The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation ...The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation deployment process was established,and the relationship between the deployment window and the phase difference of the orbit insertion point,as well as the cost of phase adjustment after orbit insertion,was derived.Then,the combination of the constellation deployment position sequence was treated as a parameter,together with the sequence of satellite deployment intervals,as optimization variables,simplifying a highdimensional search problem within a wide range of dates to a finite-dimensional integer programming problem.An improved genetic algorithm with local search on deployment dates was introduced to optimize the launch deployment strategy.With the new description of the optimization variables,the total number of elements in the solution space was reduced by N orders of magnitude.Numerical simulation confirms that the proposed optimization method accelerates the convergence speed from hours to minutes.展开更多
This paper introduced an integrated allocation model for distribution centers (DCs). The facility cost, inventory cost, transportation cost and service quality were considered in the model. An improved genetic algorit...This paper introduced an integrated allocation model for distribution centers (DCs). The facility cost, inventory cost, transportation cost and service quality were considered in the model. An improved genetic algorithm (IGA) was proposed to solve the problem. The improvement of IGA is based on the idea of adjusting crossover probability and mutation probability. The IGA is supplied by heuristic rules too. The simulation results show that the IGA is better than the standard GA(SGA) in search efficiency and equality.展开更多
The increase in bridge structure span and the complex stress characteristics directly affect the optimization of sensor placement,which in turn influences the data acquisition performance of the monitoring system.The ...The increase in bridge structure span and the complex stress characteristics directly affect the optimization of sensor placement,which in turn influences the data acquisition performance of the monitoring system.The key to the information acquisition of a bridge monitoring system is to obtain data that meets the health monitoring requirements of the bridge with a limited number of measurement points.To address this,a hybrid method based on multiple optimization criteria is proposed for optimal sensor placement(OSP).First,the minimum number of modes required for bridge monitoring is determined using the information entropy criterion(IE).Then,the number of measurement points is determined using a sequence method combined with the modal assurance criterion(MAC).Finally,the sensor placement is optimized using the generalized genetic algorithm(GGA)combined with double-structure encoding,and the optimization results are validated through finite element model analysis.The research results show that the hybrid method based on multiple optimization criteria can effectively determine the number of measurement points for bridge structures and optimize sensor placement,with a significant improvement in computational speed.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
A novel real coded improved genetic algorithm (GA) of training feed forward neural network is proposed to realize nonlinear system forecast. The improved GA employs a generation alternation model based the minimal gen...A novel real coded improved genetic algorithm (GA) of training feed forward neural network is proposed to realize nonlinear system forecast. The improved GA employs a generation alternation model based the minimal generation gap (MGP) and blend crossover operators (BLX α). Compared with traditional GA implemented in binary number, the processing time of the improved GA is faster because coding and decoding are unnecessary. In addition, it needn t set parameters such as the probability value of crossove...展开更多
Based on the slice method of the non-circular slip surface for the calculation of integral stability of slope, an improved genetic algorithm was proposed, which can freely search for the most dangerous slip surface of...Based on the slice method of the non-circular slip surface for the calculation of integral stability of slope, an improved genetic algorithm was proposed, which can freely search for the most dangerous slip surface of slope and the corresponding minimum safety factor without supposing the geometric shape of the most dangerous slip surface. This improved genetic algorithm can simulate the genetic evolution process of organisms and avoid the local minimum value compared with the classical methods. The results of engineering cases show that it is a global optimal algorithm and has many advantages, such as higher efficiency and shorter time than the simple genetic algorithm.展开更多
As an indispensable task in crop protection,the detection of crop diseases directly impacts the income of farmers.To address the problems of low crop-disease identification precision and detection abilities,a new meth...As an indispensable task in crop protection,the detection of crop diseases directly impacts the income of farmers.To address the problems of low crop-disease identification precision and detection abilities,a new method of detection is proposed based on improved genetic algorithm and extreme learning machine.Taking five different typical diseases with common crops as the objects,this method first preprocesses the images of crops and selects the optimal features for fusion.Then,it builds a model of crop disease identification for extreme learning machine,introduces the hill-climbing algorithm to improve the traditional genetic algorithm,optimizes the initial weights and thresholds of the machine,and acquires the approximately optimal solution.And finally,a data set of crop diseases is used for verification,demonstrating that,compared with several other common machine learning methods,this method can effectively improve the crop-disease identification precision and detection abilities and provide a basis for the identification of other crop diseases.展开更多
This paper deals with dynamic airspace sectorization （DAS） problem by an improved genetic algorithm （iGA）. A graph model is first constructed that represents the airspace static structure. Then the DAS problem is ...This paper deals with dynamic airspace sectorization （DAS） problem by an improved genetic algorithm （iGA）. A graph model is first constructed that represents the airspace static structure. Then the DAS problem is formulated as a graph-partitioning problem to balance the sector workload under the premise of ensuring safety. In the iGA, multiple populations and hybrid coding are applied to determine the optimal sector number and airspace sectorization. The sector constraints are well satisfied by the improved genetic operators and protect zones. This method is validated by being applied to the airspace of North China in terms of three indexes, which are sector balancing index, coordination workload index and sector average flight time index. The improvement is obvious, as the sector balancing index is reduced by 16.5 %, the coordination workload index is reduced by 11.2 %, and the sector average flight time index is increased by 11.4 % during the peak-hour traffic.展开更多
The UWB localization problem can be mapped as an optimization problem, which can be solved by genetic algorithm. In the localization process, the traditional fitness function does not include the ranging information b...The UWB localization problem can be mapped as an optimization problem, which can be solved by genetic algorithm. In the localization process, the traditional fitness function does not include the ranging information between tags, resulting in insufficient ranging information and limited improvement of the localization accuracy. In view of this, an improved genetic localization algorithm is proposed. First, a new fitness function is constructed, which not only includes the ranging information between the tag and the base station, but also the ranging information between the tags to ensure that the ranging information is fully utilized in the localization process. Then, the search method based on Brownian motion is adopted to ensure that the improved algorithm can speed up the convergence speed of the localization result. The simulation results show that, compared with the traditional genetic localization algorithm, the improved genetic localization algorithm can reduce the influence of the ranging error on the localization error and improve the localization performance.展开更多
Nowadays,energy consumption which closely contacts with environmental impacts of manufacturing processes has been highly commented as a new productivity criterion.However,little attention has paid to the development o...Nowadays,energy consumption which closely contacts with environmental impacts of manufacturing processes has been highly commented as a new productivity criterion.However,little attention has paid to the development of process planning methods that take energy consumption into account.An energy-efficient process planning model that incorporates manufacturing time and energy consumption is proposed.For solving the problem,an improved genetic algorithm method is employed to explore the optimal solution.Finally,a case study for process planning is given.The experimental result generates interesting effort,and therefore allows improving the energy efficiency of manufacturing processes in process planning.展开更多
Abstract Satellite range scheduling with the priority constraint is one of the most important prob lems in the field of satellite operation. This paper proposes a station coding based genetic algorithm to solve this p...Abstract Satellite range scheduling with the priority constraint is one of the most important prob lems in the field of satellite operation. This paper proposes a station coding based genetic algorithm to solve this problem, which adopts a new chromosome encoding method that arranges tasks according to the ground station ID. The new encoding method contributes to reducing the complex ity in conflict checking and resolving, and helps to improve the ability to find optimal resolutions. Three different selection operators are designed to match the new encoding strategy, namely ran dom selection, greedy selection, and roulette selection. To demonstrate the benefits of the improved genetic algorithm, a basic genetic algorithm is designed in which two cross operators are presented, a singlepoint crossover and a multipoint crossover. For the purpose of algorithm test and analysis, a problemgenerating program is designed, which can simulate problems by modeling features encountered in realworld problems. Based on the problem generator, computational results and analysis are made and illustrated for the scheduling of multiple ground stations.展开更多
As an optimization method that has experienced rapid development over the past 20 years, the genetic algorithm has been successfully applied in many fields, but it requires repeated searches based on the characteristi...As an optimization method that has experienced rapid development over the past 20 years, the genetic algorithm has been successfully applied in many fields, but it requires repeated searches based on the characteristics of high-speed computer calculation and conditions of the known relationship between the objective function and independent variables. There are several hundred generations of evolvement, but the functional relationship is unknown in pollution source searches. Therefore, the genetic algorithm cannot be used directly. Certain improvements need to be made based on the actual situation, so that the genetic algorithm can adapt to the actual conditions of environmental problems, and can be used in environmental monitoring and environmental quality assessment. Therefore, a series of methods are proposed for the improvement of the genetic algorithm： （1） the initial generation of individual groups should be artificially set and move from lightly polluted areas to heavily polluted areas; （2） intervention measures should be introduced in the competition between individuals; （3） guide individuals should be added; and （4） specific improvement programs should be put forward. Finally, the scientific rigor and rationality of the improved genetic algorithm are proven through an example.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
Knowledge acquisition is the “bottleneck” of building an expert system. Based on the optimization model, an improved genetic algorithm applied to knowledge acquisition of a network fault diagnostic expert system is ...Knowledge acquisition is the “bottleneck” of building an expert system. Based on the optimization model, an improved genetic algorithm applied to knowledge acquisition of a network fault diagnostic expert system is proposed. The algorithm applies operators such as selection, crossover and mutation to evolve an initial population of diagnostic rules. Especially, a self adaptive method is put forward to regulate the crossover rate and mutation rate. In the end, a knowledge acquisition problem of a simple network fault diagnostic system is simulated, the results of simulation show that the improved approach can solve the problem of convergence better.展开更多
The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature. 13enetic algorithm （GA） has been proved to be a teasibte method when the gradient ...The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature. 13enetic algorithm （GA） has been proved to be a teasibte method when the gradient is difficult to calculate. Its advantage is that the control profiles at all time stages are optimized simultaneously, but its convergence is very slow in the later period of evolution and it is easily trapped in the local optimum. In this study, a hybrid improved genetic algorithm （HIGA） for solving dynamic optimization problems is proposed to overcome these defects. Simplex method （SM） is used to perform the local search in the neighborhood of the optimal solution. By using SM, the ideal searching direction of global optimal solution could be found as soon as possible and the convergence speed of the algorithm is improved. The hybrid algorithm presents some improvements, such as protecting the best individual, accepting immigrations, as well as employing adaptive crossover and Ganssian mutation operators. The efficiency of the proposed algorithm is demonstrated by solving several dynamic optimization problems. At last, HIGA is applied to the optimal production of secreted protein in a fed batch reactor and the optimal feed-rate found by HIGA is effective and relatively stable.展开更多
a new strategy combining an expert system and improved genetic algorithms is presented for tuning proportional-integral-derivative （PID） parameters for petrochemical processes. This retains the advantages of genetic...a new strategy combining an expert system and improved genetic algorithms is presented for tuning proportional-integral-derivative （PID） parameters for petrochemical processes. This retains the advantages of genetic algorithms, namely rapid convergence and attainment of the global optimum. Utilization of an orthogonal experiment method solves the determination of the genetic factors. Combination with an expert system can make best use of the actual experience of the plant operators. Simulation results of typical process systems examples show a good control performance and robustness.展开更多
As the idea of simulated annealing (SA) is introduced into the fitness function, an improved genetic algorithm (GA) is proposed to perform the optimal design of a pressure vessel which aims to attain the minimum weigh...As the idea of simulated annealing (SA) is introduced into the fitness function, an improved genetic algorithm (GA) is proposed to perform the optimal design of a pressure vessel which aims to attain the minimum weight under burst pressure con- straint. The actual burst pressure is calculated using the arc-length and restart analysis in finite element analysis (FEA). A penalty function in the fitness function is proposed to deal with the constrained problem. The effects of the population size and the number of generations in the GA on the weight and burst pressure of the vessel are explored. The optimization results using the proposed GA are also compared with those using the simple GA and the conventional Monte Carlo method.展开更多
The joint location planning of charging/battery-swap facilities for electric vehicles is a complex problem.Considering the differences between these two modes of power replenishment,we constructed a joint location-pla...The joint location planning of charging/battery-swap facilities for electric vehicles is a complex problem.Considering the differences between these two modes of power replenishment,we constructed a joint location-planning model to minimize construction and operation costs,user costs,and user satisfaction-related penalty costs.We designed an improved genetic algorithm that changes the crossover rate using the fitness value,memorizes,and transfers excellent genes.In addition,the present model addresses the problem of“premature convergence”in conventional genetic algorithms.A simulated example revealed that our proposed model could provide a basis for optimized location planning of charging/battery-swapping facilities at different levels under different charging modes with an improved computing efficiency.The example also proved that meeting more demand for power supply of electric vehicles does not necessarily mean increasing the sites of charging/battery-swap stations.Instead,optimizing the level and location planning of charging/battery-swap stations can maximize the investment profit.The proposed model can provide a reference for the government and enterprises to better plan the location of charging/battery-swap facilities.Hence,it is of both theoretical and practical value.展开更多
The flexible job-shop scheduling problem(FJSP)with combined processing constraints is a common scheduling problem in mixed-flow production lines.However,traditional methods for classic FJSP cannot be directly applied....The flexible job-shop scheduling problem(FJSP)with combined processing constraints is a common scheduling problem in mixed-flow production lines.However,traditional methods for classic FJSP cannot be directly applied.Targeting this problem,the process state model of a mixed-flow production line is analyzed.On this basis,a mathematical model of a mixed-flow job-shop scheduling problem with combined processing constraints is established based on the traditional FJSP.Then,an improved genetic algorithm with multi-segment encoding,crossover,and mutation is proposed for the mixed-flow production line problem.Finally,the proposed algorithm is applied to the production workshop of missile structural components at an aerospace institute to verify its feasibility and effectiveness.展开更多
Since the frequency of network security incidents is nonlinear,traditional prediction methods such as ARMA,Gray systems are difficult to deal with the problem.When the size of sample is small,methods based on artifici...Since the frequency of network security incidents is nonlinear,traditional prediction methods such as ARMA,Gray systems are difficult to deal with the problem.When the size of sample is small,methods based on artificial neural network may not reach a high degree of preciseness.Least Squares Support Vector Machines (LSSVM) is a kind of machine learning methods based on the statistics learning theory,it can be applied to solve small sample and non-linear problems very well.This paper applied LSSVM to predict the occur frequency of network security incidents.To improve the accuracy,it used an improved genetic algorithm to optimize the parameters of LSSVM.Verified by real data sets,the improved genetic algorithm (IGA) converges faster than the simple genetic algorithm (SGA),and has a higher efficiency in the optimization procedure.Specially,the optimized LSSVM model worked very well on the prediction of frequency of network security incidents.展开更多
文摘The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation deployment process was established,and the relationship between the deployment window and the phase difference of the orbit insertion point,as well as the cost of phase adjustment after orbit insertion,was derived.Then,the combination of the constellation deployment position sequence was treated as a parameter,together with the sequence of satellite deployment intervals,as optimization variables,simplifying a highdimensional search problem within a wide range of dates to a finite-dimensional integer programming problem.An improved genetic algorithm with local search on deployment dates was introduced to optimize the launch deployment strategy.With the new description of the optimization variables,the total number of elements in the solution space was reduced by N orders of magnitude.Numerical simulation confirms that the proposed optimization method accelerates the convergence speed from hours to minutes.
文摘This paper introduced an integrated allocation model for distribution centers (DCs). The facility cost, inventory cost, transportation cost and service quality were considered in the model. An improved genetic algorithm (IGA) was proposed to solve the problem. The improvement of IGA is based on the idea of adjusting crossover probability and mutation probability. The IGA is supplied by heuristic rules too. The simulation results show that the IGA is better than the standard GA(SGA) in search efficiency and equality.
文摘The increase in bridge structure span and the complex stress characteristics directly affect the optimization of sensor placement,which in turn influences the data acquisition performance of the monitoring system.The key to the information acquisition of a bridge monitoring system is to obtain data that meets the health monitoring requirements of the bridge with a limited number of measurement points.To address this,a hybrid method based on multiple optimization criteria is proposed for optimal sensor placement(OSP).First,the minimum number of modes required for bridge monitoring is determined using the information entropy criterion(IE).Then,the number of measurement points is determined using a sequence method combined with the modal assurance criterion(MAC).Finally,the sensor placement is optimized using the generalized genetic algorithm(GGA)combined with double-structure encoding,and the optimization results are validated through finite element model analysis.The research results show that the hybrid method based on multiple optimization criteria can effectively determine the number of measurement points for bridge structures and optimize sensor placement,with a significant improvement in computational speed.
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.
文摘A novel real coded improved genetic algorithm (GA) of training feed forward neural network is proposed to realize nonlinear system forecast. The improved GA employs a generation alternation model based the minimal generation gap (MGP) and blend crossover operators (BLX α). Compared with traditional GA implemented in binary number, the processing time of the improved GA is faster because coding and decoding are unnecessary. In addition, it needn t set parameters such as the probability value of crossove...
文摘Based on the slice method of the non-circular slip surface for the calculation of integral stability of slope, an improved genetic algorithm was proposed, which can freely search for the most dangerous slip surface of slope and the corresponding minimum safety factor without supposing the geometric shape of the most dangerous slip surface. This improved genetic algorithm can simulate the genetic evolution process of organisms and avoid the local minimum value compared with the classical methods. The results of engineering cases show that it is a global optimal algorithm and has many advantages, such as higher efficiency and shorter time than the simple genetic algorithm.
基金This paper is supported by the National Youth Natural Science Foundation of China(61802208)the National Natural Science Foundation of China(61572261)+4 种基金the Natural Science Foundation of Anhui(1908085MF207 and 1908085QE217)the Excellent Youth Talent Support Foundation of Anhui(gxyqZD2019097)the Postdoctoral Foundation of Jiangsu(2018K009B)the Higher Education Quality Project of Anhui(2019sjjd81,2018mooc059,2018kfk009,2018sxzx38 and 2018FXJT02)the Fuyang Normal University Doctoral Startup Foundation and Fuyang Government Research Foundation(2017KYQD0008 and XDHXTD201703).
文摘As an indispensable task in crop protection,the detection of crop diseases directly impacts the income of farmers.To address the problems of low crop-disease identification precision and detection abilities,a new method of detection is proposed based on improved genetic algorithm and extreme learning machine.Taking five different typical diseases with common crops as the objects,this method first preprocesses the images of crops and selects the optimal features for fusion.Then,it builds a model of crop disease identification for extreme learning machine,introduces the hill-climbing algorithm to improve the traditional genetic algorithm,optimizes the initial weights and thresholds of the machine,and acquires the approximately optimal solution.And finally,a data set of crop diseases is used for verification,demonstrating that,compared with several other common machine learning methods,this method can effectively improve the crop-disease identification precision and detection abilities and provide a basis for the identification of other crop diseases.
基金funded by the Joint Funds of the National Natural Science Foundation of China (61079001)
文摘This paper deals with dynamic airspace sectorization （DAS） problem by an improved genetic algorithm （iGA）. A graph model is first constructed that represents the airspace static structure. Then the DAS problem is formulated as a graph-partitioning problem to balance the sector workload under the premise of ensuring safety. In the iGA, multiple populations and hybrid coding are applied to determine the optimal sector number and airspace sectorization. The sector constraints are well satisfied by the improved genetic operators and protect zones. This method is validated by being applied to the airspace of North China in terms of three indexes, which are sector balancing index, coordination workload index and sector average flight time index. The improvement is obvious, as the sector balancing index is reduced by 16.5 %, the coordination workload index is reduced by 11.2 %, and the sector average flight time index is increased by 11.4 % during the peak-hour traffic.
文摘The UWB localization problem can be mapped as an optimization problem, which can be solved by genetic algorithm. In the localization process, the traditional fitness function does not include the ranging information between tags, resulting in insufficient ranging information and limited improvement of the localization accuracy. In view of this, an improved genetic localization algorithm is proposed. First, a new fitness function is constructed, which not only includes the ranging information between the tag and the base station, but also the ranging information between the tags to ensure that the ranging information is fully utilized in the localization process. Then, the search method based on Brownian motion is adopted to ensure that the improved algorithm can speed up the convergence speed of the localization result. The simulation results show that, compared with the traditional genetic localization algorithm, the improved genetic localization algorithm can reduce the influence of the ranging error on the localization error and improve the localization performance.
基金supported by a Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Community Framework Programme(No.294931)the National Science Foundation of China (No.51175262)+1 种基金Jiangsu Province Science Foundation for Excellent Youths(No.BK2012032)Jiangsu Province Industry-Academy-Research Grant(No.BY201220116)
文摘Nowadays,energy consumption which closely contacts with environmental impacts of manufacturing processes has been highly commented as a new productivity criterion.However,little attention has paid to the development of process planning methods that take energy consumption into account.An energy-efficient process planning model that incorporates manufacturing time and energy consumption is proposed.For solving the problem,an improved genetic algorithm method is employed to explore the optimal solution.Finally,a case study for process planning is given.The experimental result generates interesting effort,and therefore allows improving the energy efficiency of manufacturing processes in process planning.
文摘Abstract Satellite range scheduling with the priority constraint is one of the most important prob lems in the field of satellite operation. This paper proposes a station coding based genetic algorithm to solve this problem, which adopts a new chromosome encoding method that arranges tasks according to the ground station ID. The new encoding method contributes to reducing the complex ity in conflict checking and resolving, and helps to improve the ability to find optimal resolutions. Three different selection operators are designed to match the new encoding strategy, namely ran dom selection, greedy selection, and roulette selection. To demonstrate the benefits of the improved genetic algorithm, a basic genetic algorithm is designed in which two cross operators are presented, a singlepoint crossover and a multipoint crossover. For the purpose of algorithm test and analysis, a problemgenerating program is designed, which can simulate problems by modeling features encountered in realworld problems. Based on the problem generator, computational results and analysis are made and illustrated for the scheduling of multiple ground stations.
基金supported by the Science and Technology Support Program of Jiangsu Province(Grant No.BE2010738)Jiangsu Colleges and Universities Natural Science Foundation Funded Project(Grant No.08KJB620001)the Qing Lan Project of Jiangsu Province
文摘As an optimization method that has experienced rapid development over the past 20 years, the genetic algorithm has been successfully applied in many fields, but it requires repeated searches based on the characteristics of high-speed computer calculation and conditions of the known relationship between the objective function and independent variables. There are several hundred generations of evolvement, but the functional relationship is unknown in pollution source searches. Therefore, the genetic algorithm cannot be used directly. Certain improvements need to be made based on the actual situation, so that the genetic algorithm can adapt to the actual conditions of environmental problems, and can be used in environmental monitoring and environmental quality assessment. Therefore, a series of methods are proposed for the improvement of the genetic algorithm： （1） the initial generation of individual groups should be artificially set and move from lightly polluted areas to heavily polluted areas; （2） intervention measures should be introduced in the competition between individuals; （3） guide individuals should be added; and （4） specific improvement programs should be put forward. Finally, the scientific rigor and rationality of the improved genetic algorithm are proven through an example.
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Knowledge acquisition is the “bottleneck” of building an expert system. Based on the optimization model, an improved genetic algorithm applied to knowledge acquisition of a network fault diagnostic expert system is proposed. The algorithm applies operators such as selection, crossover and mutation to evolve an initial population of diagnostic rules. Especially, a self adaptive method is put forward to regulate the crossover rate and mutation rate. In the end, a knowledge acquisition problem of a simple network fault diagnostic system is simulated, the results of simulation show that the improved approach can solve the problem of convergence better.
基金Supported by Major State Basic Research Development Program of China （2012CB720500）, National Natural Science Foundation of China （Key Program： Ul162202）, National Science Fund for Outstanding Young Scholars （61222303）, National Natural Science Foundation of China （21276078, 21206037） and the Fundamental Research Funds for the Central Universities.
文摘The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature. 13enetic algorithm （GA） has been proved to be a teasibte method when the gradient is difficult to calculate. Its advantage is that the control profiles at all time stages are optimized simultaneously, but its convergence is very slow in the later period of evolution and it is easily trapped in the local optimum. In this study, a hybrid improved genetic algorithm （HIGA） for solving dynamic optimization problems is proposed to overcome these defects. Simplex method （SM） is used to perform the local search in the neighborhood of the optimal solution. By using SM, the ideal searching direction of global optimal solution could be found as soon as possible and the convergence speed of the algorithm is improved. The hybrid algorithm presents some improvements, such as protecting the best individual, accepting immigrations, as well as employing adaptive crossover and Ganssian mutation operators. The efficiency of the proposed algorithm is demonstrated by solving several dynamic optimization problems. At last, HIGA is applied to the optimal production of secreted protein in a fed batch reactor and the optimal feed-rate found by HIGA is effective and relatively stable.
文摘a new strategy combining an expert system and improved genetic algorithms is presented for tuning proportional-integral-derivative （PID） parameters for petrochemical processes. This retains the advantages of genetic algorithms, namely rapid convergence and attainment of the global optimum. Utilization of an orthogonal experiment method solves the determination of the genetic factors. Combination with an expert system can make best use of the actual experience of the plant operators. Simulation results of typical process systems examples show a good control performance and robustness.
基金Project (Nos. 2006BAK04A02-02 and 2006BAK02B02-08) sup-ported by the National Key Technology R&D Program, China
文摘As the idea of simulated annealing (SA) is introduced into the fitness function, an improved genetic algorithm (GA) is proposed to perform the optimal design of a pressure vessel which aims to attain the minimum weight under burst pressure con- straint. The actual burst pressure is calculated using the arc-length and restart analysis in finite element analysis (FEA). A penalty function in the fitness function is proposed to deal with the constrained problem. The effects of the population size and the number of generations in the GA on the weight and burst pressure of the vessel are explored. The optimization results using the proposed GA are also compared with those using the simple GA and the conventional Monte Carlo method.
文摘The joint location planning of charging/battery-swap facilities for electric vehicles is a complex problem.Considering the differences between these two modes of power replenishment,we constructed a joint location-planning model to minimize construction and operation costs,user costs,and user satisfaction-related penalty costs.We designed an improved genetic algorithm that changes the crossover rate using the fitness value,memorizes,and transfers excellent genes.In addition,the present model addresses the problem of“premature convergence”in conventional genetic algorithms.A simulated example revealed that our proposed model could provide a basis for optimized location planning of charging/battery-swapping facilities at different levels under different charging modes with an improved computing efficiency.The example also proved that meeting more demand for power supply of electric vehicles does not necessarily mean increasing the sites of charging/battery-swap stations.Instead,optimizing the level and location planning of charging/battery-swap stations can maximize the investment profit.The proposed model can provide a reference for the government and enterprises to better plan the location of charging/battery-swap facilities.Hence,it is of both theoretical and practical value.
基金supported by the National Key Research and Development Program of China (No.2020YFB1710500)the National Natural Science Foundation of China(No.51805253)the Fundamental Research Funds for the Central Universities(No. NP2020304)
文摘The flexible job-shop scheduling problem(FJSP)with combined processing constraints is a common scheduling problem in mixed-flow production lines.However,traditional methods for classic FJSP cannot be directly applied.Targeting this problem,the process state model of a mixed-flow production line is analyzed.On this basis,a mathematical model of a mixed-flow job-shop scheduling problem with combined processing constraints is established based on the traditional FJSP.Then,an improved genetic algorithm with multi-segment encoding,crossover,and mutation is proposed for the mixed-flow production line problem.Finally,the proposed algorithm is applied to the production workshop of missile structural components at an aerospace institute to verify its feasibility and effectiveness.
基金supported in part by the National High Technology Research and Development Program of China ("863" Program) (No.2007AA010502)
文摘Since the frequency of network security incidents is nonlinear,traditional prediction methods such as ARMA,Gray systems are difficult to deal with the problem.When the size of sample is small,methods based on artificial neural network may not reach a high degree of preciseness.Least Squares Support Vector Machines (LSSVM) is a kind of machine learning methods based on the statistics learning theory,it can be applied to solve small sample and non-linear problems very well.This paper applied LSSVM to predict the occur frequency of network security incidents.To improve the accuracy,it used an improved genetic algorithm to optimize the parameters of LSSVM.Verified by real data sets,the improved genetic algorithm (IGA) converges faster than the simple genetic algorithm (SGA),and has a higher efficiency in the optimization procedure.Specially,the optimized LSSVM model worked very well on the prediction of frequency of network security incidents.
