The hybrid genetic algorithm is utilized to facilitate model parameter estimation.The tri-dimensional compression tests of soil are performed to supply experimental data for identifying nonlinear constitutive model of...The hybrid genetic algorithm is utilized to facilitate model parameter estimation.The tri-dimensional compression tests of soil are performed to supply experimental data for identifying nonlinear constitutive model of soil.In order to save computing time during parameter inversion,a new procedure to compute the calculated strains is presented by multi-linear simplification approach instead of finite element method(FEM).The real-coded hybrid genetic algorithm is developed by combining normal genetic algorithm with gradient-based optimization algorithm.The numerical and experimental results for conditioned soil are compared.The forecast strains based on identified nonlinear constitutive model of soil agree well with observed ones.The effectiveness and accuracy of proposed parameter estimation approach are validated.展开更多
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...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
In Systems Biology, system identification, which infers regulatory network in genetic system and metabolic pathways using experimentally observed time-course data, is one of the hottest issues. The efficient numerical...In Systems Biology, system identification, which infers regulatory network in genetic system and metabolic pathways using experimentally observed time-course data, is one of the hottest issues. The efficient numerical optimization algorithm to estimate more than 100 real-coded parameters should be developed for this purpose. New real-coded genetic algorithm (RCGA), the combination of AREX (adaptive real-coded ensemble crossover) with JGG (just generation gap), have applied to the inference of genetic interactions involving more than 100 parameters related to the interactions with using experimentally observed time-course data. Compared with conventional RCGA, the combination of UNDX (unimodal normal distribution crossover) with MGG (minimal generation gap), new algorithm has shown the superiority with improving early convergence in the first stage of search and suppressing evolutionary stagnation in the last stage of search.展开更多
During the process of enterprises＇ strategy evaluation and selection, there are many evaluating indicators, and among them there are some potential correlations and conflicts. Thus it poses the problems to the decisi...During the process of enterprises＇ strategy evaluation and selection, there are many evaluating indicators, and among them there are some potential correlations and conflicts. Thus it poses the problems to the decision-makers how to conduct correct evaluation on a business and how to make strategy adjustment and selection according to the evaluation. Based on the qualitative and quantitative method, the paper introduces the Projection Pursuit Classification （PPC） model based on the Real-coded Accelerating Genetic Algorithm （RAGA） into the process of enterprises＇ strategy evaluation and selection. The characteristic of PPC model is that it ultimately overcomes the influence of the proportion of subjectivity and avoids precocious convergence, thus providing a new objective method for strategy evaluation and selection by pursuing the most objective strategy evaluation to make the relatively sensible strategy portfolio and action.展开更多
We present a global optimization method, called the real-code genetic algorithm (RGA), to the ground state energies. The proposed method does not require partial derivatives with respect to each variational parameter ...We present a global optimization method, called the real-code genetic algorithm (RGA), to the ground state energies. The proposed method does not require partial derivatives with respect to each variational parameter or solving an eigenequation, so the present method overcomes the major difficulties of the variational method. RGAs also do not require coding and encoding procedures, so the computation time and complexity are reduced. The ground state energies of hydrogenic donors in GaAs-(Ga,Al)As quantum dots have been calculated for a range of the radius of the quantum dot radii of practical interest. They are compared with those obtained by the variational method. The results obtained demonstrate the proposed method is simple, accurate, and easy implement.展开更多
In this paper, the main objective is to identify the parameters of motors, which includes a brushless direct current (BLDC) motor and an induction motor. The motor systems are dynamically formulated by the mechanical ...In this paper, the main objective is to identify the parameters of motors, which includes a brushless direct current (BLDC) motor and an induction motor. The motor systems are dynamically formulated by the mechanical and electrical equations. The real-coded genetic algorithm (RGA) is adopted to identify all parameters of motors, and the standard genetic algorithm (SRGA) and various adaptive genetic algorithm (ARGAs) are compared in the rotational angular speeds and fitness values, which are the inverse of square differences of angular speeds. From numerical simulations and experimental results, it is found that the SRGA and ARGA are feasible, the ARGA can effectively solve the problems with slow convergent speed and premature phenomenon, and is more accurate in identifying system’s parameters than the SRGA. From the comparisons of the ARGAs in identifying parameters of motors, the best ARGA method is obtained and could be applied to any other mechatronic systems.展开更多
High-brightness electron beams are required to drive LINAC-based free-electron lasers(FELs)and storage-ring-based synchrotron radiation light sources.The bunch charge and RMS bunch length at the exit of the LINAC play...High-brightness electron beams are required to drive LINAC-based free-electron lasers(FELs)and storage-ring-based synchrotron radiation light sources.The bunch charge and RMS bunch length at the exit of the LINAC play a crucial role in the peak current;the minimum transverse emittance is mainly determined by the injector of the LINAC.Thus,a photoin-jector with a high bunch charge and low emittance that can simultaneously provide high-quality beams for 4th generation synchrotron radiation sources and FELs is desirable.The design of a 1.6-cell S-band 2998-MHz RF gun and beam dynamics optimization of a relevant beamline are presented in this paper.Beam dynamics simulations were performed by combining ASTRA and the multi-objective genetic algorithm NSGA II.The effects of the laser pulse shape,half-cell length of the RF gun,and RF parameters on the output beam quality were analyzed and compared.The normalized transverse emittance was optimized to be as low as 0.65 and 0.92 mm·mrad when the bunch charge was as high as 1 and 2 nC,respectively.Finally,the beam stability properties of the photoinjector,considering misalignment and RF jitter,were simulated and analyzed.展开更多
A numerical procedure for hydrodynamic redesign of the conventional vaned diffuser into the low solidity vaned diffuser by means of a real-coded genetic algorithm with Boltzmann, Tournament and Roulette Wheel selectio...A numerical procedure for hydrodynamic redesign of the conventional vaned diffuser into the low solidity vaned diffuser by means of a real-coded genetic algorithm with Boltzmann, Tournament and Roulette Wheel selection is presented. In the first part, an investigation on the relative efficiency of the different real-coded genetic algorithm is carried out on a typical mathematical test function. The real-coded genetic algorithm with Boltzmann selection shows the best optimization performance compared to the Tournament and Roulette Wheel selection. In the second part, an approach to redesign the vaned diffuser profile is introduced. Goal of the optimum design is to search the highest static pressure recovery coefficient and low solidity vaned diffuser. The result of the low solidity vaned diffuser optimum design confirms that the efficiency and optimization performance of the real-coded Boltzmann selection genetic algorithm outperforms the other selection methods. A comparison between the designed low solidity vaned diffuser and original vaned diffuser shows that the diffuser pump with the redesigned low solidity vaned diffuser has the higher static pressure recovery and improved total hydrodynamic performance. In addition, the smaller outlet diameter of designed vaned diffuser tends to a more compact size of diffuser pump compared to the original diffuser pump. The obtained results also demonstrate the real-coded Boltzmann selection genetic algorithm is a promising optimization algorithm for centrifugal pumps design.展开更多
Harmonic drives have various distinctive advantages and are widely used in space drive mechanisms. Accelerated life test （ALT） is commonly conducted to shorten test time and reduce associated costs. An appropriate A...Harmonic drives have various distinctive advantages and are widely used in space drive mechanisms. Accelerated life test （ALT） is commonly conducted to shorten test time and reduce associated costs. An appropriate ALT modet is needed to predict the lifetime of harmonic drives with ALT data. However, harmonic drives which are used in space usually work under a segmental stress history, and traditional ALT models can hardly be used in this situation. This paper proposes a dedicated ALT model for harmonic drives applied in space systems. A comprehensive ALT model is established and genetic algorithm （GA） is adopted to obtain optimal parameters in the model using the Manson fatigue damage rule to describe the fatigue failure process and a cumulative dam- age method to calculate and accumulate the damage caused by each segment in the stress history. An ALT of harmonic drives was carried out and experimental results show that this model is acceptable and effective.展开更多
Purpose The purpose of this paper is to study a new method to improve the performance of the magnet power supply in the experimental ring of HIRFL-CSR.Methods A hybrid genetic particle swarm optimization algorithm is ...Purpose The purpose of this paper is to study a new method to improve the performance of the magnet power supply in the experimental ring of HIRFL-CSR.Methods A hybrid genetic particle swarm optimization algorithm is introduced,and the algorithm is applied to the optimal design of the LQR controller of pulse width modulated power supply.The fitness function of hybrid genetic particle swarm optimization is a multi-objective function,which combined the current and voltage,so that the dynamic performance of the closed-loop system can be better.The hybrid genetic particle swarm algorithm is applied to determine LQR controlling matrices Q and R.Results The simulation results show that adoption of this method leads to good transient responses,and the computational time is shorter than in the traditional trial and error methods.Conclusions The results presented in this paper show that the proposed method is robust,efficient and feasible,and the dynamic and static performance of the accelerator PWM power supply has been considerably improved.展开更多
This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm(PLTVACIW-PSO).Its designed has introduced the benefits of Parallel computing ...This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm(PLTVACIW-PSO).Its designed has introduced the benefits of Parallel computing into the combined power of TVAC(Time-Variant Acceleration Coefficients)and IW(Inertial Weight).Proposed algorithm has been tested against linear,non-linear,traditional,andmultiswarmbased optimization algorithms.An experimental study is performed in two stages to assess the proposed PLTVACIW-PSO.Phase I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIWPSO vs.IW based Particle Swarm Optimization(PSO)algorithms,TVAC based PSO algorithms,traditional PSO,Genetic algorithms(GA),Differential evolution(DE),and,finally,Flower Pollination(FP)algorithms.In phase II,the proposed PLTVACIW-PSO uses the same 12 known Benchmark functions to test its performance against the BAT(BA)and Multi-Swarm BAT algorithms.In phase III,the proposed PLTVACIW-PSO is employed to augment the feature selection problem formedical datasets.This experimental study shows that the planned PLTVACIW-PSO outpaces the performances of other comparable algorithms.Outcomes from the experiments shows that the PLTVACIW-PSO is capable of outlining a feature subset that is capable of enhancing the classification efficiency and gives the minimal subset of the core features.展开更多
The intent of this paper is to schedule short-term hydrothermal system probabilistically considering stochastic operating cost curves for thermal power generation units and uncertainties in load demand and reservoir w...The intent of this paper is to schedule short-term hydrothermal system probabilistically considering stochastic operating cost curves for thermal power generation units and uncertainties in load demand and reservoir water inflows. Therefore, the stochastic multi-objective hydrothermal generation scheduling problem is formulated with explicit recognition of uncertainties in the system production cost coefficients and system load, which are treated as random variable. Fuzzy methodology has been exploited for solving a decision making problem involving multiplicity of objectives and selection criterion for best compromised solution. A real-coded genetic algorithm with arithmetic-average-bound-blend crossover and wavelet mutation operator is applied to solve short-term variable-head hydrothermal scheduling problem. Initial feasible solution has been obtained by implementing the random heuristic search. The search is performed within the operating generation limits. Equality constraints that satisfy the demand during each time interval are considered by introducing a slack thermal generating unit for each time interval. Whereas the equality constraint which satisfies the consumption of available water to its full extent for the whole scheduling period is considered by introducing slack hydro generating unit for a particular time interval. Operating limit violation by slack hydro and slack thermal generating unit is taken care using exterior penalty method. The effectiveness of the proposed method is demonstrated on two sample systems.展开更多
基金Project(2007CB714006) supported by the National Basic Research Program of China Project(90815023) supported by the National Natural Science Foundation of China
文摘The hybrid genetic algorithm is utilized to facilitate model parameter estimation.The tri-dimensional compression tests of soil are performed to supply experimental data for identifying nonlinear constitutive model of soil.In order to save computing time during parameter inversion,a new procedure to compute the calculated strains is presented by multi-linear simplification approach instead of finite element method(FEM).The real-coded hybrid genetic algorithm is developed by combining normal genetic algorithm with gradient-based optimization algorithm.The numerical and experimental results for conditioned soil are compared.The forecast strains based on identified nonlinear constitutive model of soil agree well with observed ones.The effectiveness and accuracy of proposed parameter estimation approach are validated.
基金supported by National Natural Science Foundation of China(No.50575153)Provincial Key Technology Projects of Sichuan,China(No.03GG010-002)
文摘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.
文摘In Systems Biology, system identification, which infers regulatory network in genetic system and metabolic pathways using experimentally observed time-course data, is one of the hottest issues. The efficient numerical optimization algorithm to estimate more than 100 real-coded parameters should be developed for this purpose. New real-coded genetic algorithm (RCGA), the combination of AREX (adaptive real-coded ensemble crossover) with JGG (just generation gap), have applied to the inference of genetic interactions involving more than 100 parameters related to the interactions with using experimentally observed time-course data. Compared with conventional RCGA, the combination of UNDX (unimodal normal distribution crossover) with MGG (minimal generation gap), new algorithm has shown the superiority with improving early convergence in the first stage of search and suppressing evolutionary stagnation in the last stage of search.
文摘During the process of enterprises＇ strategy evaluation and selection, there are many evaluating indicators, and among them there are some potential correlations and conflicts. Thus it poses the problems to the decision-makers how to conduct correct evaluation on a business and how to make strategy adjustment and selection according to the evaluation. Based on the qualitative and quantitative method, the paper introduces the Projection Pursuit Classification （PPC） model based on the Real-coded Accelerating Genetic Algorithm （RAGA） into the process of enterprises＇ strategy evaluation and selection. The characteristic of PPC model is that it ultimately overcomes the influence of the proportion of subjectivity and avoids precocious convergence, thus providing a new objective method for strategy evaluation and selection by pursuing the most objective strategy evaluation to make the relatively sensible strategy portfolio and action.
文摘We present a global optimization method, called the real-code genetic algorithm (RGA), to the ground state energies. The proposed method does not require partial derivatives with respect to each variational parameter or solving an eigenequation, so the present method overcomes the major difficulties of the variational method. RGAs also do not require coding and encoding procedures, so the computation time and complexity are reduced. The ground state energies of hydrogenic donors in GaAs-(Ga,Al)As quantum dots have been calculated for a range of the radius of the quantum dot radii of practical interest. They are compared with those obtained by the variational method. The results obtained demonstrate the proposed method is simple, accurate, and easy implement.
文摘In this paper, the main objective is to identify the parameters of motors, which includes a brushless direct current (BLDC) motor and an induction motor. The motor systems are dynamically formulated by the mechanical and electrical equations. The real-coded genetic algorithm (RGA) is adopted to identify all parameters of motors, and the standard genetic algorithm (SRGA) and various adaptive genetic algorithm (ARGAs) are compared in the rotational angular speeds and fitness values, which are the inverse of square differences of angular speeds. From numerical simulations and experimental results, it is found that the SRGA and ARGA are feasible, the ARGA can effectively solve the problems with slow convergent speed and premature phenomenon, and is more accurate in identifying system’s parameters than the SRGA. From the comparisons of the ARGAs in identifying parameters of motors, the best ARGA method is obtained and could be applied to any other mechatronic systems.
基金supported by the Science and Technology Major Project of Hubei Province,China (No.2021AFB001).
文摘High-brightness electron beams are required to drive LINAC-based free-electron lasers(FELs)and storage-ring-based synchrotron radiation light sources.The bunch charge and RMS bunch length at the exit of the LINAC play a crucial role in the peak current;the minimum transverse emittance is mainly determined by the injector of the LINAC.Thus,a photoin-jector with a high bunch charge and low emittance that can simultaneously provide high-quality beams for 4th generation synchrotron radiation sources and FELs is desirable.The design of a 1.6-cell S-band 2998-MHz RF gun and beam dynamics optimization of a relevant beamline are presented in this paper.Beam dynamics simulations were performed by combining ASTRA and the multi-objective genetic algorithm NSGA II.The effects of the laser pulse shape,half-cell length of the RF gun,and RF parameters on the output beam quality were analyzed and compared.The normalized transverse emittance was optimized to be as low as 0.65 and 0.92 mm·mrad when the bunch charge was as high as 1 and 2 nC,respectively.Finally,the beam stability properties of the photoinjector,considering misalignment and RF jitter,were simulated and analyzed.
文摘A numerical procedure for hydrodynamic redesign of the conventional vaned diffuser into the low solidity vaned diffuser by means of a real-coded genetic algorithm with Boltzmann, Tournament and Roulette Wheel selection is presented. In the first part, an investigation on the relative efficiency of the different real-coded genetic algorithm is carried out on a typical mathematical test function. The real-coded genetic algorithm with Boltzmann selection shows the best optimization performance compared to the Tournament and Roulette Wheel selection. In the second part, an approach to redesign the vaned diffuser profile is introduced. Goal of the optimum design is to search the highest static pressure recovery coefficient and low solidity vaned diffuser. The result of the low solidity vaned diffuser optimum design confirms that the efficiency and optimization performance of the real-coded Boltzmann selection genetic algorithm outperforms the other selection methods. A comparison between the designed low solidity vaned diffuser and original vaned diffuser shows that the diffuser pump with the redesigned low solidity vaned diffuser has the higher static pressure recovery and improved total hydrodynamic performance. In addition, the smaller outlet diameter of designed vaned diffuser tends to a more compact size of diffuser pump compared to the original diffuser pump. The obtained results also demonstrate the real-coded Boltzmann selection genetic algorithm is a promising optimization algorithm for centrifugal pumps design.
基金co-supported by National Natural Science Foundation of China(Grant No.51505015,51575019)the National Basic Research Program of China(No.2014CB046402)CAST-BISEE Innovation Foundation of China
文摘Harmonic drives have various distinctive advantages and are widely used in space drive mechanisms. Accelerated life test （ALT） is commonly conducted to shorten test time and reduce associated costs. An appropriate ALT modet is needed to predict the lifetime of harmonic drives with ALT data. However, harmonic drives which are used in space usually work under a segmental stress history, and traditional ALT models can hardly be used in this situation. This paper proposes a dedicated ALT model for harmonic drives applied in space systems. A comprehensive ALT model is established and genetic algorithm （GA） is adopted to obtain optimal parameters in the model using the Manson fatigue damage rule to describe the fatigue failure process and a cumulative dam- age method to calculate and accumulate the damage caused by each segment in the stress history. An ALT of harmonic drives was carried out and experimental results show that this model is acceptable and effective.
文摘Purpose The purpose of this paper is to study a new method to improve the performance of the magnet power supply in the experimental ring of HIRFL-CSR.Methods A hybrid genetic particle swarm optimization algorithm is introduced,and the algorithm is applied to the optimal design of the LQR controller of pulse width modulated power supply.The fitness function of hybrid genetic particle swarm optimization is a multi-objective function,which combined the current and voltage,so that the dynamic performance of the closed-loop system can be better.The hybrid genetic particle swarm algorithm is applied to determine LQR controlling matrices Q and R.Results The simulation results show that adoption of this method leads to good transient responses,and the computational time is shorter than in the traditional trial and error methods.Conclusions The results presented in this paper show that the proposed method is robust,efficient and feasible,and the dynamic and static performance of the accelerator PWM power supply has been considerably improved.
基金funded by the Prince Sultan University,Riyadh,Saudi Arabia.
文摘This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm(PLTVACIW-PSO).Its designed has introduced the benefits of Parallel computing into the combined power of TVAC(Time-Variant Acceleration Coefficients)and IW(Inertial Weight).Proposed algorithm has been tested against linear,non-linear,traditional,andmultiswarmbased optimization algorithms.An experimental study is performed in two stages to assess the proposed PLTVACIW-PSO.Phase I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIWPSO vs.IW based Particle Swarm Optimization(PSO)algorithms,TVAC based PSO algorithms,traditional PSO,Genetic algorithms(GA),Differential evolution(DE),and,finally,Flower Pollination(FP)algorithms.In phase II,the proposed PLTVACIW-PSO uses the same 12 known Benchmark functions to test its performance against the BAT(BA)and Multi-Swarm BAT algorithms.In phase III,the proposed PLTVACIW-PSO is employed to augment the feature selection problem formedical datasets.This experimental study shows that the planned PLTVACIW-PSO outpaces the performances of other comparable algorithms.Outcomes from the experiments shows that the PLTVACIW-PSO is capable of outlining a feature subset that is capable of enhancing the classification efficiency and gives the minimal subset of the core features.
文摘The intent of this paper is to schedule short-term hydrothermal system probabilistically considering stochastic operating cost curves for thermal power generation units and uncertainties in load demand and reservoir water inflows. Therefore, the stochastic multi-objective hydrothermal generation scheduling problem is formulated with explicit recognition of uncertainties in the system production cost coefficients and system load, which are treated as random variable. Fuzzy methodology has been exploited for solving a decision making problem involving multiplicity of objectives and selection criterion for best compromised solution. A real-coded genetic algorithm with arithmetic-average-bound-blend crossover and wavelet mutation operator is applied to solve short-term variable-head hydrothermal scheduling problem. Initial feasible solution has been obtained by implementing the random heuristic search. The search is performed within the operating generation limits. Equality constraints that satisfy the demand during each time interval are considered by introducing a slack thermal generating unit for each time interval. Whereas the equality constraint which satisfies the consumption of available water to its full extent for the whole scheduling period is considered by introducing slack hydro generating unit for a particular time interval. Operating limit violation by slack hydro and slack thermal generating unit is taken care using exterior penalty method. The effectiveness of the proposed method is demonstrated on two sample systems.
