.展开更多
An integer linear bilevel programming problem is firstly transformed into a binary linear bilevel programming problem, and then converted into a single-level binary implicit programming. An orthogonal genetic algorith...An integer linear bilevel programming problem is firstly transformed into a binary linear bilevel programming problem, and then converted into a single-level binary implicit programming. An orthogonal genetic algorithm is developed for solving the binary linear implicit programming problem based on the orthogonal design. The orthogonal design with the factor analysis, an experimental design method is applied to the genetic algorithm to make the algorithm more robust, statistical y sound and quickly convergent. A crossover operator formed by the orthogonal array and the factor analysis is presented. First, this crossover operator can generate a smal but representative sample of points as offspring. After al of the better genes of these offspring are selected, a best combination among these offspring is then generated. The simulation results show the effectiveness of the proposed algorithm.展开更多
An improved genetic algorithm（IGA） based on a novel selection strategy to handle nonlinear programming problems is proposed.Each individual in selection process is represented as a three-dimensional feature vector w...An improved genetic algorithm（IGA） based on a novel selection strategy to handle nonlinear programming problems is proposed.Each individual in selection process is represented as a three-dimensional feature vector which is composed of objective function value,the degree of constraints violations and the number of constraints violations.It is easy to distinguish excellent individuals from general individuals by using an individuals＇ feature vector.Additionally,a local search（LS） process is incorporated into selection operation so as to find feasible solutions located in the neighboring areas of some infeasible solutions.The combination of IGA and LS should offer the advantage of both the quality of solutions and diversity of solutions.Experimental results over a set of benchmark problems demonstrate that IGA has better performance than other algorithms.展开更多
The classical job shop scheduling problem(JSP) is the most popular machine scheduling model in practice and is known as NP-hard.The formulation of the JSP is based on the assumption that for each part type or job ther...The classical job shop scheduling problem(JSP) is the most popular machine scheduling model in practice and is known as NP-hard.The formulation of the JSP is based on the assumption that for each part type or job there is only one process plan that prescribes the sequence of operations and the machine on which each operation has to be performed.However,JSP with alternative machines for various operations is an extension of the classical JSP,which allows an operation to be processed by any machine from a given set of machines.Since this problem requires an additional decision of machine allocation during scheduling,it is much more complex than JSP.We present a domain independent genetic algorithm(GA) approach for the job shop scheduling problem with alternative machines.The GA is implemented in a spreadsheet environment.The performance of the proposed GA is analyzed by comparing with various problem instances taken from the literatures.The result shows that the proposed GA is competitive with the existing approaches.A simplified approach that would be beneficial to both practitioners and researchers is presented for solving scheduling problems with alternative machines.展开更多
In a manufacturing industry, mixed model assembly line(MMAL) is preferred in order to meet the variety in product demand. MMAL balancing helps in assembling products with similar characteristics in a random fashion. T...In a manufacturing industry, mixed model assembly line(MMAL) is preferred in order to meet the variety in product demand. MMAL balancing helps in assembling products with similar characteristics in a random fashion. The objective of this work aims in reducing the number of workstations, work load index between stations and within each station. As manual contribution of workers in final assembly line is more, ergonomics is taken as an additional objective function. Ergonomic risk level of a workstation is evaluated using a parameter called accumulated risk posture(ARP), which is calculated using rapid upper limb assessment(RULA) check sheet. This work is based on the case study of an MMAL problem in Rane(Madras) Ltd.(India), in which a problem based genetic algorithm(GA) has been proposed to minimize the mentioned objectives. The working of the genetic operators such as selection, crossover and mutation has been modified with respect to the addressed MMAL problem. The results show that there is a significant impact over productivity and the process time of the final assembled product, i.e., the rate of production is increased by 39.5% and the assembly time for one particular model is reduced to 13 min from existing 18 min. Also, the space required using the proposed assembly line is only 200 m2 against existing 350 m2. Further, the algorithm helps in reducing workers fatigue(i.e., ergonomic friendly).展开更多
The existing active tag-based radio frequency identi-fication(RFID)localization techniques show low accuracy in practical applications.To address such problems,we propose a chaotic adaptive genetic algorithm to align ...The existing active tag-based radio frequency identi-fication(RFID)localization techniques show low accuracy in practical applications.To address such problems,we propose a chaotic adaptive genetic algorithm to align the passive tag ar-rays.We use chaotic sequences to generate the intersection points,the weakest single point intersection is used to ensure the convergence accuracy of the algorithm while avoiding the optimization jitter problem.Meanwhile,to avoid the problem of slow convergence and immature convergence of the algorithm caused by the weakening of individual competition at a later stage,we use adaptive rate of change to improve the optimiza-tion efficiency.In addition,to remove signal noise and outliers,we preprocess the data using Gaussian filtering.Experimental results demonstrate that the proposed algorithm achieves high-er localization accuracy and improves the convergence speed.展开更多
A new genetic algorithm for community detection in complex networks was proposed. It adopts matrix encoding that enables traditional crossover between individuals. Initial populations are generated using nodes similar...A new genetic algorithm for community detection in complex networks was proposed. It adopts matrix encoding that enables traditional crossover between individuals. Initial populations are generated using nodes similarity, which enhances the diversity of initial individuals while retaining an acceptable level of accuracy, and improves the efficiency of optimal solution search. Individual crossover is based on the quality of individuals' genes; all nodes unassigned to any community are grouped into a new community, while ambiguously placed nodes are assigned to the community to which most of their neighbors belong. Individual mutation, which splits a gene into two new genes or randomly fuses it into other genes, is non-uniform. The simplicity and effectiveness of the algorithm are revealed in experimental tests using artificial random networks and real networks. The accuracy of the algorithm is superior to that of some classic algorithms, and is comparable to that of some recent high-precision algorithms.展开更多
While various kinds of fibers are used to improve the hot mix asphalt(HMA) performance, a few works have been undertaken on the hybrid fiber-reinforced HMA. Therefore, the fatigue life of modified HMA samples using po...While various kinds of fibers are used to improve the hot mix asphalt(HMA) performance, a few works have been undertaken on the hybrid fiber-reinforced HMA. Therefore, the fatigue life of modified HMA samples using polypropylene and polyester fibers was evaluated and two models namely regression and artificial neural network(ANN) were used to predict the fatigue life based on the fibers parameters. As ANN contains many parameters such as the number of hidden layers which directly influence the prediction accuracy, genetic algorithm(GA) was used to solve optimization problem for ANN. Moreover, the trial and error method was used to optimize the GA parameters such as the population size. The comparison of the results obtained from regression and optimized ANN with GA shows that the two-hidden-layer ANN with two and five neurons in the first and second hidden layers, respectively, can predict the fatigue life of fiber-reinforced HMA with high accuracy(correlation coefficient of 0.96).展开更多
The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborativ...The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborative optimization algorithm is proposed for the FJSP.Multi-population structure is used to independently evolve two sub-problems of the FJSP in the MSCGA.Good operators are adopted and designed to ensure this algorithm to achieve a good performance.Some famous FJSP benchmarks are chosen to evaluate the effectiveness of the MSCGA.The adaptability and superiority of the proposed method are demonstrated by comparing with other reported algorithms.展开更多
It is of great significance to carry out effective scheduling for the carrier-based aircraft flight deck operations.In this paper,the precedence constraints and resource constraints in flight deck operations are analy...It is of great significance to carry out effective scheduling for the carrier-based aircraft flight deck operations.In this paper,the precedence constraints and resource constraints in flight deck operations are analyzed,then the model of the multi-aircraft integrated scheduling problem with transfer times(MAISPTT)is established.A dual population multi-operator genetic algorithm(DPMOGA)is proposed for solving the problem.In the algorithm,the dual population structure and random-key encoding modified by starting/ending time of operations are adopted,and multiple genetic operators are self-adaptively used to obtain better encodings.In order to conduct the mapping from encodings to feasible schedules,serial and parallel scheduling generation scheme-based decoding operators,each of which adopts different justified mechanisms in two separated populations,are introduced.The superiority of the DPMOGA is verified by simulation experiments.展开更多
文摘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.
基金supported by the National Social Science Fund of China(2022-SKJJ-B-084).
文摘The learning algorithms of causal discovery mainly include score-based methods and genetic algorithms(GA).The score-based algorithms are prone to searching space explosion.Classical GA is slow to converge,and prone to falling into local optima.To address these issues,an improved GA with domain knowledge(IGADK)is proposed.Firstly,domain knowledge is incorporated into the learning process of causality to construct a new fitness function.Secondly,a dynamical mutation operator is introduced in the algorithm to accelerate the convergence rate.Finally,an experiment is conducted on simulation data,which compares the classical GA with IGADK with domain knowledge of varying accuracy.The IGADK can greatly reduce the number of iterations,populations,and samples required for learning,which illustrates the efficiency and effectiveness of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(724701189072431011).
文摘Project construction and development are an impor-tant part of future army designs.In today’s world,intelligent war-fare and joint operations have become the dominant develop-ments in warfare,so the construction and development of the army need top-down,top-level design,and comprehensive plan-ning.The traditional project development model is no longer suf-ficient to meet the army’s complex capability requirements.Projects in various fields need to be developed and coordinated to form a joint force and improve the army’s combat effective-ness.At the same time,when a program consists of large-scale project data,the effectiveness of the traditional,precise mathe-matical planning method is greatly reduced because it is time-consuming,costly,and impractical.To solve above problems,this paper proposes a multi-stage program optimization model based on a heterogeneous network and hybrid genetic algo-rithm and verifies the effectiveness and feasibility of the model and algorithm through an example.The results show that the hybrid algorithm proposed in this paper is better than the exist-ing meta-heuristic algorithm.
文摘Thinning of antenna arrays has been a popular topic for the last several decades.With increasing computational power,this optimization task acquired a new hue.This paper suggests a genetic algorithm as an instrument for antenna array thinning.The algorithm with a deliberately chosen fitness function allows synthesizing thinned linear antenna arrays with low peak sidelobe level(SLL)while maintaining the half-power beamwidth(HPBW)of a full linear antenna array.Based on results from existing papers in the field and known approaches to antenna array thinning,a classification of thinning types is introduced.The optimal thinning type for a linear thinned antenna array is determined on the basis of a maximum attainable SLL.The effect of thinning coefficient on main directional pattern characteristics,such as peak SLL and HPBW,is discussed for a number of amplitude distributions.
基金Projects(61102039, 51107034) supported by the National Natural Science Foundation of ChinaProject(2011FJ3080) supported by the Planned Science and Technology Project of Hunan Province ChinaProject supported by Fundamental Research Funds for the Central Universities, China
文摘An adaptive technique adopting quantum genetic algorithm （QGA） for antenna impedance tuning is presented. Three examples are given with different types of antenna impedance. The frequency range of the dual standards is from 1.7 to 2.2 GHz. Simulation results show that the proposed tuning technique can achieve good accuracy of impedance matching and load power. The reflection coefficient and VSWR obtained are also very close to their ideal values. Comparison of the proposed QGA tuning method with conventional genetic algorithm based tuning method is Moreover, the proposed method can be useful for software wireless bands. also given, which shows that the QGA tuning algorithm is much faster. defined radio systems using a single antenna for multiple mobile and
基金Project supported by Faculty of Technology,Department of Electrical Engineering,University of Batna,Algeria
文摘This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different heuristic optimization techniques including PID-PSO, Fuzzy-PSO and GA-PSO to improve the DSIM speed controlled loop behavior. The GA and PSO algorithms are developed and implemented into MATLAB. As a result, fuzzy-PSO is the most appropriate scheme. The main performance of fuzzy-PSO is reducing high torque ripples, improving rise time and avoiding disturbances that affect the drive performance.
基金Project(2012ZX04010-081) supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China
文摘To obtain the optimal process parameters of stamping forming, finite element analysis and optimization technique were integrated via transforming multi-objective issue into a single-objective issue. A Pareto-based genetic algorithm was applied to optimizing the head stamping forming process. In the proposed optimal model, fracture, wrinkle and thickness varying are a function of several factors, such as fillet radius, draw-bead position, blank size and blank-holding force. Hence, it is necessary to investigate the relationship between the objective functions and the variables in order to make objective functions varying minimized simultaneously. Firstly, the central composite experimental(CCD) with four factors and five levels was applied, and the experimental data based on the central composite experimental were acquired. Then, the response surface model(RSM) was set up and the results of the analysis of variance(ANOVA) show that it is reliable to predict the fracture, wrinkle and thickness varying functions by the response surface model. Finally, a Pareto-based genetic algorithm was used to find out a set of Pareto front, which makes fracture, wrinkle and thickness varying minimized integrally. A head stamping case indicates that the present method has higher precision and practicability compared with the "trial and error" procedure.
文摘A simple control structure in servo system is occasionally needed for simple industrial application which precise and high control performance is not exessively important so that the cost production can be reduced efficiently. Simplified vector control, which has simple control structure, is utilized as the permanent magnet synchronous motor control algorithm and genetic algorithm is used to tune three PI controllers used in simplified vector control. The control performance is obtained from simulation and investigated to verify the feasibility of the algorithm to be applied in the real application. Simulation results show that the speed and torque responses of the system in both continuous time and discrete time can achieve good performances. Furthermore, simplified vector control combined with genetic algorithm has a similar perfofmance with conventional field oriented control algorithm and possible to be realized into the real simple application in the future.
基金supported by Changwon National University in 2010,Korea
文摘The wear rate between the rotors of a hypotrochoidal gear pump is characterized.Using the knowledge of shape design on the rotors,the contact stresses without hydrodynamic effect between the rotor teeth were evaluated through the calculation of the Hertzian contact stress.Based on the above results and the sliding velocity between the rotors,a genetic algorithm (GA) was used as an optimization technique forminimizing the wear rate proportional factor (WRPF).The result shows that the wear rate or the WRPF can be reduced considerably,e.g.approximately 12.8%,throughout the optimization using GA.
基金Project(2016YFC0802904)supported by the National Key Research and Development Program of China
文摘Aiming at dealing with the difficulty for traditional emergency rescue vehicle(ECV)to enter into limited rescue scenes,the electro-hydraulic steer-by-wire(SBW)system is introduced to achieve the multi-mode steering of the ECV.The overall structure and mathematical model of the SBW system are described at length.The fractional order proportional-integral-derivative(FOPID)controller based on fractional calculus theory is designed to control the steering cylinder’s movement in SBW system.The anti-windup problem is considered in the FOPID controller design to reduce the bad influence of saturation.Five parameters of the FOPID controller are optimized using the genetic algorithm by maximizing the fitness function which involves integral of time by absolute value error(ITAE),peak overshoot,as well as settling time.The time-domain simulations are implemented to identify the performance of the raised FOPID controller.The simulation results indicate the presented FOPID controller possesses more effective control properties than classical proportional-integral-derivative(PID)controller on the part of transient response,tracking capability and robustness.
基金Project(50878082)supported by the National Natural Science Foundation of ChinaProject(2012C21058)supported by the Public Welfare Technology Application Research of Zhejiang Province,China
文摘A local improvement procedure based on tabu search(TS) was incorporated into a basic genetic algorithm(GA) and a global optimal algorithm,i.e.,hybrid genetic algorithm(HGA) approach was used to search the circular and noncircular slip surfaces associated with their minimum safety factors.The slope safety factors of circular and noncircular critical slip surfaces were calculated by the simplified Bishop method and an improved Morgenstern-Price method which can be conveniently programmed,respectively.Comparisons with other methods were made which indicate the high efficiency and accuracy of the HGA approach.The HGA approach was used to calculate one case example and the results demonstrated its applicability to practical engineering.
基金supported by the Fundamental Research Funds for the Central Universities(K50511700004)the Natural Science Basic Research Plan in Shaanxi Province of China(2013JM1022)
文摘An integer linear bilevel programming problem is firstly transformed into a binary linear bilevel programming problem, and then converted into a single-level binary implicit programming. An orthogonal genetic algorithm is developed for solving the binary linear implicit programming problem based on the orthogonal design. The orthogonal design with the factor analysis, an experimental design method is applied to the genetic algorithm to make the algorithm more robust, statistical y sound and quickly convergent. A crossover operator formed by the orthogonal array and the factor analysis is presented. First, this crossover operator can generate a smal but representative sample of points as offspring. After al of the better genes of these offspring are selected, a best combination among these offspring is then generated. The simulation results show the effectiveness of the proposed algorithm.
基金supported by the National Natural Science Foundation of China (60632050)National Basic Research Program of Jiangsu Province University (08KJB520003)
文摘An improved genetic algorithm（IGA） based on a novel selection strategy to handle nonlinear programming problems is proposed.Each individual in selection process is represented as a three-dimensional feature vector which is composed of objective function value,the degree of constraints violations and the number of constraints violations.It is easy to distinguish excellent individuals from general individuals by using an individuals＇ feature vector.Additionally,a local search（LS） process is incorporated into selection operation so as to find feasible solutions located in the neighboring areas of some infeasible solutions.The combination of IGA and LS should offer the advantage of both the quality of solutions and diversity of solutions.Experimental results over a set of benchmark problems demonstrate that IGA has better performance than other algorithms.
文摘The classical job shop scheduling problem(JSP) is the most popular machine scheduling model in practice and is known as NP-hard.The formulation of the JSP is based on the assumption that for each part type or job there is only one process plan that prescribes the sequence of operations and the machine on which each operation has to be performed.However,JSP with alternative machines for various operations is an extension of the classical JSP,which allows an operation to be processed by any machine from a given set of machines.Since this problem requires an additional decision of machine allocation during scheduling,it is much more complex than JSP.We present a domain independent genetic algorithm(GA) approach for the job shop scheduling problem with alternative machines.The GA is implemented in a spreadsheet environment.The performance of the proposed GA is analyzed by comparing with various problem instances taken from the literatures.The result shows that the proposed GA is competitive with the existing approaches.A simplified approach that would be beneficial to both practitioners and researchers is presented for solving scheduling problems with alternative machines.
基金support and help of many individuals in the SASTRA University
文摘In a manufacturing industry, mixed model assembly line(MMAL) is preferred in order to meet the variety in product demand. MMAL balancing helps in assembling products with similar characteristics in a random fashion. The objective of this work aims in reducing the number of workstations, work load index between stations and within each station. As manual contribution of workers in final assembly line is more, ergonomics is taken as an additional objective function. Ergonomic risk level of a workstation is evaluated using a parameter called accumulated risk posture(ARP), which is calculated using rapid upper limb assessment(RULA) check sheet. This work is based on the case study of an MMAL problem in Rane(Madras) Ltd.(India), in which a problem based genetic algorithm(GA) has been proposed to minimize the mentioned objectives. The working of the genetic operators such as selection, crossover and mutation has been modified with respect to the addressed MMAL problem. The results show that there is a significant impact over productivity and the process time of the final assembled product, i.e., the rate of production is increased by 39.5% and the assembly time for one particular model is reduced to 13 min from existing 18 min. Also, the space required using the proposed assembly line is only 200 m2 against existing 350 m2. Further, the algorithm helps in reducing workers fatigue(i.e., ergonomic friendly).
基金supported by the Aviation Science Foundation(ASFC-20181352009).
文摘The existing active tag-based radio frequency identi-fication(RFID)localization techniques show low accuracy in practical applications.To address such problems,we propose a chaotic adaptive genetic algorithm to align the passive tag ar-rays.We use chaotic sequences to generate the intersection points,the weakest single point intersection is used to ensure the convergence accuracy of the algorithm while avoiding the optimization jitter problem.Meanwhile,to avoid the problem of slow convergence and immature convergence of the algorithm caused by the weakening of individual competition at a later stage,we use adaptive rate of change to improve the optimiza-tion efficiency.In addition,to remove signal noise and outliers,we preprocess the data using Gaussian filtering.Experimental results demonstrate that the proposed algorithm achieves high-er localization accuracy and improves the convergence speed.
文摘A new genetic algorithm for community detection in complex networks was proposed. It adopts matrix encoding that enables traditional crossover between individuals. Initial populations are generated using nodes similarity, which enhances the diversity of initial individuals while retaining an acceptable level of accuracy, and improves the efficiency of optimal solution search. Individual crossover is based on the quality of individuals' genes; all nodes unassigned to any community are grouped into a new community, while ambiguously placed nodes are assigned to the community to which most of their neighbors belong. Individual mutation, which splits a gene into two new genes or randomly fuses it into other genes, is non-uniform. The simplicity and effectiveness of the algorithm are revealed in experimental tests using artificial random networks and real networks. The accuracy of the algorithm is superior to that of some classic algorithms, and is comparable to that of some recent high-precision algorithms.
文摘While various kinds of fibers are used to improve the hot mix asphalt(HMA) performance, a few works have been undertaken on the hybrid fiber-reinforced HMA. Therefore, the fatigue life of modified HMA samples using polypropylene and polyester fibers was evaluated and two models namely regression and artificial neural network(ANN) were used to predict the fatigue life based on the fibers parameters. As ANN contains many parameters such as the number of hidden layers which directly influence the prediction accuracy, genetic algorithm(GA) was used to solve optimization problem for ANN. Moreover, the trial and error method was used to optimize the GA parameters such as the population size. The comparison of the results obtained from regression and optimized ANN with GA shows that the two-hidden-layer ANN with two and five neurons in the first and second hidden layers, respectively, can predict the fatigue life of fiber-reinforced HMA with high accuracy(correlation coefficient of 0.96).
基金supported by the National Key R&D Program of China(2018AAA0101700)the Program for HUST Academic Frontier Youth Team(2017QYTD04).
文摘The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborative optimization algorithm is proposed for the FJSP.Multi-population structure is used to independently evolve two sub-problems of the FJSP in the MSCGA.Good operators are adopted and designed to ensure this algorithm to achieve a good performance.Some famous FJSP benchmarks are chosen to evaluate the effectiveness of the MSCGA.The adaptability and superiority of the proposed method are demonstrated by comparing with other reported algorithms.
基金supported by the National Natural Science Foundation of China(61671462).
文摘It is of great significance to carry out effective scheduling for the carrier-based aircraft flight deck operations.In this paper,the precedence constraints and resource constraints in flight deck operations are analyzed,then the model of the multi-aircraft integrated scheduling problem with transfer times(MAISPTT)is established.A dual population multi-operator genetic algorithm(DPMOGA)is proposed for solving the problem.In the algorithm,the dual population structure and random-key encoding modified by starting/ending time of operations are adopted,and multiple genetic operators are self-adaptively used to obtain better encodings.In order to conduct the mapping from encodings to feasible schedules,serial and parallel scheduling generation scheme-based decoding operators,each of which adopts different justified mechanisms in two separated populations,are introduced.The superiority of the DPMOGA is verified by simulation experiments.
