Dynamic reliability is a very important issue in reliability research. The dynamic reliability analysis for the project is still in search of domestic and international research in the exploration stage. By now, dynam...Dynamic reliability is a very important issue in reliability research. The dynamic reliability analysis for the project is still in search of domestic and international research in the exploration stage. By now, dynamic reliability research mainly concentrates on the reliability assessment; the methods mainly include dynamic fault tree, extension of event sequence diagram and Monte Carlo simulation, and et al. The paper aims to research the dynamic reliability optimization. On the basis of analysis of the four quality influence factors in the construction engineering, a method based on gray correlation degree is employed to calculate the weights of factors affecting construction process quality. Then the weights are added into the reliability improvement feasible index （RIFI）. Furthermore, a novel nonlinear programming mathematic optimization model is established. In the Insight software environment, the Adaptive Simulated Annealing （ASA） algorithm is used to get a more accurate construction subsystem optimal reliability under different RIFI conditions. In addition, the relationship between construction quality and construction system reliability is analyzed, the proposed methods and detailed processing can offer a useful reference for improving the construction system quality level.展开更多
Cloud workloads are highly dynamic and complex,making task scheduling in cloud computing a challenging problem.While several scheduling algorithms have been proposed in recent years,they are mainly designed to handle ...Cloud workloads are highly dynamic and complex,making task scheduling in cloud computing a challenging problem.While several scheduling algorithms have been proposed in recent years,they are mainly designed to handle batch tasks and not well-suited for real-time workloads.To address this issue,researchers have started exploring the use of Deep Reinforcement Learning(DRL).However,the existing models are limited in handling independent tasks and cannot process workflows,which are prevalent in cloud computing and consist of related subtasks.In this paper,we propose SA-DQN,a scheduling approach specifically designed for real-time cloud workflows.Our approach seamlessly integrates the Simulated Annealing(SA)algorithm and Deep Q-Network(DQN)algorithm.The SA algorithm is employed to determine an optimal execution order of subtasks in a cloud server,serving as a crucial feature of the task for the neural network to learn.We provide a detailed design of our approach and show that SA-DQN outperforms existing algorithms in terms of handling real-time cloud workflows through experimental results.展开更多
To provide a much more resilient transport scheme for tractor and trailer transportation systems,this paper explores the generation method of tractor and trailer transport schemes considering the influence of disrupte...To provide a much more resilient transport scheme for tractor and trailer transportation systems,this paper explores the generation method of tractor and trailer transport schemes considering the influence of disrupted events.Three states of tractors including towing loaded trailers,towing empty trailers,and idle driving are taken into account.Based on the disruption management theory,a scheduling model is constructed to minimize the total deviation cost including transportation time,transportation path,and number of used vehicles under the three states of tractors.A heuristics based on the contract net and simulated annealing algorithm is designed to solve the proposed model.Through comparative analysis of examples with different numbers of newly added transportation tasks and different types of road networks,the performance of the contract net algorithm in terms of deviations in idle driving paths,empty trailer paths,loaded trailer paths,time,number of used vehicles,and total deviation cost are analyzed.The results demonstrate the effectiveness of the model and algorithm,highlighting the superiority of the disruption management model and the contract net annealing algorithm.The study provides a reference for handling unexpected events in the tractor and trailer transportation industry.展开更多
To enhance the rationality of the layout of electric vehicle charging stations,meet the actual needs of users,and optimise the service range and coverage efficiency of charging stations,this paper proposes an optimisa...To enhance the rationality of the layout of electric vehicle charging stations,meet the actual needs of users,and optimise the service range and coverage efficiency of charging stations,this paper proposes an optimisation strategy for the layout of electric vehicle charging stations that integrates Mini Batch K-Means and simulated annealing algorithms.By constructing a circle-like service area model with the charging station as the centre and a certain distance as the radius,the maximum coverage of electric vehicle charging stations in the region and the influence of different regional environments on charging demand are considered.Based on the real data of electric vehicle charging stations in Nanjing,Jiangsu Province,this paper uses the model proposed in this paper to optimise the layout of charging stations in the study area.The results show that the optimisation strategy incorporating Mini Batch K-Means and simulated annealing algorithms outperforms the existing charging station layouts in terms of coverage and the number of stations served,and compared to the original charging station layouts,the optimised charging station layouts have flatter Lorentzian curves and are closer to the average distribution.The proposed optimisation strategy not only improves the service efficiency and user satisfaction of EV(Electric Vehicle)charging stations but also provides a reference for the layout optimisation of EV charging stations in other cities,which has important practical value and promotion potential.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
This paper presents an approximate algorithm based on simulated annealing to achieve the maximum probability of the minimal cut sets for a fault tree. Near optimal minimal cut sets and important sequence of the basic ...This paper presents an approximate algorithm based on simulated annealing to achieve the maximum probability of the minimal cut sets for a fault tree. Near optimal minimal cut sets and important sequence of the basic events are also solved by the method. Computer simulations show that the algorithm performs very well.展开更多
Forest inventory is increasingly producing infor-mation on the locations and sizes of individual trees.This information can be acquired by airborne or terrestrial laser scanning or analyzing photogrammetric data.Howev...Forest inventory is increasingly producing infor-mation on the locations and sizes of individual trees.This information can be acquired by airborne or terrestrial laser scanning or analyzing photogrammetric data.However,all trees are seldom detected,especially in young,dense,or multi-layered stands.On the other hand,the complete size distributions of trees can be predicted with various methods,for instance,kNN data imputation in an area-based LiDAR inventory,predicting the parameters of a distribution func-tion from remote sensing data,field sampling,or using his-togram matching and calibration methods.The predicted distribution can be used to estimate the number and sizes of the non-detected trees.The study’s objective was to develop a method for forest planning that efficiently uses the avail-able tree-level data in management optimization.The study developed a two-stage hierarchical method for tree-level management optimization for cases where only part of the trees is detected or measured individually.Cutting years and harvest rate curves for the non-detected trees are optimized at the higher level,and the cutting events of the detected trees are optimized at the lower level.The study used differ-ential evolution at the higher level and simulated annealing at the lower level.The method was tested and demonstrated in even-aged Larix olgensis plantations in the Heilongjiang province of China.The optimizations showed that optimiz-ing the harvest decisions at the tree level improves the profit-ability of management compared to optimizations in which only the dependence of thinning intensity on tree diameter is optimized.The approach demonstrated in this study pro-vides feasible options for tree-level forest planning based on LiDAR inventories.The method is immediately applicable to forestry practice,especially in plantations.展开更多
Spatial confinement of block copolymers can induce frustrations,which can further be utilized to regulate self-assembled structures,thus providing an efficient route for fabricating novel structures.We studied the sel...Spatial confinement of block copolymers can induce frustrations,which can further be utilized to regulate self-assembled structures,thus providing an efficient route for fabricating novel structures.We studied the self-assembly of AB di-block copolymers(di-BCPs)confined in Janus spherical nanocavities using simulations,and explained the structure formation mechanisms.In the case of a strongly selective cavity wall,all the lamella-forming,gyroid-forming,and cylinder-forming di-BCPs can form interfacial frustration-induced Janus concentric perforated lamellar nanoparticles,whose outermost is a Janus spherical shell and the internal is a sphere with concentric perforated lamellar structure.In particular,Janus concentric perforated lamellar nanoparticles with holes distributed only near the equatorial plane were obtained in both lamella-forming and gyroid-forming di-BCPs,directly reflecting the effect of interfacial frustration.The minority-block domain of the cylider-forming di-BCPs may form hemispherical perforated lamellar structures with holes distributed in parallel layers with a specific orientation.For symmetric di-BCPs,both the A and B domains in each nanoparticle are continuous,interchangeable,and have rotational symmetry.While for gyroid-forming and cylinder-forming di-BCPs,only the majority-block domains are continuous in each nanoparticle,and holes in the minority-block domains usually have rotational symmetry.In the case of a weakly selective cavity wall,the inhomogeneity of the cavity wall results in structures having a specific orientation(such as flower-like and branched structures in gyroid-forming and cylinder-forming di-BCPs)and a perforated wetting layer with uniformly distributed holes.The novel nanoparticles obtained may have potential applications in nanotechnology as functional nanostructures or nanoparticles.展开更多
Recent studies employing deep learning to solve the traveling salesman problem(TSP)have mainly focused on learning construction heuristics.Such methods can improve TSP solutions,but still depend on additional programs...Recent studies employing deep learning to solve the traveling salesman problem(TSP)have mainly focused on learning construction heuristics.Such methods can improve TSP solutions,but still depend on additional programs.However,methods that focus on learning improvement heuristics to iteratively refine solutions remain insufficient.Traditional improvement heuristics are guided by a manually designed search strategy and may only achieve limited improvements.This paper proposes a novel framework for learning improvement heuristics,which automatically discovers better improvement policies for heuristics to iteratively solve the TSP.Our framework first designs a new architecture based on a transformer model to make the policy network parameterized,which introduces an action-dropout layer to prevent action selection from overfitting.It then proposes a deep reinforcement learning approach integrating a simulated annealing mechanism(named RL-SA)to learn the pairwise selected policy,aiming to improve the 2-opt algorithm's performance.The RL-SA leverages the whale optimization algorithm to generate initial solutions for better sampling efficiency and uses the Gaussian perturbation strategy to tackle the sparse reward problem of reinforcement learning.The experiment results show that the proposed approach is significantly superior to the state-of-the-art learning-based methods,and further reduces the gap between learning-based methods and highly optimized solvers in the benchmark datasets.Moreover,our pre-trained model M can be applied to guide the SA algorithm(named M-SA(ours)),which performs better than existing deep models in small-,medium-,and large-scale TSPLIB datasets.Additionally,the M-SA(ours)achieves excellent generalization performance in a real-world dataset on global liner shipping routes,with the optimization percentages in distance reduction ranging from3.52%to 17.99%.展开更多
Path planning for recovery is studied on the engineering background of double unmanned surface vehicles(USVs)towing oil booms for oil spill recovery.Given the influence of obstacles on the sea,the improved artificial ...Path planning for recovery is studied on the engineering background of double unmanned surface vehicles(USVs)towing oil booms for oil spill recovery.Given the influence of obstacles on the sea,the improved artificial potential field(APF)method is used for path planning.For addressing the two problems of unreachable target and local minimum in the APF,three improved algorithms are proposed by combining the motion performance constraints of the double USV system.These algorithms are then combined as the final APF-123 algorithm for oil spill recovery.Multiple sets of simulation tests are designed according to the flaws of the APF and the process of oil spill recovery.Results show that the proposed algorithms can ensure the system’s safety in tracking oil spills in a complex environment,and the speed is increased by more than 40%compared with the APF method.展开更多
The current trends in forestry in Europe include the increased use of continuous cover forestry(CCF)and the increased availability of tree-level forest inventory data.Accordingly,recent literature suggests methodologi...The current trends in forestry in Europe include the increased use of continuous cover forestry(CCF)and the increased availability of tree-level forest inventory data.Accordingly,recent literature suggests methodologies for optimizing the harvest decisions at the tree level.Using tree-level optimization for all trees of the stand is computationally demanding.This study proposed a two-level optimization method for CCF where the harvest prescriptions are optimized at the tree level for only a part of the trees or the first cuttings.The higher-level algorithm optimizes the cutting years and the harvest rates of those diameter classes for which tree-level optimization is not used.The lower-level algorithm allocates the individually optimized trees to different cutting events.The most detailed problem formulations,employing much tree-level optimization,resulted in the highest net present value and longest optimization time.However,restricting tree-level optimization to the largest trees and first cuttings did not significantly alter the time,intensity,or type of first cutting.Computing times could also be shortened by applying accumulated knowledge from previous optimizations,implementing learning aspects in heuristic search,and optimizing the search algorithms for short computing time and good-quality solutions.展开更多
Wettability has complex effects on the physical properties of reservoir rocks.The wettability of rocks should be characterized accurately to explore and develop oil and gas.Researchers have studied the rock wettabilit...Wettability has complex effects on the physical properties of reservoir rocks.The wettability of rocks should be characterized accurately to explore and develop oil and gas.Researchers have studied the rock wettability by dielectric spectra which contained abundant information.To study the rock wettability from dielectric dispersion,four rock samples with different wettabilities were used to design an experimental measurement flow.The relative dielectric permittivity in the frequency range of 100 Hz-10MHz and nuclear magnetic resonance T_(2)spectra of the samples were obtained.Subsequently,the wettabilities of the rocks were verified by the T_(2)spectra.The dielectric dispersions of the samples under different conditions were analyzed.Furthermore,the simulated-annealing(SA)algorithm was used to invert the wettability and related parameters of the rocks by a dielectric dispersion model.The results indicated that the dielectric permittivity of lipophilic rocks is lower than that of hydrophilic rocks,and that the dielectric permittivity of hydrophilic rocks decreases faster as the frequency increases.The dielectric permittivity in the high-frequency band is associated with the water content.The rock wettability parameters obtained via inversion agreed well with the T_(2)spectra,and the saturation index of the rocks.The errors between the rock permittivity calculated by the inverted parameters and the experimentally measured values were minor,indicating that rock wettability could be accurately characterized using dielectric dispersion data.展开更多
Since there were few chaotic neural networks applicable to the global optimization, in this paper, we propose a new neural network model ? chaotic parameters disturbance annealing (CPDA) network, which is superior to ...Since there were few chaotic neural networks applicable to the global optimization, in this paper, we propose a new neural network model ? chaotic parameters disturbance annealing (CPDA) network, which is superior to other existing neural networks, genetic algorithms, and simulated annealing algorithms in global optimization. In the present CPDA network, we add some chaotic parameters in the energy function, which make the Hopfield neural network escape from the attraction of a local minimal solution and with the parameter annealing, our model will converge to the global optimal solutions quickly and steadily. The converge ability and other characters are also analyzed in this paper. The benchmark examples show the present CPDA neural network's merits in nonlinear global optimization.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
This article presents a simulated annealing-based approach to the optimal synthesis of distillation column considering intermediate heat exchangers arrangements. T-he number of intermediate condensers and/or intermedi...This article presents a simulated annealing-based approach to the optimal synthesis of distillation column considering intermediate heat exchangers arrangements. T-he number of intermediate condensers and/or intermediate reboilers, the placement locations, the.operating pressure of column, and the heat duties of intermediate heat exchangers are treated as optimization variables. A novel coding procedure making use of an integer number series is proposed to represent and manipulate the structure of system and a stage-to-stage method is used for column design and cost calculation. With the representation procedure, the synthesis problem is formulated as a mixed integer nonlinear programming （MINLP） problem, which can then be solved with an improved simulated annealing algorithm. Two examples are illustrated to show the effectiveness of the suggested approach.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
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
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
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
At present,simultaneous localization and mapping(SLAM) for an autonomous underwater vehicle(AUV)is a research hotspot.Aiming at the problem of non-linear model and non-Gaussian noise in AUV motion,an improved method o...At present,simultaneous localization and mapping(SLAM) for an autonomous underwater vehicle(AUV)is a research hotspot.Aiming at the problem of non-linear model and non-Gaussian noise in AUV motion,an improved method of variance reduction fast simultaneous localization and mapping(FastSLAM) with simulated annealing is proposed to solve the problems of particle degradation,particle depletion and particle loss in traditional FastSLAM,which lead to the reduction of AUV location estimation accuracy.The adaptive exponential fading factor is generated by the anneal function of simulated annealing algorithm to improve the effective particle number and replace resampling.By increasing the weight of small particles and decreasing the weight of large particles,the variance of particle weight can be reduced,the number of effective particles can be increased,and the accuracy of AUV location and feature location estimation can be improved to some extent by retaining more information carried by particles.The experimental results based on trial data show that the proposed simulated annealing variance reduction FastSLAM method avoids particle degradation,maintains the diversity of particles,weakened the degeneracy and improves the accuracy and stability of AUV navigation and localization system.展开更多
文摘Dynamic reliability is a very important issue in reliability research. The dynamic reliability analysis for the project is still in search of domestic and international research in the exploration stage. By now, dynamic reliability research mainly concentrates on the reliability assessment; the methods mainly include dynamic fault tree, extension of event sequence diagram and Monte Carlo simulation, and et al. The paper aims to research the dynamic reliability optimization. On the basis of analysis of the four quality influence factors in the construction engineering, a method based on gray correlation degree is employed to calculate the weights of factors affecting construction process quality. Then the weights are added into the reliability improvement feasible index （RIFI）. Furthermore, a novel nonlinear programming mathematic optimization model is established. In the Insight software environment, the Adaptive Simulated Annealing （ASA） algorithm is used to get a more accurate construction subsystem optimal reliability under different RIFI conditions. In addition, the relationship between construction quality and construction system reliability is analyzed, the proposed methods and detailed processing can offer a useful reference for improving the construction system quality level.
基金supported by the Fundamental Research Funds for the Central Universities(2023JC004 and 2023YQ002)。
文摘Cloud workloads are highly dynamic and complex,making task scheduling in cloud computing a challenging problem.While several scheduling algorithms have been proposed in recent years,they are mainly designed to handle batch tasks and not well-suited for real-time workloads.To address this issue,researchers have started exploring the use of Deep Reinforcement Learning(DRL).However,the existing models are limited in handling independent tasks and cannot process workflows,which are prevalent in cloud computing and consist of related subtasks.In this paper,we propose SA-DQN,a scheduling approach specifically designed for real-time cloud workflows.Our approach seamlessly integrates the Simulated Annealing(SA)algorithm and Deep Q-Network(DQN)algorithm.The SA algorithm is employed to determine an optimal execution order of subtasks in a cloud server,serving as a crucial feature of the task for the neural network to learn.We provide a detailed design of our approach and show that SA-DQN outperforms existing algorithms in terms of handling real-time cloud workflows through experimental results.
基金support provided by the National Natural Science Foundation of China(Grant No.52362055)the Science and Technology Plan Project of Guangxi Zhuang Autonomous Region(Grant No.2021AC19334)Guangxi Science and Technology Major Program(Grant No.AA23062053).
文摘To provide a much more resilient transport scheme for tractor and trailer transportation systems,this paper explores the generation method of tractor and trailer transport schemes considering the influence of disrupted events.Three states of tractors including towing loaded trailers,towing empty trailers,and idle driving are taken into account.Based on the disruption management theory,a scheduling model is constructed to minimize the total deviation cost including transportation time,transportation path,and number of used vehicles under the three states of tractors.A heuristics based on the contract net and simulated annealing algorithm is designed to solve the proposed model.Through comparative analysis of examples with different numbers of newly added transportation tasks and different types of road networks,the performance of the contract net algorithm in terms of deviations in idle driving paths,empty trailer paths,loaded trailer paths,time,number of used vehicles,and total deviation cost are analyzed.The results demonstrate the effectiveness of the model and algorithm,highlighting the superiority of the disruption management model and the contract net annealing algorithm.The study provides a reference for handling unexpected events in the tractor and trailer transportation industry.
基金supported by the Jiangsu Provincial College Students Innovation andEntrepreneurship Training Plan Project(grant number 202411276037Z)the Nanjing Institute ofTechnology Fund for Research Startup Projects of Introduced Talents(grant number TB202406012).
文摘To enhance the rationality of the layout of electric vehicle charging stations,meet the actual needs of users,and optimise the service range and coverage efficiency of charging stations,this paper proposes an optimisation strategy for the layout of electric vehicle charging stations that integrates Mini Batch K-Means and simulated annealing algorithms.By constructing a circle-like service area model with the charging station as the centre and a certain distance as the radius,the maximum coverage of electric vehicle charging stations in the region and the influence of different regional environments on charging demand are considered.Based on the real data of electric vehicle charging stations in Nanjing,Jiangsu Province,this paper uses the model proposed in this paper to optimise the layout of charging stations in the study area.The results show that the optimisation strategy incorporating Mini Batch K-Means and simulated annealing algorithms outperforms the existing charging station layouts in terms of coverage and the number of stations served,and compared to the original charging station layouts,the optimised charging station layouts have flatter Lorentzian curves and are closer to the average distribution.The proposed optimisation strategy not only improves the service efficiency and user satisfaction of EV(Electric Vehicle)charging stations but also provides a reference for the layout optimisation of EV charging stations in other cities,which has important practical value and promotion potential.
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.
文摘This paper presents an approximate algorithm based on simulated annealing to achieve the maximum probability of the minimal cut sets for a fault tree. Near optimal minimal cut sets and important sequence of the basic events are also solved by the method. Computer simulations show that the algorithm performs very well.
基金supported by the Natural Science Foundation of China (U21A20244 and 32071758)funding provided by University of Eastern Finland (including Kuopio University Hospital)
文摘Forest inventory is increasingly producing infor-mation on the locations and sizes of individual trees.This information can be acquired by airborne or terrestrial laser scanning or analyzing photogrammetric data.However,all trees are seldom detected,especially in young,dense,or multi-layered stands.On the other hand,the complete size distributions of trees can be predicted with various methods,for instance,kNN data imputation in an area-based LiDAR inventory,predicting the parameters of a distribution func-tion from remote sensing data,field sampling,or using his-togram matching and calibration methods.The predicted distribution can be used to estimate the number and sizes of the non-detected trees.The study’s objective was to develop a method for forest planning that efficiently uses the avail-able tree-level data in management optimization.The study developed a two-stage hierarchical method for tree-level management optimization for cases where only part of the trees is detected or measured individually.Cutting years and harvest rate curves for the non-detected trees are optimized at the higher level,and the cutting events of the detected trees are optimized at the lower level.The study used differ-ential evolution at the higher level and simulated annealing at the lower level.The method was tested and demonstrated in even-aged Larix olgensis plantations in the Heilongjiang province of China.The optimizations showed that optimiz-ing the harvest decisions at the tree level improves the profit-ability of management compared to optimizations in which only the dependence of thinning intensity on tree diameter is optimized.The approach demonstrated in this study pro-vides feasible options for tree-level forest planning based on LiDAR inventories.The method is immediately applicable to forestry practice,especially in plantations.
基金supported by the National Natural Science Foundation of China(NSFC)(Nos.22173051,21829301,21774066)College Discipline Innovation and Intelligence Introduction Program(111 Project(B16027)+1 种基金the International Cooperation Base(2016D01025)Tianjin International Joint Research and Development Center。
文摘Spatial confinement of block copolymers can induce frustrations,which can further be utilized to regulate self-assembled structures,thus providing an efficient route for fabricating novel structures.We studied the self-assembly of AB di-block copolymers(di-BCPs)confined in Janus spherical nanocavities using simulations,and explained the structure formation mechanisms.In the case of a strongly selective cavity wall,all the lamella-forming,gyroid-forming,and cylinder-forming di-BCPs can form interfacial frustration-induced Janus concentric perforated lamellar nanoparticles,whose outermost is a Janus spherical shell and the internal is a sphere with concentric perforated lamellar structure.In particular,Janus concentric perforated lamellar nanoparticles with holes distributed only near the equatorial plane were obtained in both lamella-forming and gyroid-forming di-BCPs,directly reflecting the effect of interfacial frustration.The minority-block domain of the cylider-forming di-BCPs may form hemispherical perforated lamellar structures with holes distributed in parallel layers with a specific orientation.For symmetric di-BCPs,both the A and B domains in each nanoparticle are continuous,interchangeable,and have rotational symmetry.While for gyroid-forming and cylinder-forming di-BCPs,only the majority-block domains are continuous in each nanoparticle,and holes in the minority-block domains usually have rotational symmetry.In the case of a weakly selective cavity wall,the inhomogeneity of the cavity wall results in structures having a specific orientation(such as flower-like and branched structures in gyroid-forming and cylinder-forming di-BCPs)and a perforated wetting layer with uniformly distributed holes.The novel nanoparticles obtained may have potential applications in nanotechnology as functional nanostructures or nanoparticles.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.72101046 and 61672128)。
文摘Recent studies employing deep learning to solve the traveling salesman problem(TSP)have mainly focused on learning construction heuristics.Such methods can improve TSP solutions,but still depend on additional programs.However,methods that focus on learning improvement heuristics to iteratively refine solutions remain insufficient.Traditional improvement heuristics are guided by a manually designed search strategy and may only achieve limited improvements.This paper proposes a novel framework for learning improvement heuristics,which automatically discovers better improvement policies for heuristics to iteratively solve the TSP.Our framework first designs a new architecture based on a transformer model to make the policy network parameterized,which introduces an action-dropout layer to prevent action selection from overfitting.It then proposes a deep reinforcement learning approach integrating a simulated annealing mechanism(named RL-SA)to learn the pairwise selected policy,aiming to improve the 2-opt algorithm's performance.The RL-SA leverages the whale optimization algorithm to generate initial solutions for better sampling efficiency and uses the Gaussian perturbation strategy to tackle the sparse reward problem of reinforcement learning.The experiment results show that the proposed approach is significantly superior to the state-of-the-art learning-based methods,and further reduces the gap between learning-based methods and highly optimized solvers in the benchmark datasets.Moreover,our pre-trained model M can be applied to guide the SA algorithm(named M-SA(ours)),which performs better than existing deep models in small-,medium-,and large-scale TSPLIB datasets.Additionally,the M-SA(ours)achieves excellent generalization performance in a real-world dataset on global liner shipping routes,with the optimization percentages in distance reduction ranging from3.52%to 17.99%.
基金Supported by the National Natural Science Foundation of China (Grant No. 52071097)Hainan Provincial Natural Science Foundation of China (Grant No. 522MS162)Research Fund from Science and Technology on Underwater Vehicle Technology Laboratory (Grant No. 2021JCJQ-SYSJJ-LB06910)。
文摘Path planning for recovery is studied on the engineering background of double unmanned surface vehicles(USVs)towing oil booms for oil spill recovery.Given the influence of obstacles on the sea,the improved artificial potential field(APF)method is used for path planning.For addressing the two problems of unreachable target and local minimum in the APF,three improved algorithms are proposed by combining the motion performance constraints of the double USV system.These algorithms are then combined as the final APF-123 algorithm for oil spill recovery.Multiple sets of simulation tests are designed according to the flaws of the APF and the process of oil spill recovery.Results show that the proposed algorithms can ensure the system’s safety in tracking oil spills in a complex environment,and the speed is increased by more than 40%compared with the APF method.
基金supported by the KESTO project (Planning and implementation of the harvesting of climate-resilient continuous cover forests (CCF) using digitalization in North Karelia),Grant Number 41007-00241901funded by the European Regional Development Fund (ERDF)funding provided by University of Eastern Finland (including Kuopio University Hospital)
文摘The current trends in forestry in Europe include the increased use of continuous cover forestry(CCF)and the increased availability of tree-level forest inventory data.Accordingly,recent literature suggests methodologies for optimizing the harvest decisions at the tree level.Using tree-level optimization for all trees of the stand is computationally demanding.This study proposed a two-level optimization method for CCF where the harvest prescriptions are optimized at the tree level for only a part of the trees or the first cuttings.The higher-level algorithm optimizes the cutting years and the harvest rates of those diameter classes for which tree-level optimization is not used.The lower-level algorithm allocates the individually optimized trees to different cutting events.The most detailed problem formulations,employing much tree-level optimization,resulted in the highest net present value and longest optimization time.However,restricting tree-level optimization to the largest trees and first cuttings did not significantly alter the time,intensity,or type of first cutting.Computing times could also be shortened by applying accumulated knowledge from previous optimizations,implementing learning aspects in heuristic search,and optimizing the search algorithms for short computing time and good-quality solutions.
基金supported by the Beijing Municipal Natural Science Foundation(1242025)。
文摘Wettability has complex effects on the physical properties of reservoir rocks.The wettability of rocks should be characterized accurately to explore and develop oil and gas.Researchers have studied the rock wettability by dielectric spectra which contained abundant information.To study the rock wettability from dielectric dispersion,four rock samples with different wettabilities were used to design an experimental measurement flow.The relative dielectric permittivity in the frequency range of 100 Hz-10MHz and nuclear magnetic resonance T_(2)spectra of the samples were obtained.Subsequently,the wettabilities of the rocks were verified by the T_(2)spectra.The dielectric dispersions of the samples under different conditions were analyzed.Furthermore,the simulated-annealing(SA)algorithm was used to invert the wettability and related parameters of the rocks by a dielectric dispersion model.The results indicated that the dielectric permittivity of lipophilic rocks is lower than that of hydrophilic rocks,and that the dielectric permittivity of hydrophilic rocks decreases faster as the frequency increases.The dielectric permittivity in the high-frequency band is associated with the water content.The rock wettability parameters obtained via inversion agreed well with the T_(2)spectra,and the saturation index of the rocks.The errors between the rock permittivity calculated by the inverted parameters and the experimentally measured values were minor,indicating that rock wettability could be accurately characterized using dielectric dispersion data.
文摘Since there were few chaotic neural networks applicable to the global optimization, in this paper, we propose a new neural network model ? chaotic parameters disturbance annealing (CPDA) network, which is superior to other existing neural networks, genetic algorithms, and simulated annealing algorithms in global optimization. In the present CPDA network, we add some chaotic parameters in the energy function, which make the Hopfield neural network escape from the attraction of a local minimal solution and with the parameter annealing, our model will converge to the global optimal solutions quickly and steadily. The converge ability and other characters are also analyzed in this paper. The benchmark examples show the present CPDA neural network's merits in nonlinear global optimization.
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.
文摘This article presents a simulated annealing-based approach to the optimal synthesis of distillation column considering intermediate heat exchangers arrangements. T-he number of intermediate condensers and/or intermediate reboilers, the placement locations, the.operating pressure of column, and the heat duties of intermediate heat exchangers are treated as optimization variables. A novel coding procedure making use of an integer number series is proposed to represent and manipulate the structure of system and a stage-to-stage method is used for column design and cost calculation. With the representation procedure, the synthesis problem is formulated as a mixed integer nonlinear programming （MINLP） problem, which can then be solved with an improved simulated annealing algorithm. Two examples are illustrated to show the effectiveness of the suggested approach.
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 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 Science Fund of China under Grants 61603034China Postdoctoral Science Foundation under Grant 2019M653870XB+1 种基金Beijing Municipal Natural Science Foundation (3182027)Fundamental Research Funds for the Central Universities,China,FRF-GF-17-B44,and XJS191315
文摘At present,simultaneous localization and mapping(SLAM) for an autonomous underwater vehicle(AUV)is a research hotspot.Aiming at the problem of non-linear model and non-Gaussian noise in AUV motion,an improved method of variance reduction fast simultaneous localization and mapping(FastSLAM) with simulated annealing is proposed to solve the problems of particle degradation,particle depletion and particle loss in traditional FastSLAM,which lead to the reduction of AUV location estimation accuracy.The adaptive exponential fading factor is generated by the anneal function of simulated annealing algorithm to improve the effective particle number and replace resampling.By increasing the weight of small particles and decreasing the weight of large particles,the variance of particle weight can be reduced,the number of effective particles can be increased,and the accuracy of AUV location and feature location estimation can be improved to some extent by retaining more information carried by particles.The experimental results based on trial data show that the proposed simulated annealing variance reduction FastSLAM method avoids particle degradation,maintains the diversity of particles,weakened the degeneracy and improves the accuracy and stability of AUV navigation and localization system.
