This paper presents an Improved Catastrophic Genetic Algorithm (ICGA) for optimal reactive power optimization. Firstly, a new catastrophic operator to enhance the genetic algorithms’ convergence stability is proposed...This paper presents an Improved Catastrophic Genetic Algorithm (ICGA) for optimal reactive power optimization. Firstly, a new catastrophic operator to enhance the genetic algorithms’ convergence stability is proposed. Then, a new probability algorithm of crossover depending on the number of generations, and a new probability algorithm of mutation depending on the fitness value are designed to solving the main conflict of the convergent speed with the global astringency. In these ways, the ICGA can prevent premature convergence and instability of genetic-catastrophic algorithms (GCA). Finally, the ICGA is applied for power system reactive power optimization and evaluated on the IEEE 14-bus power system, and the application results show that the proposed method is suitable for reactive power optimization in power system.展开更多
Tis paper presents a genetic algorithm for reactive power optimization of power system in a more effective and rapid manner, and verifies the results with an IEEE 30-bus test system.
An adaptive immune-genetic algorithm (AIGA) is proposed to avoid premature convergence and guarantee the diversity of the population. Rapid immune response (secondary response), adaptive mutation and density opera...An adaptive immune-genetic algorithm (AIGA) is proposed to avoid premature convergence and guarantee the diversity of the population. Rapid immune response (secondary response), adaptive mutation and density operators in the AIGA are emphatically designed to improve the searching ability, greatly increase the converging speed, and decrease locating the local maxima due to the premature convergence. The simulation results obtained from the global optimization to four multivariable and multi-extreme functions show that AIGA converges rapidly, guarantees the diversity, stability and good searching ability.展开更多
Aiming at the demand for optimization of hydrodynamic coefficients in submarine's motion equations,an adaptive weight immune genetic algorithm was proposed to optimize hydrodynamic coefficients in motion equations...Aiming at the demand for optimization of hydrodynamic coefficients in submarine's motion equations,an adaptive weight immune genetic algorithm was proposed to optimize hydrodynamic coefficients in motion equations.Some hydrodynamic coefficients of high sensitivity to control and maneuver were chosen as the optimization objects in the algorithm.By using adaptive weight method to determine the weight and target function,the multi-objective optimization could be translated into single-objective optimization.For a certain kind of submarine,three typical maneuvers were chosen to be the objects of study:overshoot maneuver in horizontal plane,overshoot maneuver in vertical plane and turning circle maneuver in horizontal plane.From the results of computer simulations using primal hydrodynamic coefficient and optimized hydrodynamic coefficient,the efficiency of proposed method is proved.展开更多
As in the building of deep buried long tunnels,there are complicated conditions such as great deformation,high stress,multi-variables,high non-linearity and so on,the algorithm for structure optimization and its appli...As in the building of deep buried long tunnels,there are complicated conditions such as great deformation,high stress,multi-variables,high non-linearity and so on,the algorithm for structure optimization and its application in tunnel engineering are still in the starting stage. Along with the rapid development of highways across the country,it has become a very urgent task to be tackled to carry out the optimization design of the structure of the section of the tunnel to lessen excavation workload and to reinforce the support. Artificial intelligence demonstrates an extremely strong capability of identifying,expressing and disposing such kind of multiple variables and complicated non-linear relations. In this paper,a comprehensive consideration of the strategy of the selection and updating of the concentration and adaptability of the immune algorithm is made to replace the selection mode in the original genetic algorithm which depends simply on the adaptability value. Such an algorithm has the advantages of both the immune algorithm and the genetic algorithm,thus serving the purpose of not only enhancing the individual adaptability but maintaining the individual diversity as well. By use of the identifying function of the antigen memory,the global search capability of the immune genetic algorithm is raised,thereby avoiding the occurrence of the premature phenomenon. By optimizing the structure of the section of the Huayuan tunnel,the current excavation area and support design are adjusted. A conclusion with applicable value is arrived at. At a higher computational speed and a higher efficiency,the current method is verified to have advantages in the optimization computation of the tunnel project. This also suggests that the application of the immune genetic algorithm has a practical significance to the stability assessment and informationization design of the wall rock of the tunnel.展开更多
To deduce error and fussy work of manual adjustment of parameters for an S-surface controller in underwater vehicle motion control, the immune-genetic optimization of S-surface controller of an underwater vehicle was ...To deduce error and fussy work of manual adjustment of parameters for an S-surface controller in underwater vehicle motion control, the immune-genetic optimization of S-surface controller of an underwater vehicle was proposed. The ability of producing various antibodies for the immune algorithm, the self-adjustment of antibody density, and the antigen immune memory were used to realize the rapid convergence of S-surface controller parameters. It avoided loitering near the local peak value. Deduction of the S-surface controller was given. General process of the immune-genetic algorithm was described and immune-genetic optimization of S-surface controller parameters was discussed. Definitive results were obtained from many simulation experiments and lake experiments, which indicate that the algorithm can get good effect in optimizing the nonlinear motion controller parameters of an underwater vehicle.展开更多
A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed, which is called the immune genetic algorithm with the elitism (IGAE). In IGAE, the new methods for computing antibody sim...A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed, which is called the immune genetic algorithm with the elitism (IGAE). In IGAE, the new methods for computing antibody similarity, expected reproduction probability, and clonal selection probability were given. IGAE has three features. The first is that the similarities of two antibodies in structure and quality are all defined in the form of percentage, which helps to describe the similarity of two antibodies more accurately and to reduce the computational burden effectively. The second is that with the elitist selection and elitist crossover strategy IGAE is able to find the globally optimal solution of a given problem. The third is that the formula of expected reproduction probability of antibody can be adjusted through a parameter β, which helps to balance the population diversity and the convergence speed of IGAE so that IGAE can find the globally optimal solution of a given problem more rapidly. Two different complex multi-modal functions were selected to test the validity of IGAE. The experimental results show that IGAE can find the globally maximum/minimum values of the two functions rapidly. The experimental results also confirm that IGAE is of better performance in convergence speed, solution variation behavior, and computational efficiency compared with the canonical genetic algorithm with the elitism and the immune genetic algorithm with the information entropy and elitism.展开更多
A neural network model of key process parameters and forming quality is developed based on training samples which are obtained from the orthogonal experiment and the finite element numerical simulation. Optimization o...A neural network model of key process parameters and forming quality is developed based on training samples which are obtained from the orthogonal experiment and the finite element numerical simulation. Optimization of the process parameters is conducted using the genetic algorithm (GA). The experimental results have shown that a surface model of the neural network can describe the nonlinear implicit relationship between the parameters of the power spinning process:the wall margin and amount of expansion. It has been found that the process of determining spinning technological parameters can be accelerated using the optimization method developed based on the BP neural network and the genetic algorithm used for the process parameters of power spinning formation. It is undoubtedly beneficial towards engineering applications.展开更多
A computing model employing the immune and genetic algorithm (IGA) for the optimization of part design is presented. This model operates on a population of points in search space simultaneously, not on just one point....A computing model employing the immune and genetic algorithm (IGA) for the optimization of part design is presented. This model operates on a population of points in search space simultaneously, not on just one point. It uses the objective function itself, not derivative or any other additional information and guarantees the fast convergence toward the global optimum. This method avoids some weak points in genetic algorithm, such as inefficient to some local searching problems and its convergence is too early. Based on this model, an optimal design support system (IGBODS) is developed.IGBODS has been used in practice and the result shows that this model has great advantage than traditional one and promises good application in optimal design.展开更多
Considering the factors affecting the increasing rate of power consumption, the BP neural network structure and the neural network forecasting model of the increasing rate of power consumption were established. Immune...Considering the factors affecting the increasing rate of power consumption, the BP neural network structure and the neural network forecasting model of the increasing rate of power consumption were established. Immune genetic algorithm was applied to optimizing the weight from input layer to hidden layer, from hidden layer to output layer, and the threshold value of neuron nodes in hidden and output layers. Finally, training the related data of the increasing rate of power consumption from 1980 to 2000 in China, a nonlinear network model between the increasing rate of power consumption and influencing factors was obtained. The model was adopted to forecasting the increasing rate of power consumption from 2001 to 2005, and the average absolute error ratio of forecasting results is 13.521 8%. Compared with the ordinary neural network optimized by genetic algorithm, the results show that this method has better forecasting accuracy and stability for forecasting the increasing rate of power consumption.展开更多
Considering the soft constraint characteristics of voltage constraints, the Interior-Point Filter Algorithm is applied to solve the formulation of fuzzy model for the power system reactive power optimization with a la...Considering the soft constraint characteristics of voltage constraints, the Interior-Point Filter Algorithm is applied to solve the formulation of fuzzy model for the power system reactive power optimization with a large number of equality and inequality constraints. Based on the primal-dual interior-point algorithm, the algorithm maintains an updating “filter” at each iteration in order to decide whether to admit correction of iteration point which can avoid effectively oscillation due to the conflict between the decrease of objective function and the satisfaction of constraints and ensure the global convergence. Moreover, the “filter” improves computational efficiency because it filters the unnecessary iteration points. The calculation results of a practical power system indicate that the algorithm can effectively deal with the large number of inequality constraints of the fuzzy model of reactive power optimization and satisfy the requirement of online calculation which realizes to decrease the network loss and maintain specified margins of voltage.展开更多
The vehicle model of the recirculating ball-type electric power steering (EPS) system for the pure electric bus was built. According to the features of constrained optimization for multi-variable function, a multi-obj...The vehicle model of the recirculating ball-type electric power steering (EPS) system for the pure electric bus was built. According to the features of constrained optimization for multi-variable function, a multi-objective genetic algorithm (GA) was designed. Based on the model of system, the quantitative formula of the road feel, sensitivity, and operation stability of the steering were induced. Considering the road feel and sensitivity of steering as optimization objectives, and the operation stability of steering as constraint, the multi-objective GA was proposed and the system parameters were optimized. The simulation results show that the system optimized by multi-objective genetic algorithm has better road feel, steering sensibility and steering stability. The energy of steering road feel after optimization is 1.44 times larger than the one before optimization, and the energy of portability after optimization is 0.4 times larger than the one before optimization. The ground test was conducted in order to verify the feasibility of simulation results, and it is shown that the pure electric bus equipped with the recirculating ball-type EPS system can provide better road feel and better steering portability for the drivers, thus the optimization methods can provide a theoretical basis for the design and optimization of the recirculating ball-type EPS system.展开更多
Using state assignment to minimize power dissipation and area for finite state ma-chines is computationally hard. Most of published results show that the reduction of switchingactivity often trades with area penalty. ...Using state assignment to minimize power dissipation and area for finite state ma-chines is computationally hard. Most of published results show that the reduction of switchingactivity often trades with area penalty. In this paper, a new approach is proposed. Experimentalresults show a significant reduction of switching activity without area penalty compared withprevious publications.展开更多
In this paper, a new optimization system based genetic algorithm is presented. Our approach integrates the merits of both ant colony optimization and genetic algorithm and it has two characteristic features. Firstly, ...In this paper, a new optimization system based genetic algorithm is presented. Our approach integrates the merits of both ant colony optimization and genetic algorithm and it has two characteristic features. Firstly, since there is instabilities in the global market, implications of global financial crisis and the rapid fluctuations of prices, a fuzzy representation of the optimal power flow problem has been defined, where the input data involve many parameters whose possible values may be assigned by the expert. Secondly, by enhancing ant colony optimization through genetic algorithm, a strong robustness and more effectively algorithm was created. Also, stable Pareto set of solutions has been detected, where in a practical sense only Pareto optimal solutions that are stable are of interest since there are always uncertainties associated with efficiency data. The results on the standard IEEE systems demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal nondominated solutions of the multiobjective OPF.展开更多
For the structural-acoustic radiation optimization problem under external loading,acoustic radiation power was considered to be an objective function in the optimization method. The finite element method(FEM) and boun...For the structural-acoustic radiation optimization problem under external loading,acoustic radiation power was considered to be an objective function in the optimization method. The finite element method(FEM) and boundary element method(BEM) were adopted in numerical calculations,and structural response and the acoustic response were assumed to be de-coupled in the analysis. A genetic algorithm was used as the strategy in optimization. In order to build the relational expression of the pressure objective function and the power objective function,the enveloping surface model was used to evaluate pressure in the acoustic domain. By taking the stiffened panel structural-acoustic optimization problem as an example,the acoustic power and field pressure after optimized was compared. Optimization results prove that this method is reasonable and effective.展开更多
In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm (ACOA) was used to optimize the reactive power compensation for the rural distribution system. In this stud...In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm (ACOA) was used to optimize the reactive power compensation for the rural distribution system. In this study, the traditional ACOA was improved in two aspects: one was the local search strategy, and the other was pheromone mutation and re-initialization strategies. The reactive power optimization for a county's distribution network showed that the improved ACOA was practicable.展开更多
Optimization of the open absorption desiccant cooling system has been carried out in the present work. A finite difference method is used to simulate the combined heat and mass transfer processes that occur in the liq...Optimization of the open absorption desiccant cooling system has been carried out in the present work. A finite difference method is used to simulate the combined heat and mass transfer processes that occur in the liquid desiccant regenerator which uses calcium chloride (CaCl2) solution as the working desiccant. The source of input heat is assumed to be the total radiation incident on a tilted surface. The system of equations is solved using the Matlab-Simulink platform. The effect of the important parameters, namely the regenerator length, desiccant solution flow rate and concentration, and air flow rates, on the performance of the system is investigated. In order to optimize the system performance, a genetic algorithm technique has been applied. The system coefficient of performance COP has been maximized for different design parameters. It has been found that the maximum values of COP could be obtained for different combinations of regenerator length solution flow rate and air flow rate. Therefore, it is essential to select the design parameters for each ambient condition to maximize the performance of the system.展开更多
The optimal allocation of integrated energy systemcapacity based on the heuristic algorithms can reduce economic costs and achieve maximum consumption of renewable energy,which has attracted many attentions.However,th...The optimal allocation of integrated energy systemcapacity based on the heuristic algorithms can reduce economic costs and achieve maximum consumption of renewable energy,which has attracted many attentions.However,the optimization results of heuristic algorithms are usually influenced by the choice of hyperparameters.To solve the above problem,the particle swarm algorithm is introduced to find the optimal hyperparameters of the heuristic algorithms.Firstly,an integrated energy system consisting of the photovoltaic,wind turbine,electrolysis cell,hydrogen storage tank,and energy storage is established.Meanwhile,the minimum economic cost,the maximum wind and PV power consumption rate,and the minimum load shortage rate are considered to be the objective functions.Then,a hybrid method combined the particle swarm combined with non-dominated sorting genetic algorithms-II is proposed to solve the optimal allocation problem.According to the optimal result,the economic cost is 6.3 million RMB,and the load shortage rate is 9.83%.Finally,four comparative experiments are conducted to verify the superiority-seeking ability of the proposed method.The comparative results indicate that the proposed method possesses a strongermerit-seeking ability,resulting in a solution satisfaction rate of 87.37%,which is higher than that of the unimproved non-dominated sorting genetic algorithms-II.展开更多
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
Binary Decision Diagrams (BDDs) can be graphically manipulated to reduce the number of nodes and hence the area. In this context, ordering of BDDs play a major role. Most of the algorithms for input variable ordering ...Binary Decision Diagrams (BDDs) can be graphically manipulated to reduce the number of nodes and hence the area. In this context, ordering of BDDs play a major role. Most of the algorithms for input variable ordering of OBDD focus primarily on area minimization. However, suitable input variable ordering helps in minimizing the power consumption also. In this particular work, we have proposed two algorithms namely, a genetic algorithm based technique and a branch and bound algorithm to find an optimal input variable order. Of course, the node reordering is taken care of by the standard BDD package buddy-2.4. Moreover, we have evaluated the performances of the proposed algorithms by running an exhaustive search program. Experi-mental results show a substantial saving in area and power. We have also compared our techniques with other state-of-art techniques of variable ordering for OBDDs and found to give superior results.展开更多
文摘This paper presents an Improved Catastrophic Genetic Algorithm (ICGA) for optimal reactive power optimization. Firstly, a new catastrophic operator to enhance the genetic algorithms’ convergence stability is proposed. Then, a new probability algorithm of crossover depending on the number of generations, and a new probability algorithm of mutation depending on the fitness value are designed to solving the main conflict of the convergent speed with the global astringency. In these ways, the ICGA can prevent premature convergence and instability of genetic-catastrophic algorithms (GCA). Finally, the ICGA is applied for power system reactive power optimization and evaluated on the IEEE 14-bus power system, and the application results show that the proposed method is suitable for reactive power optimization in power system.
文摘Tis paper presents a genetic algorithm for reactive power optimization of power system in a more effective and rapid manner, and verifies the results with an IEEE 30-bus test system.
基金the Research Fund for the Doctoral Program of Higher Education of China (20020008004).
文摘An adaptive immune-genetic algorithm (AIGA) is proposed to avoid premature convergence and guarantee the diversity of the population. Rapid immune response (secondary response), adaptive mutation and density operators in the AIGA are emphatically designed to improve the searching ability, greatly increase the converging speed, and decrease locating the local maxima due to the premature convergence. The simulation results obtained from the global optimization to four multivariable and multi-extreme functions show that AIGA converges rapidly, guarantees the diversity, stability and good searching ability.
文摘Aiming at the demand for optimization of hydrodynamic coefficients in submarine's motion equations,an adaptive weight immune genetic algorithm was proposed to optimize hydrodynamic coefficients in motion equations.Some hydrodynamic coefficients of high sensitivity to control and maneuver were chosen as the optimization objects in the algorithm.By using adaptive weight method to determine the weight and target function,the multi-objective optimization could be translated into single-objective optimization.For a certain kind of submarine,three typical maneuvers were chosen to be the objects of study:overshoot maneuver in horizontal plane,overshoot maneuver in vertical plane and turning circle maneuver in horizontal plane.From the results of computer simulations using primal hydrodynamic coefficient and optimized hydrodynamic coefficient,the efficiency of proposed method is proved.
基金supported by the National Natural Science Foundation of China (No.50808090)
文摘As in the building of deep buried long tunnels,there are complicated conditions such as great deformation,high stress,multi-variables,high non-linearity and so on,the algorithm for structure optimization and its application in tunnel engineering are still in the starting stage. Along with the rapid development of highways across the country,it has become a very urgent task to be tackled to carry out the optimization design of the structure of the section of the tunnel to lessen excavation workload and to reinforce the support. Artificial intelligence demonstrates an extremely strong capability of identifying,expressing and disposing such kind of multiple variables and complicated non-linear relations. In this paper,a comprehensive consideration of the strategy of the selection and updating of the concentration and adaptability of the immune algorithm is made to replace the selection mode in the original genetic algorithm which depends simply on the adaptability value. Such an algorithm has the advantages of both the immune algorithm and the genetic algorithm,thus serving the purpose of not only enhancing the individual adaptability but maintaining the individual diversity as well. By use of the identifying function of the antigen memory,the global search capability of the immune genetic algorithm is raised,thereby avoiding the occurrence of the premature phenomenon. By optimizing the structure of the section of the Huayuan tunnel,the current excavation area and support design are adjusted. A conclusion with applicable value is arrived at. At a higher computational speed and a higher efficiency,the current method is verified to have advantages in the optimization computation of the tunnel project. This also suggests that the application of the immune genetic algorithm has a practical significance to the stability assessment and informationization design of the wall rock of the tunnel.
文摘To deduce error and fussy work of manual adjustment of parameters for an S-surface controller in underwater vehicle motion control, the immune-genetic optimization of S-surface controller of an underwater vehicle was proposed. The ability of producing various antibodies for the immune algorithm, the self-adjustment of antibody density, and the antigen immune memory were used to realize the rapid convergence of S-surface controller parameters. It avoided loitering near the local peak value. Deduction of the S-surface controller was given. General process of the immune-genetic algorithm was described and immune-genetic optimization of S-surface controller parameters was discussed. Definitive results were obtained from many simulation experiments and lake experiments, which indicate that the algorithm can get good effect in optimizing the nonlinear motion controller parameters of an underwater vehicle.
基金Project(50275150) supported by the National Natural Science Foundation of ChinaProjects(20040533035, 20070533131) supported by the National Research Foundation for the Doctoral Program of Higher Education of China
文摘A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed, which is called the immune genetic algorithm with the elitism (IGAE). In IGAE, the new methods for computing antibody similarity, expected reproduction probability, and clonal selection probability were given. IGAE has three features. The first is that the similarities of two antibodies in structure and quality are all defined in the form of percentage, which helps to describe the similarity of two antibodies more accurately and to reduce the computational burden effectively. The second is that with the elitist selection and elitist crossover strategy IGAE is able to find the globally optimal solution of a given problem. The third is that the formula of expected reproduction probability of antibody can be adjusted through a parameter β, which helps to balance the population diversity and the convergence speed of IGAE so that IGAE can find the globally optimal solution of a given problem more rapidly. Two different complex multi-modal functions were selected to test the validity of IGAE. The experimental results show that IGAE can find the globally maximum/minimum values of the two functions rapidly. The experimental results also confirm that IGAE is of better performance in convergence speed, solution variation behavior, and computational efficiency compared with the canonical genetic algorithm with the elitism and the immune genetic algorithm with the information entropy and elitism.
基金Supported by the Natural Science Foundation of Shanxi Province Project(2012011023-2)
文摘A neural network model of key process parameters and forming quality is developed based on training samples which are obtained from the orthogonal experiment and the finite element numerical simulation. Optimization of the process parameters is conducted using the genetic algorithm (GA). The experimental results have shown that a surface model of the neural network can describe the nonlinear implicit relationship between the parameters of the power spinning process:the wall margin and amount of expansion. It has been found that the process of determining spinning technological parameters can be accelerated using the optimization method developed based on the BP neural network and the genetic algorithm used for the process parameters of power spinning formation. It is undoubtedly beneficial towards engineering applications.
文摘A computing model employing the immune and genetic algorithm (IGA) for the optimization of part design is presented. This model operates on a population of points in search space simultaneously, not on just one point. It uses the objective function itself, not derivative or any other additional information and guarantees the fast convergence toward the global optimum. This method avoids some weak points in genetic algorithm, such as inefficient to some local searching problems and its convergence is too early. Based on this model, an optimal design support system (IGBODS) is developed.IGBODS has been used in practice and the result shows that this model has great advantage than traditional one and promises good application in optimal design.
基金Project(70373017) supported by the National Natural Science Foundation of China
文摘Considering the factors affecting the increasing rate of power consumption, the BP neural network structure and the neural network forecasting model of the increasing rate of power consumption were established. Immune genetic algorithm was applied to optimizing the weight from input layer to hidden layer, from hidden layer to output layer, and the threshold value of neuron nodes in hidden and output layers. Finally, training the related data of the increasing rate of power consumption from 1980 to 2000 in China, a nonlinear network model between the increasing rate of power consumption and influencing factors was obtained. The model was adopted to forecasting the increasing rate of power consumption from 2001 to 2005, and the average absolute error ratio of forecasting results is 13.521 8%. Compared with the ordinary neural network optimized by genetic algorithm, the results show that this method has better forecasting accuracy and stability for forecasting the increasing rate of power consumption.
文摘Considering the soft constraint characteristics of voltage constraints, the Interior-Point Filter Algorithm is applied to solve the formulation of fuzzy model for the power system reactive power optimization with a large number of equality and inequality constraints. Based on the primal-dual interior-point algorithm, the algorithm maintains an updating “filter” at each iteration in order to decide whether to admit correction of iteration point which can avoid effectively oscillation due to the conflict between the decrease of objective function and the satisfaction of constraints and ensure the global convergence. Moreover, the “filter” improves computational efficiency because it filters the unnecessary iteration points. The calculation results of a practical power system indicate that the algorithm can effectively deal with the large number of inequality constraints of the fuzzy model of reactive power optimization and satisfy the requirement of online calculation which realizes to decrease the network loss and maintain specified margins of voltage.
基金Projects(51005115, 51005248) supported by the National Natural Science Foundation of ChinaProject(SKLMT-KFKT-201105)supported by the Visiting Scholar Foundation of State Key Laboratory of Mechanical Transmission in Chongqing University, ChinaProject(QC201101) supported by Visiting Scholar Foundation of the Automobile Engineering Key Laboratory of Jiangsu Province, China
文摘The vehicle model of the recirculating ball-type electric power steering (EPS) system for the pure electric bus was built. According to the features of constrained optimization for multi-variable function, a multi-objective genetic algorithm (GA) was designed. Based on the model of system, the quantitative formula of the road feel, sensitivity, and operation stability of the steering were induced. Considering the road feel and sensitivity of steering as optimization objectives, and the operation stability of steering as constraint, the multi-objective GA was proposed and the system parameters were optimized. The simulation results show that the system optimized by multi-objective genetic algorithm has better road feel, steering sensibility and steering stability. The energy of steering road feel after optimization is 1.44 times larger than the one before optimization, and the energy of portability after optimization is 0.4 times larger than the one before optimization. The ground test was conducted in order to verify the feasibility of simulation results, and it is shown that the pure electric bus equipped with the recirculating ball-type EPS system can provide better road feel and better steering portability for the drivers, thus the optimization methods can provide a theoretical basis for the design and optimization of the recirculating ball-type EPS system.
基金Supported by NNSF of China(Key International Cooperative Project No.60010121219)
文摘Using state assignment to minimize power dissipation and area for finite state ma-chines is computationally hard. Most of published results show that the reduction of switchingactivity often trades with area penalty. In this paper, a new approach is proposed. Experimentalresults show a significant reduction of switching activity without area penalty compared withprevious publications.
文摘In this paper, a new optimization system based genetic algorithm is presented. Our approach integrates the merits of both ant colony optimization and genetic algorithm and it has two characteristic features. Firstly, since there is instabilities in the global market, implications of global financial crisis and the rapid fluctuations of prices, a fuzzy representation of the optimal power flow problem has been defined, where the input data involve many parameters whose possible values may be assigned by the expert. Secondly, by enhancing ant colony optimization through genetic algorithm, a strong robustness and more effectively algorithm was created. Also, stable Pareto set of solutions has been detected, where in a practical sense only Pareto optimal solutions that are stable are of interest since there are always uncertainties associated with efficiency data. The results on the standard IEEE systems demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal nondominated solutions of the multiobjective OPF.
文摘For the structural-acoustic radiation optimization problem under external loading,acoustic radiation power was considered to be an objective function in the optimization method. The finite element method(FEM) and boundary element method(BEM) were adopted in numerical calculations,and structural response and the acoustic response were assumed to be de-coupled in the analysis. A genetic algorithm was used as the strategy in optimization. In order to build the relational expression of the pressure objective function and the power objective function,the enveloping surface model was used to evaluate pressure in the acoustic domain. By taking the stiffened panel structural-acoustic optimization problem as an example,the acoustic power and field pressure after optimized was compared. Optimization results prove that this method is reasonable and effective.
基金Supported by China Postdoctoral Science Foundation(20090460873)
文摘In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm (ACOA) was used to optimize the reactive power compensation for the rural distribution system. In this study, the traditional ACOA was improved in two aspects: one was the local search strategy, and the other was pheromone mutation and re-initialization strategies. The reactive power optimization for a county's distribution network showed that the improved ACOA was practicable.
文摘Optimization of the open absorption desiccant cooling system has been carried out in the present work. A finite difference method is used to simulate the combined heat and mass transfer processes that occur in the liquid desiccant regenerator which uses calcium chloride (CaCl2) solution as the working desiccant. The source of input heat is assumed to be the total radiation incident on a tilted surface. The system of equations is solved using the Matlab-Simulink platform. The effect of the important parameters, namely the regenerator length, desiccant solution flow rate and concentration, and air flow rates, on the performance of the system is investigated. In order to optimize the system performance, a genetic algorithm technique has been applied. The system coefficient of performance COP has been maximized for different design parameters. It has been found that the maximum values of COP could be obtained for different combinations of regenerator length solution flow rate and air flow rate. Therefore, it is essential to select the design parameters for each ambient condition to maximize the performance of the system.
基金supported in part by the Natural Science Foundation of Shandong Province(ZR2021QE289)in part by State Key Laboratory of Electrical Insulation and Power Equipment(EIPE22201).
文摘The optimal allocation of integrated energy systemcapacity based on the heuristic algorithms can reduce economic costs and achieve maximum consumption of renewable energy,which has attracted many attentions.However,the optimization results of heuristic algorithms are usually influenced by the choice of hyperparameters.To solve the above problem,the particle swarm algorithm is introduced to find the optimal hyperparameters of the heuristic algorithms.Firstly,an integrated energy system consisting of the photovoltaic,wind turbine,electrolysis cell,hydrogen storage tank,and energy storage is established.Meanwhile,the minimum economic cost,the maximum wind and PV power consumption rate,and the minimum load shortage rate are considered to be the objective functions.Then,a hybrid method combined the particle swarm combined with non-dominated sorting genetic algorithms-II is proposed to solve the optimal allocation problem.According to the optimal result,the economic cost is 6.3 million RMB,and the load shortage rate is 9.83%.Finally,four comparative experiments are conducted to verify the superiority-seeking ability of the proposed method.The comparative results indicate that the proposed method possesses a strongermerit-seeking ability,resulting in a solution satisfaction rate of 87.37%,which is higher than that of the unimproved non-dominated sorting genetic algorithms-II.
文摘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.
文摘Binary Decision Diagrams (BDDs) can be graphically manipulated to reduce the number of nodes and hence the area. In this context, ordering of BDDs play a major role. Most of the algorithms for input variable ordering of OBDD focus primarily on area minimization. However, suitable input variable ordering helps in minimizing the power consumption also. In this particular work, we have proposed two algorithms namely, a genetic algorithm based technique and a branch and bound algorithm to find an optimal input variable order. Of course, the node reordering is taken care of by the standard BDD package buddy-2.4. Moreover, we have evaluated the performances of the proposed algorithms by running an exhaustive search program. Experi-mental results show a substantial saving in area and power. We have also compared our techniques with other state-of-art techniques of variable ordering for OBDDs and found to give superior results.