Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always ...Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always influenced by external factors,making the original path planning solution ineffective.In this paper,the multi-depot multi-UAV path planning problem with uncertain flight time is modeled as a robust optimization model with a budget uncertainty set.Then,the robust optimization model is transformed into a mixed integer linear programming model by the strong duality theorem,which makes the problem easy to solve.To effectively solve large-scale instances,a simulated annealing algorithm with a robust feasibility check(SA-RFC)is developed.The numerical experiment shows that the SA-RFC can find high-quality solutions within a few seconds.Moreover,the effect of the task location distribution,depot counts,and variations in robustness parameters on the robust optimization solution is analyzed by using Monte Carlo experiments.The results demonstrate that the proposed robust model can effectively reduce the risk of the UAV failing to return to the depot without significantly compromising the profit.展开更多
Agile earth observation satellites(AEOSs)represent a new generation of satellites with three degrees of freedom(pitch,roll,and yaw);they possess a long visible time window(VTW)for ground targets and support imaging at...Agile earth observation satellites(AEOSs)represent a new generation of satellites with three degrees of freedom(pitch,roll,and yaw);they possess a long visible time window(VTW)for ground targets and support imaging at any moment within the VTW.However,different observation times demonstrate different cloud cover distributions,which exhibit different effects on the AEOS observation.Previous studies ignored pitch angles,discretized VTWs,or fixed cloud cover for every VTW,which led to the loss of intermediate observation states,thus these studies are not suitable for AEOS scheduling considering cloud cover distribution.In this study,a relationship formula between the cloud cover and observation time is proposed to calculate the cloud cover for every observation time,and a relationship formula between the observation time and pitch angle is designed to calculate the pitch angle for every observation time in the VTW.A refined model including the pitch angle,roll angle,and cloud cover distribution is established,which can make the scheme closer to the actual application of AEOSs.A hybrid genetic simulated annealing(HGSA)algorithm for AEOS scheduling is proposed,which integrates the advantages of genetic and simulated annealing algorithms and can effectively avoid falling into a local optimal solution.The experiments are conducted to compare the proposed algorithm with the traditional algorithms,the results verify that the proposed model and algorithm are efficient and effective for AEOS scheduling considering cloud cover distribution.展开更多
In rough communication, because each agent has a different language and cannot provide precise communication to each other, the concept translated among multi-agents will loss some information and this results in a le...In rough communication, because each agent has a different language and cannot provide precise communication to each other, the concept translated among multi-agents will loss some information and this results in a less or rougher concept. With different translation sequences, the problem of information loss is varied. To get the translation sequence, in which the jth agent taking part in rough communication gets maximum information, a simulated annealing algorithm is used. Analysis and simulation of this algorithm demonstrate its effectiveness.展开更多
In order to enable quality-aware web services selection in the process of service composition,this paper first describes the non-functional requirements of service consumers and the quality of elementary service or co...In order to enable quality-aware web services selection in the process of service composition,this paper first describes the non-functional requirements of service consumers and the quality of elementary service or composite service as a quality vector,and then models the QoS（quality of service）-aware composition as a multiple criteria optimization problem in extending directed graph.A novel simulated annealing algorithm for QoS-aware web services composition is presented.A normalizing for composite service QoS values is made,and a secondary iterative optimization is used in the algorithm.Experimental results show that the simulated annealing algorithm can satisfy the multiple criteria and global QoS requirements of service consumers.The algorithm produces near optimum solution with much less computation cost.展开更多
The multi-stream heat exchanger network synthesis (HENS) problem can be formulated as a mixed integer nonlinear programming model according to Yee et al. Its nonconvexity nature leads to existence of more than one opt...The multi-stream heat exchanger network synthesis (HENS) problem can be formulated as a mixed integer nonlinear programming model according to Yee et al. Its nonconvexity nature leads to existence of more than one optimum and computational difficulty for traditional algorithms to find the global optimum. Compared with deterministic algorithms, evolutionary computation provides a promising approach to tackle this problem. In this paper, a mathematical model of multi-stream heat exchangers network synthesis problem is setup. Different from the assumption of isothermal mixing of stream splits and thus linearity constraints of Yee et al., non-isothermal mixing is supported. As a consequence, nonlinear constraints are resulted and nonconvexity of the objective function is added. To solve the mathematical model, an algorithm named GA/SA (parallel genetic/simulated annealing algorithm) is detailed for application to the multi-stream heat exchanger network synthesis problem. The performance of the proposed approach is demonstrated with three examples and the obtained solutions indicate the presented approach is effective for multi-stream HENS.展开更多
Disassembly sequence planning （DSP） plays a significant role in maintenance planning of the aircraft. It is used during the design stage for the analysis of maintainability of the aircraft. To solve product disassem...Disassembly sequence planning （DSP） plays a significant role in maintenance planning of the aircraft. It is used during the design stage for the analysis of maintainability of the aircraft. To solve product disassembly sequence planning problems efficiently, a product disassembly hybrid graph model, which describes the connection, non-connection and precedence relationships between the product parts, is established based on the characteristic of disassembly. Farther, the optimization model is provided to optimize disassembly sequence. And the solution methodology based on the genetic/simulated annealing algorithm with binaxy-tree algorithm is given. Finally, an example is analyzed in detail, and the result shows that the model is correct and efficient.展开更多
In this paper combined with the advantages of genetic algorithm and simulated annealing, brings forward a parallel genetic simulated annealing hybrid algorithm （PGSAHA） and applied to solve task scheduling problem i...In this paper combined with the advantages of genetic algorithm and simulated annealing, brings forward a parallel genetic simulated annealing hybrid algorithm （PGSAHA） and applied to solve task scheduling problem in grid computing. It first generates a new group of individuals through genetic operation such as reproduction, crossover, mutation, etc, and than simulated anneals independently all the generated individuals respectively. When the temperature in the process of cooling no longer falls, the result is the optimal solution on the whole. From the analysis and experiment result, it is concluded that this algorithm is superior to genetic algorithm and simulated annealing.展开更多
Evolutionary computation techniques have mostly been used to solve various optimization problems, and it is well known that graph isomorphism problem （GIP） is a nondeterministic polynomial problem. A simulated annea...Evolutionary computation techniques have mostly been used to solve various optimization problems, and it is well known that graph isomorphism problem （GIP） is a nondeterministic polynomial problem. A simulated annealing （SA） algorithm for detecting graph isomorphism is proposed, and the proposed SA algorithm is well suited to deal with random graphs with large size. To verify the validity of the proposed SA algorithm, simulations are performed on three pairs of small graphs and four pairs of large random graphs with edge densities 0.5, 0.1, and 0.01, respectively. The simulation results show that the proposed SA algorithm can detect graph isomorphism with a high probability.展开更多
In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tig...In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method.展开更多
Taking the ratio of heat transfer area to net power and heat recovery efficiency into account, a multi-objective mathematical model was developed for organic Rankine cycle （ORC）. Working fluids considered were R123,...Taking the ratio of heat transfer area to net power and heat recovery efficiency into account, a multi-objective mathematical model was developed for organic Rankine cycle （ORC）. Working fluids considered were R123, R134a, R141b, R227ea and R245fa. Under the given conditions, the parameters including evaporating and condensing pressures, working fluid and cooling water velocities were optimized by simulated annealing algorithm. The results show that the optimal evaporating pressure increases with the heat source temperature increasing. Compared with other working fluids, R123 is the best choice for the temperature range of 100--180℃ and R141 b shows better performance when the temperature is higher than 180 ℃. Economic characteristic of system decreases rapidly with the decrease of heat source temperature. ORC system is uneconomical for the heat source temperature lower than 100℃.展开更多
A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decom...A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome.展开更多
Under the condition of the designated collection ratio and the interfused ratio of mullock, to ensure the least energy consumption, the parameters of collecting head （the feed speed, the axes height of collecting hea...Under the condition of the designated collection ratio and the interfused ratio of mullock, to ensure the least energy consumption, the parameters of collecting head （the feed speed, the axes height of collecting head, and the rotate speed） are chosen as the optimized parameters. According to the force on the cutting pick, the collecting size of the cobalt crust and bedrock and the optimized energy consumption of the collecting head, the optimized design model of collecting head is built. Taking two hundred groups seabed microtopography for grand in the range of depth displacement from 4.5 to 5.5 era, then making use of the improved simulated annealing genetic algorithm （SAGA）, the corresponding optimized result can be obtained. At the same time, in order to speed up the controlling of collecting head, the optimization results are analyzed using the regression analysis method, and the conclusion of the second parameter of the seabed microtopography is drawn.展开更多
This paper establishes a mathematical model of multi-objective optimization with behavior constraints in solid space based on the problem of optimal design of hydraulic manifold blocks （HMB）. Due to the limitation o...This paper establishes a mathematical model of multi-objective optimization with behavior constraints in solid space based on the problem of optimal design of hydraulic manifold blocks （HMB）. Due to the limitation of its local search ability of genetic algorithm （GA） in solving a massive combinatorial optimization problem, simulated annealing （SA） is combined, the multi-parameter concatenated coding is adopted, and the memory function is added. Thus a hybrid genetic-simulated annealing with memory function is formed. Examples show that the modified algorithm can improve the local search ability in the solution space, and the solution quality.展开更多
Reducing the error of sensitive parameters by studying the parameters sensitivity can reduce the uncertainty of the model,while simulating double-gyre variation in Regional Ocean Modeling System(ROMS).Conditional Nonl...Reducing the error of sensitive parameters by studying the parameters sensitivity can reduce the uncertainty of the model,while simulating double-gyre variation in Regional Ocean Modeling System(ROMS).Conditional Nonlinear Optimal Perturbation related to Parameter(CNOP-P)is an effective method of studying the parameters sensitivity,which represents a type of parameter error with maximum nonlinear development at the prediction time.Intelligent algorithms have been widely applied to solving Conditional Nonlinear Optimal Perturbation(CNOP).In the paper,we proposed an improved simulated annealing(SA)algorithm to solve CNOP-P to get the optimal parameters error,studied the sensitivity of the single parameter and the combination of multiple parameters and verified the effect of reducing the error of sensitive parameters on reducing the uncertainty of model simulation.Specifically,we firstly found the non-period oscillation of kinetic energy time series of double gyre variation,then extracted two transition periods,which are respectively from high energy to low energy and from low energy to high energy.For every transition period,three parameters,respectively wind amplitude(WD),viscosity coefficient(VC)and linear bottom drag coefficient(RDRG),were studied by CNOP-P solved with SA algorithm.Finally,for sensitive parameters,their effect on model simulation is verified.Experiments results showed that the sensitivity order is WD>VC>>RDRG,the effect of the combination of multiple sensitive parameters is greater than that of single parameter superposition and the reduction of error of sensitive parameters can effectively reduce model prediction error which confirmed the importance of sensitive parameters analysis.展开更多
Alloy nanoparticles exhibit higher catalytic activity than monometallic nanoparticles, and their stable structures are of importance to their applications. We employ the simulated annealing algorithm to systematically...Alloy nanoparticles exhibit higher catalytic activity than monometallic nanoparticles, and their stable structures are of importance to their applications. We employ the simulated annealing algorithm to systematically explore the stable structure and segregation behavior of tetrahexahedral Pt–Pd–Cu–Au quaternary alloy nanoparticles. Three alloy nanoparticles consisting of 443 atoms, 1417 atoms, and 3285 atoms are considered and compared. The preferred positions of atoms in the nanoparticles are analyzed. The simulation results reveal that Cu and Au atoms tend to occupy the surface, Pt atoms preferentially occupy the middle layers, and Pd atoms tend to segregate to the inner layers. Furthermore, Au atoms present stronger surface segregation than Cu ones. This study provides a fundamental understanding on the structural features and segregation phenomena of multi-metallic nanoparticles.展开更多
Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it...Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it avoids converging to the local optimum. However, it takes too much CPU time in the late process of GA. On the other hand, in the late process Simulated Annealing (SA) converges faster than GA but it is easily trapped to local optimum. In this letter, a useful method that unifies GA and SA is introduced, which utilizes the advantage of the global search ability of GA and fast convergence of SA. The experimental results show that the proposed algorithm outperforms GA in terms of CPU time without degradation of performance. It also achieves highly comparable placement cost compared to the state-of-the-art results obtained by Versatile Place and Route (VPR) Tool.展开更多
To overcome the default of single search tendency, the ants in the colony are divided into several sub-groups. The ants in different subgroups have different trail information and expectation coefficients. The simulat...To overcome the default of single search tendency, the ants in the colony are divided into several sub-groups. The ants in different subgroups have different trail information and expectation coefficients. The simulated annealing method is introduced to the algorithm. Through setting the temperature changing with the iterations, after each turn of tours, the solution set obtained by the ants is taken as the candidate set. The update set is obtained by adding the solutions in the candidate set to the previous update set with the probability determined by the temperature. The solutions in the candidate set are used to update the trail information. In each turn of updating, the current best solution is also used to enhance the trail information on the current best route. The trail information is reset when the algorithm is in stagnation state. The computer experiments demonstrate that the proposed algorithm has higher stability and convergence speed.展开更多
Simulated annealing algorithm is a mathematic model,which imitates the physical process of annealing. And optical thin film is widely used in many industry.Its design is difficult and can be regarded as an optimizatio...Simulated annealing algorithm is a mathematic model,which imitates the physical process of annealing. And optical thin film is widely used in many industry.Its design is difficult and can be regarded as an optimization problem.In this paper,we use the simulated annealing algorithm to design an edge filter,which is composed of 20 dielectric thin film layers with TiO2 and SiO2.The simulated annealing algorithm is a very robust algorithm for optical thin film design.展开更多
Although the phase-shift seismic processing method has characteristics of high accuracy, good stability, high efficiency, and high-dip imaging, it is not able to adapt to strong lateral velocity variation. To overcome...Although the phase-shift seismic processing method has characteristics of high accuracy, good stability, high efficiency, and high-dip imaging, it is not able to adapt to strong lateral velocity variation. To overcome this defect, a finite-difference method in the frequency-space domain is introduced in the migration process, because it can adapt to strong lateral velocity variation and the coefficient is optimized by a hybrid genetic and simulated annealing algorithm. The two measures improve the precision of the approximation dispersion equation. Thus, the imaging effect is improved for areas of high-dip structure and strong lateral velocity variation. The migration imaging of a 2-D SEG/EAGE salt dome model proves that a better imaging effect in these areas is achieved by optimized phase-shift migration operator plus a finite-difference method based on a hybrid genetic and simulated annealing algorithm. The method proposed in this paper is better than conventional methods in imaging of areas of high-dip angle and strong lateral velocity variation.展开更多
The similarity measure is crucial to the performance of spectral clustering. The Gaussian kernel function based on the Euclidean distance is usual y adopted as the similarity measure. However, the Euclidean distance m...The similarity measure is crucial to the performance of spectral clustering. The Gaussian kernel function based on the Euclidean distance is usual y adopted as the similarity measure. However, the Euclidean distance measure cannot ful y reveal the complex distribution data, and the result of spectral clustering is very sensitive to the scaling parameter. To solve these problems, a new manifold distance measure and a novel simulated anneal-ing spectral clustering （SASC） algorithm based on the manifold distance measure are proposed. The simulated annealing based on genetic algorithm （SAGA）, characterized by its rapid convergence to the global optimum, is used to cluster the sample points in the spectral mapping space. The proposed algorithm can not only reflect local and global consistency better, but also reduce the sensitivity of spectral clustering to the kernel parameter, which improves the algorithm’s clustering performance. To efficiently apply the algorithm to image segmentation, the Nystrom method is used to reduce the computation complexity. Experimental results show that compared with traditional clustering algorithms and those popular spectral clustering algorithms, the proposed algorithm can achieve better clustering performances on several synthetic datasets, texture images and real images.展开更多
基金supported by the National Natural Science Foundation of China(72571094,72271076,71871079)。
文摘Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always influenced by external factors,making the original path planning solution ineffective.In this paper,the multi-depot multi-UAV path planning problem with uncertain flight time is modeled as a robust optimization model with a budget uncertainty set.Then,the robust optimization model is transformed into a mixed integer linear programming model by the strong duality theorem,which makes the problem easy to solve.To effectively solve large-scale instances,a simulated annealing algorithm with a robust feasibility check(SA-RFC)is developed.The numerical experiment shows that the SA-RFC can find high-quality solutions within a few seconds.Moreover,the effect of the task location distribution,depot counts,and variations in robustness parameters on the robust optimization solution is analyzed by using Monte Carlo experiments.The results demonstrate that the proposed robust model can effectively reduce the risk of the UAV failing to return to the depot without significantly compromising the profit.
基金supported by the National Natural Science Foundation of China(72071064,72271074,72001004)the Anhui Provincial Natural Science Foundation(2408085QG221).
文摘Agile earth observation satellites(AEOSs)represent a new generation of satellites with three degrees of freedom(pitch,roll,and yaw);they possess a long visible time window(VTW)for ground targets and support imaging at any moment within the VTW.However,different observation times demonstrate different cloud cover distributions,which exhibit different effects on the AEOS observation.Previous studies ignored pitch angles,discretized VTWs,or fixed cloud cover for every VTW,which led to the loss of intermediate observation states,thus these studies are not suitable for AEOS scheduling considering cloud cover distribution.In this study,a relationship formula between the cloud cover and observation time is proposed to calculate the cloud cover for every observation time,and a relationship formula between the observation time and pitch angle is designed to calculate the pitch angle for every observation time in the VTW.A refined model including the pitch angle,roll angle,and cloud cover distribution is established,which can make the scheme closer to the actual application of AEOSs.A hybrid genetic simulated annealing(HGSA)algorithm for AEOS scheduling is proposed,which integrates the advantages of genetic and simulated annealing algorithms and can effectively avoid falling into a local optimal solution.The experiments are conducted to compare the proposed algorithm with the traditional algorithms,the results verify that the proposed model and algorithm are efficient and effective for AEOS scheduling considering cloud cover distribution.
基金the Natural Science Foundation of Shandong Province (Y2006A12)the Scientific ResearchDevelopment Project of Shandong Provincial Education Department(J06P01)the Doctoral Foundation of University of Jinan(B0633).
文摘In rough communication, because each agent has a different language and cannot provide precise communication to each other, the concept translated among multi-agents will loss some information and this results in a less or rougher concept. With different translation sequences, the problem of information loss is varied. To get the translation sequence, in which the jth agent taking part in rough communication gets maximum information, a simulated annealing algorithm is used. Analysis and simulation of this algorithm demonstrate its effectiveness.
基金The National Natural Science Foundation of China(No.60773217)Free Exploration Project(985 Project of Renmin University of China)(No.21361231)
文摘In order to enable quality-aware web services selection in the process of service composition,this paper first describes the non-functional requirements of service consumers and the quality of elementary service or composite service as a quality vector,and then models the QoS（quality of service）-aware composition as a multiple criteria optimization problem in extending directed graph.A novel simulated annealing algorithm for QoS-aware web services composition is presented.A normalizing for composite service QoS values is made,and a secondary iterative optimization is used in the algorithm.Experimental results show that the simulated annealing algorithm can satisfy the multiple criteria and global QoS requirements of service consumers.The algorithm produces near optimum solution with much less computation cost.
基金Supported by the Deutsche Forschungsgemeinschaft (DFG No. RO294/9).
文摘The multi-stream heat exchanger network synthesis (HENS) problem can be formulated as a mixed integer nonlinear programming model according to Yee et al. Its nonconvexity nature leads to existence of more than one optimum and computational difficulty for traditional algorithms to find the global optimum. Compared with deterministic algorithms, evolutionary computation provides a promising approach to tackle this problem. In this paper, a mathematical model of multi-stream heat exchangers network synthesis problem is setup. Different from the assumption of isothermal mixing of stream splits and thus linearity constraints of Yee et al., non-isothermal mixing is supported. As a consequence, nonlinear constraints are resulted and nonconvexity of the objective function is added. To solve the mathematical model, an algorithm named GA/SA (parallel genetic/simulated annealing algorithm) is detailed for application to the multi-stream heat exchanger network synthesis problem. The performance of the proposed approach is demonstrated with three examples and the obtained solutions indicate the presented approach is effective for multi-stream HENS.
基金supported by the National High Technology Research and Development Program of China(2006AA04Z427).
文摘Disassembly sequence planning （DSP） plays a significant role in maintenance planning of the aircraft. It is used during the design stage for the analysis of maintainability of the aircraft. To solve product disassembly sequence planning problems efficiently, a product disassembly hybrid graph model, which describes the connection, non-connection and precedence relationships between the product parts, is established based on the characteristic of disassembly. Farther, the optimization model is provided to optimize disassembly sequence. And the solution methodology based on the genetic/simulated annealing algorithm with binaxy-tree algorithm is given. Finally, an example is analyzed in detail, and the result shows that the model is correct and efficient.
基金Supported by the National Basic ResearchProgramof China (973 Program2003CB314804)
文摘In this paper combined with the advantages of genetic algorithm and simulated annealing, brings forward a parallel genetic simulated annealing hybrid algorithm （PGSAHA） and applied to solve task scheduling problem in grid computing. It first generates a new group of individuals through genetic operation such as reproduction, crossover, mutation, etc, and than simulated anneals independently all the generated individuals respectively. When the temperature in the process of cooling no longer falls, the result is the optimal solution on the whole. From the analysis and experiment result, it is concluded that this algorithm is superior to genetic algorithm and simulated annealing.
基金the National Natural Science Foundation of China (60373089, 60674106, and 60533010)the National High Technology Research and Development "863" Program (2006AA01Z104)
文摘Evolutionary computation techniques have mostly been used to solve various optimization problems, and it is well known that graph isomorphism problem （GIP） is a nondeterministic polynomial problem. A simulated annealing （SA） algorithm for detecting graph isomorphism is proposed, and the proposed SA algorithm is well suited to deal with random graphs with large size. To verify the validity of the proposed SA algorithm, simulations are performed on three pairs of small graphs and four pairs of large random graphs with edge densities 0.5, 0.1, and 0.01, respectively. The simulation results show that the proposed SA algorithm can detect graph isomorphism with a high probability.
基金funded by the National Natural Science Foundation of China(42174131)the Strategic Cooperation Technology Projects of CNPC and CUPB(ZLZX2020-03).
文摘In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method.
基金Project(2009GK2009) supported by Science and Technology Department Funds of Hunan Province,ChinaProject(08C26224302178) supported by Innovation Fund for Technology Based Firms of China
文摘Taking the ratio of heat transfer area to net power and heat recovery efficiency into account, a multi-objective mathematical model was developed for organic Rankine cycle （ORC）. Working fluids considered were R123, R134a, R141b, R227ea and R245fa. Under the given conditions, the parameters including evaporating and condensing pressures, working fluid and cooling water velocities were optimized by simulated annealing algorithm. The results show that the optimal evaporating pressure increases with the heat source temperature increasing. Compared with other working fluids, R123 is the best choice for the temperature range of 100--180℃ and R141 b shows better performance when the temperature is higher than 180 ℃. Economic characteristic of system decreases rapidly with the decrease of heat source temperature. ORC system is uneconomical for the heat source temperature lower than 100℃.
文摘A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome.
基金Project(50875265) supported by the National Natural Science Foundation of ChinaProject(20080440992) supported by the Postdoctoral Science Foundation of ChinaProject(2009SK3159) supported by the Technology Support Plan of Hunan Province,China
文摘Under the condition of the designated collection ratio and the interfused ratio of mullock, to ensure the least energy consumption, the parameters of collecting head （the feed speed, the axes height of collecting head, and the rotate speed） are chosen as the optimized parameters. According to the force on the cutting pick, the collecting size of the cobalt crust and bedrock and the optimized energy consumption of the collecting head, the optimized design model of collecting head is built. Taking two hundred groups seabed microtopography for grand in the range of depth displacement from 4.5 to 5.5 era, then making use of the improved simulated annealing genetic algorithm （SAGA）, the corresponding optimized result can be obtained. At the same time, in order to speed up the controlling of collecting head, the optimization results are analyzed using the regression analysis method, and the conclusion of the second parameter of the seabed microtopography is drawn.
基金Project supported by the National Natural Science Foundation of China （Grant No.50375023）
文摘This paper establishes a mathematical model of multi-objective optimization with behavior constraints in solid space based on the problem of optimal design of hydraulic manifold blocks （HMB）. Due to the limitation of its local search ability of genetic algorithm （GA） in solving a massive combinatorial optimization problem, simulated annealing （SA） is combined, the multi-parameter concatenated coding is adopted, and the memory function is added. Thus a hybrid genetic-simulated annealing with memory function is formed. Examples show that the modified algorithm can improve the local search ability in the solution space, and the solution quality.
基金Supported by the National Natural Science Foundation of China(No.41405097)the Fundamental Research Funds for the Central Universities of China in 2017
文摘Reducing the error of sensitive parameters by studying the parameters sensitivity can reduce the uncertainty of the model,while simulating double-gyre variation in Regional Ocean Modeling System(ROMS).Conditional Nonlinear Optimal Perturbation related to Parameter(CNOP-P)is an effective method of studying the parameters sensitivity,which represents a type of parameter error with maximum nonlinear development at the prediction time.Intelligent algorithms have been widely applied to solving Conditional Nonlinear Optimal Perturbation(CNOP).In the paper,we proposed an improved simulated annealing(SA)algorithm to solve CNOP-P to get the optimal parameters error,studied the sensitivity of the single parameter and the combination of multiple parameters and verified the effect of reducing the error of sensitive parameters on reducing the uncertainty of model simulation.Specifically,we firstly found the non-period oscillation of kinetic energy time series of double gyre variation,then extracted two transition periods,which are respectively from high energy to low energy and from low energy to high energy.For every transition period,three parameters,respectively wind amplitude(WD),viscosity coefficient(VC)and linear bottom drag coefficient(RDRG),were studied by CNOP-P solved with SA algorithm.Finally,for sensitive parameters,their effect on model simulation is verified.Experiments results showed that the sensitivity order is WD>VC>>RDRG,the effect of the combination of multiple sensitive parameters is greater than that of single parameter superposition and the reduction of error of sensitive parameters can effectively reduce model prediction error which confirmed the importance of sensitive parameters analysis.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.51271156,11474234,and 61403318)the Natural Science Foundation of Fujian Province of China(Grant Nos.2013J01255 and 2013J06002)
文摘Alloy nanoparticles exhibit higher catalytic activity than monometallic nanoparticles, and their stable structures are of importance to their applications. We employ the simulated annealing algorithm to systematically explore the stable structure and segregation behavior of tetrahexahedral Pt–Pd–Cu–Au quaternary alloy nanoparticles. Three alloy nanoparticles consisting of 443 atoms, 1417 atoms, and 3285 atoms are considered and compared. The preferred positions of atoms in the nanoparticles are analyzed. The simulation results reveal that Cu and Au atoms tend to occupy the surface, Pt atoms preferentially occupy the middle layers, and Pd atoms tend to segregate to the inner layers. Furthermore, Au atoms present stronger surface segregation than Cu ones. This study provides a fundamental understanding on the structural features and segregation phenomena of multi-metallic nanoparticles.
基金Supported by School of Engineering, Napier University, United Kingdom, and partially supported by the National Natural Science Foundation of China (No.60273093).
文摘Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it avoids converging to the local optimum. However, it takes too much CPU time in the late process of GA. On the other hand, in the late process Simulated Annealing (SA) converges faster than GA but it is easily trapped to local optimum. In this letter, a useful method that unifies GA and SA is introduced, which utilizes the advantage of the global search ability of GA and fast convergence of SA. The experimental results show that the proposed algorithm outperforms GA in terms of CPU time without degradation of performance. It also achieves highly comparable placement cost compared to the state-of-the-art results obtained by Versatile Place and Route (VPR) Tool.
基金Project supported by the National Natural Science Foundation of China (Grant No.50608069)
文摘To overcome the default of single search tendency, the ants in the colony are divided into several sub-groups. The ants in different subgroups have different trail information and expectation coefficients. The simulated annealing method is introduced to the algorithm. Through setting the temperature changing with the iterations, after each turn of tours, the solution set obtained by the ants is taken as the candidate set. The update set is obtained by adding the solutions in the candidate set to the previous update set with the probability determined by the temperature. The solutions in the candidate set are used to update the trail information. In each turn of updating, the current best solution is also used to enhance the trail information on the current best route. The trail information is reset when the algorithm is in stagnation state. The computer experiments demonstrate that the proposed algorithm has higher stability and convergence speed.
文摘Simulated annealing algorithm is a mathematic model,which imitates the physical process of annealing. And optical thin film is widely used in many industry.Its design is difficult and can be regarded as an optimization problem.In this paper,we use the simulated annealing algorithm to design an edge filter,which is composed of 20 dielectric thin film layers with TiO2 and SiO2.The simulated annealing algorithm is a very robust algorithm for optical thin film design.
基金the Open Fund(PLC201104)of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Chengdu University of Technology)the National Natural Science Foundation of China(No.61072073)the Key Project of Education Commission of Sichuan Province(No.10ZA072)
文摘Although the phase-shift seismic processing method has characteristics of high accuracy, good stability, high efficiency, and high-dip imaging, it is not able to adapt to strong lateral velocity variation. To overcome this defect, a finite-difference method in the frequency-space domain is introduced in the migration process, because it can adapt to strong lateral velocity variation and the coefficient is optimized by a hybrid genetic and simulated annealing algorithm. The two measures improve the precision of the approximation dispersion equation. Thus, the imaging effect is improved for areas of high-dip structure and strong lateral velocity variation. The migration imaging of a 2-D SEG/EAGE salt dome model proves that a better imaging effect in these areas is achieved by optimized phase-shift migration operator plus a finite-difference method based on a hybrid genetic and simulated annealing algorithm. The method proposed in this paper is better than conventional methods in imaging of areas of high-dip angle and strong lateral velocity variation.
基金supported by the National Natural Science Foundationof China(61272119)
文摘The similarity measure is crucial to the performance of spectral clustering. The Gaussian kernel function based on the Euclidean distance is usual y adopted as the similarity measure. However, the Euclidean distance measure cannot ful y reveal the complex distribution data, and the result of spectral clustering is very sensitive to the scaling parameter. To solve these problems, a new manifold distance measure and a novel simulated anneal-ing spectral clustering （SASC） algorithm based on the manifold distance measure are proposed. The simulated annealing based on genetic algorithm （SAGA）, characterized by its rapid convergence to the global optimum, is used to cluster the sample points in the spectral mapping space. The proposed algorithm can not only reflect local and global consistency better, but also reduce the sensitivity of spectral clustering to the kernel parameter, which improves the algorithm’s clustering performance. To efficiently apply the algorithm to image segmentation, the Nystrom method is used to reduce the computation complexity. Experimental results show that compared with traditional clustering algorithms and those popular spectral clustering algorithms, the proposed algorithm can achieve better clustering performances on several synthetic datasets, texture images and real images.
