[Objective] The aim was to study the feature extraction of stored-grain insects based on ant colony optimization and support vector machine algorithm, and to explore the feasibility of the feature extraction of stored...[Objective] The aim was to study the feature extraction of stored-grain insects based on ant colony optimization and support vector machine algorithm, and to explore the feasibility of the feature extraction of stored-grain insects. [Method] Through the analysis of feature extraction in the image recognition of the stored-grain insects, the recognition accuracy of the cross-validation training model in support vector machine （SVM） algorithm was taken as an important factor of the evaluation principle of feature extraction of stored-grain insects. The ant colony optimization （ACO） algorithm was applied to the automatic feature extraction of stored-grain insects. [Result] The algorithm extracted the optimal feature subspace of seven features from the 17 morphological features, including area and perimeter. The ninety image samples of the stored-grain insects were automatically recognized by the optimized SVM classifier, and the recognition accuracy was over 95%. [Conclusion] The experiment shows that the application of ant colony optimization to the feature extraction of grain insects is practical and feasible.展开更多
Low earth orbit (LEO) satellite networkscan provide wider service coverage and lower latencythan traditional terrestrial networks, which haveattracted considerable attention. However, the unevendistribution of human p...Low earth orbit (LEO) satellite networkscan provide wider service coverage and lower latencythan traditional terrestrial networks, which haveattracted considerable attention. However, the unevendistribution of human population and data trafficon the ground incurs unbalanced traffic load inLEO satellite networks. To this end, we proposea load-balancing routing algorithm for LEO satellitenetworks based on ant colony optimization and reinforcementlearning. In the ant colony algorithm,we improve the pheromone update rule by introducingload-aware heuristic information, e.g., the currentnode transmission overhead, delay and load status, andreinforcement learning-based link quality evaluation.It enables the routing algorithm to select the lightlyloaded node as the next hop to balance the networkload. We simulate and verify the proposed algorithmusing the NS2 simulation platform, and the resultsshow that our algorithm improves the data delivery ratioand throughput while ensuring lower latency andtransmission overhead.展开更多
This paper aims at rescheduling of observing spacecraft imaging plans under uncertainties. Firstly, uncertainties in spacecraft observation scheduling are analyzed. Then, considering the uncertainties with fuzzy featu...This paper aims at rescheduling of observing spacecraft imaging plans under uncertainties. Firstly, uncertainties in spacecraft observation scheduling are analyzed. Then, considering the uncertainties with fuzzy features, this paper proposes a fuzzy neural network and a hybrid rescheduling policy to deal with them. It then establishes a mathematical model and manages to solve the rescheduling problem by proposing an ant colony algorithm, which introduces an adaptive control mechanism and takes advantage of the information in an existing schedule. Finally, the above method is applied to solve the rescheduling problem of a certain type of earth-observing satellite. The computation of the example shows that the approach is feasible and effective in dealing with uncertainties in spacecraft observation scheduling. The approach designed here can be useful in solving the problem that the original schedule is contaminated by disturbances.展开更多
As electro-hydrostatic actuator(EHA)technology advances towards lightweight and integration,the demand for enhanced internal flow pathways in hydraulic valve blocks intensifies.However,owing to the constraints imposed...As electro-hydrostatic actuator(EHA)technology advances towards lightweight and integration,the demand for enhanced internal flow pathways in hydraulic valve blocks intensifies.However,owing to the constraints imposed by traditional manufacturing processes,conventional hydraulic integrated valve blocks fail to satisfy the demands of a more compact channel layout and lower energy dissipation.Notably,the subjectivity in the arrangement of internal passages results in a time-consuming and labor-intensive process.This study employed additive manufacturing technology and the ant colony algorithm and B-spline curves for the meticulous design of internal passages within an aviation EHA valve block.The layout environment for the valve block passages was established,and path optimization was achieved using the ant colony algorithm,complemented by smoothing using B-spline curves.Three-dimensional modeling was performed using SolidWorks software,revealing a 10.03%reduction in volume for the optimized passages compared with the original passages.Computational fluid dynamics(CFD)simulations were performed using Fluent software,demonstrating that the algorithmically optimized passages effectively prevented the occurrence of vortices at right-angled locations,exhibited superior flow characteristics,and concurrently reduced pressure losses by 34.09%-36.36%.The small discrepancy between the experimental and simulation results validated the efficacy of the ant colony algorithm and B-spline curves in optimizing the passage design,offering a viable solution for channel design in additive manufacturing.展开更多
In order to improve safety,economy efficiency and design automation degree of air route in terminal airspace,Three-dimensional(3D)planning of routes network is investigated.A waypoint probability search method is prop...In order to improve safety,economy efficiency and design automation degree of air route in terminal airspace,Three-dimensional(3D)planning of routes network is investigated.A waypoint probability search method is proposed to optimize individual flight path.Through updating horizontal pheromones by negative feedback factors,an antcolony algorithm of path searching in 3Dterminal airspace is implemented.The principle of optimization sequence of arrival and departure routes is analyzed.Each route is optimized successively,and the overall optimization of the whole route network is finally achieved.A case study shows that it takes about 63 sto optimize 8arrival and departure routes,and the operation efficiency can be significantly improved with desirable safety and economy.展开更多
In order to increase the robust performance of electro-hydraulic servo system, the system transfer function was identified by the intergration algorithm of genetic algorithm and ant colony optimization(GA-ACO), which ...In order to increase the robust performance of electro-hydraulic servo system, the system transfer function was identified by the intergration algorithm of genetic algorithm and ant colony optimization(GA-ACO), which was based on standard genetic algorithm and combined with positive feedback mechanism of ant colony algorithm. This method can obtain the precise mathematic model of continuous rotary motor which determines the order of servo system. Firstly, by constructing an appropriate fitness function, the problem of system parameters identification is converted into the problem of system parameter optimization. Secondly, in the given upper and lower bounds a set of optimal parameters are selected to meet the best approximation of the actual system. And the result shows that the identification output can trace the sampling output of actual system, and the error is very small. In addition, another set of experimental data are used to test the identification result. The result shows that the identification parameters can approach the actual system. The experimental results verify the feasibility of this method. And it is fit for the parameter identification of general complex system using the integration algorithm of GA-ACO.展开更多
Although ant colony algorithm for the heuristic solution of hard combinational optimization problems enjoy a rapidly growing popularity, but little is known about its convergence properties. Based on the introduction ...Although ant colony algorithm for the heuristic solution of hard combinational optimization problems enjoy a rapidly growing popularity, but little is known about its convergence properties. Based on the introduction of the basic principle and mathematical model, a novel approach to the convergence proof that applies directly to the ant colony algorithm is proposed in this paper. Then, a MATLAB GUI- based ant colony algorithm simulation platform is developed, and the interface of this simulation platform is very friendly, easy to use and to modify.展开更多
The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the slid...The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the sliding crack or so called, “wing crack” model. Fairhurst-Cook model explains this specific type of failure which starts by a pre-crack and finally breaks the rock by propagating 2-D cracks under uniaxial compression. In this paper, optimization of this model has been considered and the process has been done by a complete sensitivity analysis on the main parameters of the model and excluding the trends of their changes and also their limits and “peak points”. Later on this paper, three artificial intelligence algorithms including Particle Swarm Intelligence (PSO), Ant Colony Optimization (ACO) and genetic algorithm (GA) has been used and compared in order to achieve optimized sets of parameters resulting in near-maximum or near-minimum amounts of wedging forces creating a wing crack.展开更多
For the formation control of multi-robot fish system,the paper focuses on how to reach the specified location along the shortest path during the moving process.The problem is handled by a proposed strategy which combi...For the formation control of multi-robot fish system,the paper focuses on how to reach the specified location along the shortest path during the moving process.The problem is handled by a proposed strategy which combines the leader-follower framework with ant colony algorithm.The strategy will first search for the shortest path by using ant colony algorithm.Then the formation controller will be designed according to the principle of leader-follower algorithm and fuzzy control mechanism to make robotic fish keep formation and arrive at the designated location safely along the shortest path.The feasibility of the proposed method is verified by simulation,which indicates that the multiple robot fish system can move with formation and fluency on the one hand,and as well find the shortest motion path from a given starting point to the destination point on the other hand.展开更多
Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as ...Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as inaccurate node clustering,low energy efficiency,and shortened network lifespan in practical deployments,which significantly limit their large-scale application.To address these issues,this paper proposes an Adaptive Chaotic Ant Colony Optimization algorithm(AC-ACO),aiming to optimize the energy utilization and system lifespan of WSNs.AC-ACO combines the path-planning capability of Ant Colony Optimization(ACO)with the dynamic characteristics of chaotic mapping and introduces an adaptive mechanism to enhance the algorithm’s flexibility and adaptability.By dynamically adjusting the pheromone evaporation factor and heuristic weights,efficient node clustering is achieved.Additionally,a chaotic mapping initialization strategy is employed to enhance population diversity and avoid premature convergence.To validate the algorithm’s performance,this paper compares AC-ACO with clustering methods such as Low-Energy Adaptive Clustering Hierarchy(LEACH),ACO,Particle Swarm Optimization(PSO),and Genetic Algorithm(GA).Simulation results demonstrate that AC-ACO outperforms the compared algorithms in key metrics such as energy consumption optimization,network lifetime extension,and communication delay reduction,providing an efficient solution for improving energy efficiency and ensuring long-term stable operation of wireless sensor networks.展开更多
Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of t...Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of the searched point to determine the next search point during the search process,reducing the uncertainty in the random search process.Due to the ability of the Bayesian algorithm to reduce uncertainty,a Bayesian ACO algorithm is proposed in this paper to increase the convergence speed of the conventional ACO algorithm for image edge detection.In addition,this paper has the following two innovations on the basis of the classical algorithm,one of which is to add random perturbations after completing the pheromone update.The second is the use of adaptive pheromone heuristics.Experimental results illustrate that the proposed Bayesian ACO algorithm has faster convergence and higher precision and recall than the traditional ant colony algorithm,due to the improvement of the pheromone utilization rate.Moreover,Bayesian ACO algorithm outperforms the other comparative methods in edge detection task.展开更多
An adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of updating trail information. The algorithm can keep good balance between accelerating convergence and averting precocity and s...An adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of updating trail information. The algorithm can keep good balance between accelerating convergence and averting precocity and stagnation. The results of function optimization show that the algorithm has good searching ability and high convergence speed. The algorithm is employed to design a neuro-fuzzy controller for real-time control of an inverted pendulum. In order to avoid the combinatorial explosion of fuzzy rules due tσ multivariable inputs, a state variable synthesis scheme is employed to reduce the number of fuzzy rules greatly. The simulation results show that the designed controller can control the inverted pendulum successfully.展开更多
Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective funct...Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective function,and an air route network node(ARNN)optimization model was developed to circumvent the restrictions imposed by″three areas″,also known as prohibited areas,restricted areas,and dangerous areas(PRDs),by creating agrid environment.And finally the objective function was solved by means of an adaptive ant colony algorithm(AACA).The A593,A470,B221,and G204 air routes in the busy ZSHA flight information region,where the airspace includes areas with different levels of PRDs,were taken as an example.Based on current flight patterns,a layout optimization of the ARNN was computed using this model and algorithm and successfully avoided PRDs.The optimized result reduced the total length of routes by 2.14% and the total cost by 9.875%.展开更多
Based on structural surface normal vector spherical distance and the pole stereographic projection Euclidean distance,two distance functions were established.The cluster analysis of structure surface was conducted by ...Based on structural surface normal vector spherical distance and the pole stereographic projection Euclidean distance,two distance functions were established.The cluster analysis of structure surface was conducted by the use of ATTA clustering methods based on ant colony piles,and Silhouette index was introduced to evaluate the clustering effect.The clustering analysis of the measured data of Sanshandao Gold Mine shows that ant colony ATTA-based clustering method does better than K-mean clustering analysis.Meanwhile,clustering results of ATTA method based on pole Euclidean distance and ATTA method based on normal vector spherical distance have a great consistence.The clustering results are most close to the pole isopycnic graph.It can efficiently realize grouping of structural plane and determination of the dominant structural surface direction.It is made up for the defects of subjectivity and inaccuracy in icon measurement approach and has great engineering value.展开更多
The problem of fault reasoning has aroused great concern in scientific and engineering fields.However,fault investigation and reasoning of complex system is not a simple reasoning decision-making problem.It has become...The problem of fault reasoning has aroused great concern in scientific and engineering fields.However,fault investigation and reasoning of complex system is not a simple reasoning decision-making problem.It has become a typical multi-constraint and multi-objective reticulate optimization decision-making problem under many influencing factors and constraints.So far,little research has been carried out in this field.This paper transforms the fault reasoning problem of complex system into a paths-searching problem starting from known symptoms to fault causes.Three optimization objectives are considered simultaneously: maximum probability of average fault,maximum average importance,and minimum average complexity of test.Under the constraints of both known symptoms and the causal relationship among different components,a multi-objective optimization mathematical model is set up,taking minimizing cost of fault reasoning as the target function.Since the problem is non-deterministic polynomial-hard(NP-hard),a modified multi-objective ant colony algorithm is proposed,in which a reachability matrix is set up to constrain the feasible search nodes of the ants and a new pseudo-random-proportional rule and a pheromone adjustment mechinism are constructed to balance conflicts between the optimization objectives.At last,a Pareto optimal set is acquired.Evaluation functions based on validity and tendency of reasoning paths are defined to optimize noninferior set,through which the final fault causes can be identified according to decision-making demands,thus realize fault reasoning of the multi-constraint and multi-objective complex system.Reasoning results demonstrate that the improved multi-objective ant colony optimization(IMACO) can realize reasoning and locating fault positions precisely by solving the multi-objective fault diagnosis model,which provides a new method to solve the problem of multi-constraint and multi-objective fault diagnosis and reasoning of complex system.展开更多
To develop a better approach for spatial evaluation of drinking water quality, an intelligent evaluation method integrating a geographical information system(GIS) and an ant colony clustering algorithm(ACCA) was used....To develop a better approach for spatial evaluation of drinking water quality, an intelligent evaluation method integrating a geographical information system(GIS) and an ant colony clustering algorithm(ACCA) was used. Drinking water samples from 29 wells in Zhenping County, China, were collected and analyzed. 35 parameters on water quality were selected, such as chloride concentration, sulphate concentration, total hardness, nitrate concentration, fluoride concentration, turbidity, pH, chromium concentration, COD, bacterium amount, total coliforms and color. The best spatial interpolation methods for the 35 parameters were found and selected from all types of interpolation methods in GIS environment according to the minimum cross-validation errors. The ACCA was improved through three strategies, namely mixed distance function, average similitude degree and probability conversion functions. Then, the ACCA was carried out to obtain different water quality grades in the GIS environment. In the end, the result from the ACCA was compared with those from the competitive Hopfield neural network(CHNN) to validate the feasibility and effectiveness of the ACCA according to three evaluation indexes, which are stochastic sampling method, pixel amount and convergence speed. It is shown that the spatial water quality grades obtained from the ACCA were more effective, accurate and intelligent than those obtained from the CHNN.展开更多
It took that the weight minimum and drive efficiency maximal were as double optimizing target,the optimization model had built the drilling string,and the optimization solution was used of the ant colony algorithm to ...It took that the weight minimum and drive efficiency maximal were as double optimizing target,the optimization model had built the drilling string,and the optimization solution was used of the ant colony algorithm to find in progress.Adopted a two-layer search of the continuous space ant colony algorithm with overlapping or variation global ant search operation strategy and conjugated gradient partial ant search operation strat- egy.The experiment indicates that the spiral drill weight reduces 16.77% and transports the efficiency enhance 7.05% through the optimization design,the ant colony algorithm application on the spiral drill optimized design has provided the basis for the system re- search screw coal mine machine.展开更多
Buffer influences the performance of production lines greatly.To solve the buffer allocation problem(BAP) in serial production lines with unreliable machines effectively,an optimization method is proposed based on an ...Buffer influences the performance of production lines greatly.To solve the buffer allocation problem(BAP) in serial production lines with unreliable machines effectively,an optimization method is proposed based on an improved ant colony optimization(IACO) algorithm.Firstly,a problem domain describing buffer allocation is structured.Then a mathematical programming model is established with an objective of maximizing throughput rate of the production line.On the basis of the descriptions mentioned above,combining with a two-opt strategy and an acceptance probability rule,an IACO algorithm is built to solve the BAP.Finally,the simulation experiments are designed to evaluate the proposed algorithm.The results indicate that the IACO algorithm is valid and practical.展开更多
How to extract river nets effectively is of great significance for water resources investigation,flood forecasting and environmental monitoring,etc.In the paper,combining with ant colony algorithm,a new approach of ex...How to extract river nets effectively is of great significance for water resources investigation,flood forecasting and environmental monitoring,etc.In the paper,combining with ant colony algorithm,a new approach of extracting river nets on moderate-resolution imaging spectroradiometer(MODIS)remote sensing images was proposed through analyzing two general extraction methods of river nets.The experiment results show that river nets can be optimized by ant colony algorithm efficiently,and difference ratio between the experimental vectorgraph and the data of National Fundamental Geographic Information System is down to 8.7%.The proposed algorithm can work for extracting river nets on MODIS remote sensing images effectively.展开更多
基金Supported by the National Natural Science Foundation of China(31101085)the Program for Young Core Teachers of Colleges in Henan(2011GGJS-094)the Scientific Research Project for the High Level Talents,North China University of Water Conservancy and Hydroelectric Power~~
文摘[Objective] The aim was to study the feature extraction of stored-grain insects based on ant colony optimization and support vector machine algorithm, and to explore the feasibility of the feature extraction of stored-grain insects. [Method] Through the analysis of feature extraction in the image recognition of the stored-grain insects, the recognition accuracy of the cross-validation training model in support vector machine （SVM） algorithm was taken as an important factor of the evaluation principle of feature extraction of stored-grain insects. The ant colony optimization （ACO） algorithm was applied to the automatic feature extraction of stored-grain insects. [Result] The algorithm extracted the optimal feature subspace of seven features from the 17 morphological features, including area and perimeter. The ninety image samples of the stored-grain insects were automatically recognized by the optimized SVM classifier, and the recognition accuracy was over 95%. [Conclusion] The experiment shows that the application of ant colony optimization to the feature extraction of grain insects is practical and feasible.
基金supported in part by the National Natural Science Foundation of China(Grant No.62273107,61702127,62272113)Science and Technology Program of Guangzhou(Grant No.201804010461).
文摘Low earth orbit (LEO) satellite networkscan provide wider service coverage and lower latencythan traditional terrestrial networks, which haveattracted considerable attention. However, the unevendistribution of human population and data trafficon the ground incurs unbalanced traffic load inLEO satellite networks. To this end, we proposea load-balancing routing algorithm for LEO satellitenetworks based on ant colony optimization and reinforcementlearning. In the ant colony algorithm,we improve the pheromone update rule by introducingload-aware heuristic information, e.g., the currentnode transmission overhead, delay and load status, andreinforcement learning-based link quality evaluation.It enables the routing algorithm to select the lightlyloaded node as the next hop to balance the networkload. We simulate and verify the proposed algorithmusing the NS2 simulation platform, and the resultsshow that our algorithm improves the data delivery ratioand throughput while ensuring lower latency andtransmission overhead.
基金supported by the National Natural Science Foundation of China (No. 61203151)the National Basic Research Program of China (973 Program) (No. 2012CB720003)+2 种基金the Postdoctoral Science Foundation of China (20100471044)the Fundamental Research Funds for the Central Universities of China (No. HIT.NSRIF.2013038)the Key Laboratory Opening Funding of China (No. HIT.KLOF.2009071)
文摘This paper aims at rescheduling of observing spacecraft imaging plans under uncertainties. Firstly, uncertainties in spacecraft observation scheduling are analyzed. Then, considering the uncertainties with fuzzy features, this paper proposes a fuzzy neural network and a hybrid rescheduling policy to deal with them. It then establishes a mathematical model and manages to solve the rescheduling problem by proposing an ant colony algorithm, which introduces an adaptive control mechanism and takes advantage of the information in an existing schedule. Finally, the above method is applied to solve the rescheduling problem of a certain type of earth-observing satellite. The computation of the example shows that the approach is feasible and effective in dealing with uncertainties in spacecraft observation scheduling. The approach designed here can be useful in solving the problem that the original schedule is contaminated by disturbances.
基金Supported by National Natural Science Foundation of China(Grant No.51890881)。
文摘As electro-hydrostatic actuator(EHA)technology advances towards lightweight and integration,the demand for enhanced internal flow pathways in hydraulic valve blocks intensifies.However,owing to the constraints imposed by traditional manufacturing processes,conventional hydraulic integrated valve blocks fail to satisfy the demands of a more compact channel layout and lower energy dissipation.Notably,the subjectivity in the arrangement of internal passages results in a time-consuming and labor-intensive process.This study employed additive manufacturing technology and the ant colony algorithm and B-spline curves for the meticulous design of internal passages within an aviation EHA valve block.The layout environment for the valve block passages was established,and path optimization was achieved using the ant colony algorithm,complemented by smoothing using B-spline curves.Three-dimensional modeling was performed using SolidWorks software,revealing a 10.03%reduction in volume for the optimized passages compared with the original passages.Computational fluid dynamics(CFD)simulations were performed using Fluent software,demonstrating that the algorithmically optimized passages effectively prevented the occurrence of vortices at right-angled locations,exhibited superior flow characteristics,and concurrently reduced pressure losses by 34.09%-36.36%.The small discrepancy between the experimental and simulation results validated the efficacy of the ant colony algorithm and B-spline curves in optimizing the passage design,offering a viable solution for channel design in additive manufacturing.
基金supported by the National Natural Science Foundation of China(No.61039001)the State Technology Supporting Plan(No.2011BAH24B08)the Fundamental Research Funds for the Central Universities (No.ZXH2011A002)
文摘In order to improve safety,economy efficiency and design automation degree of air route in terminal airspace,Three-dimensional(3D)planning of routes network is investigated.A waypoint probability search method is proposed to optimize individual flight path.Through updating horizontal pheromones by negative feedback factors,an antcolony algorithm of path searching in 3Dterminal airspace is implemented.The principle of optimization sequence of arrival and departure routes is analyzed.Each route is optimized successively,and the overall optimization of the whole route network is finally achieved.A case study shows that it takes about 63 sto optimize 8arrival and departure routes,and the operation efficiency can be significantly improved with desirable safety and economy.
基金Project of China Postdoctoral Science Foundation,China (No. 2012M510982)Special Fund on the Science and Technology Innovation People of Harbin,China (No. 2011RFQXG002)+2 种基金Technology Item of Heilongjiang Provincial Education Committee,China (No.12511088)Postdoctoral Project of Heilongjiang,China (No. LBH-Z10117 )Youth Fund of Harbin University of Science and Technology,China (No. 2011YF030)
文摘In order to increase the robust performance of electro-hydraulic servo system, the system transfer function was identified by the intergration algorithm of genetic algorithm and ant colony optimization(GA-ACO), which was based on standard genetic algorithm and combined with positive feedback mechanism of ant colony algorithm. This method can obtain the precise mathematic model of continuous rotary motor which determines the order of servo system. Firstly, by constructing an appropriate fitness function, the problem of system parameters identification is converted into the problem of system parameter optimization. Secondly, in the given upper and lower bounds a set of optimal parameters are selected to meet the best approximation of the actual system. And the result shows that the identification output can trace the sampling output of actual system, and the error is very small. In addition, another set of experimental data are used to test the identification result. The result shows that the identification parameters can approach the actual system. The experimental results verify the feasibility of this method. And it is fit for the parameter identification of general complex system using the integration algorithm of GA-ACO.
文摘Although ant colony algorithm for the heuristic solution of hard combinational optimization problems enjoy a rapidly growing popularity, but little is known about its convergence properties. Based on the introduction of the basic principle and mathematical model, a novel approach to the convergence proof that applies directly to the ant colony algorithm is proposed in this paper. Then, a MATLAB GUI- based ant colony algorithm simulation platform is developed, and the interface of this simulation platform is very friendly, easy to use and to modify.
文摘The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the sliding crack or so called, “wing crack” model. Fairhurst-Cook model explains this specific type of failure which starts by a pre-crack and finally breaks the rock by propagating 2-D cracks under uniaxial compression. In this paper, optimization of this model has been considered and the process has been done by a complete sensitivity analysis on the main parameters of the model and excluding the trends of their changes and also their limits and “peak points”. Later on this paper, three artificial intelligence algorithms including Particle Swarm Intelligence (PSO), Ant Colony Optimization (ACO) and genetic algorithm (GA) has been used and compared in order to achieve optimized sets of parameters resulting in near-maximum or near-minimum amounts of wedging forces creating a wing crack.
基金This work is supported by National Nature Science Foundation of China(Nos.61075114,41076062).
文摘For the formation control of multi-robot fish system,the paper focuses on how to reach the specified location along the shortest path during the moving process.The problem is handled by a proposed strategy which combines the leader-follower framework with ant colony algorithm.The strategy will first search for the shortest path by using ant colony algorithm.Then the formation controller will be designed according to the principle of leader-follower algorithm and fuzzy control mechanism to make robotic fish keep formation and arrive at the designated location safely along the shortest path.The feasibility of the proposed method is verified by simulation,which indicates that the multiple robot fish system can move with formation and fluency on the one hand,and as well find the shortest motion path from a given starting point to the destination point on the other hand.
基金funded by the Natural Science Foundation of Xinjiang Uygur Autonomous Region:No.22D01B148Bidding Topics for the Center for Integration of Education and Production and Development of New Business in 2024:No.2024-KYJD05+1 种基金Basic Scientific Research Business Fee Project of Colleges and Universities in Autonomous Region:No.XJEDU2025P126Xinjiang College of Science&Technology School-level Scientific Research Fund Project:No.2024-KYTD01.
文摘Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as inaccurate node clustering,low energy efficiency,and shortened network lifespan in practical deployments,which significantly limit their large-scale application.To address these issues,this paper proposes an Adaptive Chaotic Ant Colony Optimization algorithm(AC-ACO),aiming to optimize the energy utilization and system lifespan of WSNs.AC-ACO combines the path-planning capability of Ant Colony Optimization(ACO)with the dynamic characteristics of chaotic mapping and introduces an adaptive mechanism to enhance the algorithm’s flexibility and adaptability.By dynamically adjusting the pheromone evaporation factor and heuristic weights,efficient node clustering is achieved.Additionally,a chaotic mapping initialization strategy is employed to enhance population diversity and avoid premature convergence.To validate the algorithm’s performance,this paper compares AC-ACO with clustering methods such as Low-Energy Adaptive Clustering Hierarchy(LEACH),ACO,Particle Swarm Optimization(PSO),and Genetic Algorithm(GA).Simulation results demonstrate that AC-ACO outperforms the compared algorithms in key metrics such as energy consumption optimization,network lifetime extension,and communication delay reduction,providing an efficient solution for improving energy efficiency and ensuring long-term stable operation of wireless sensor networks.
基金supported by the National Natural Science Foundation of China(62276055).
文摘Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of the searched point to determine the next search point during the search process,reducing the uncertainty in the random search process.Due to the ability of the Bayesian algorithm to reduce uncertainty,a Bayesian ACO algorithm is proposed in this paper to increase the convergence speed of the conventional ACO algorithm for image edge detection.In addition,this paper has the following two innovations on the basis of the classical algorithm,one of which is to add random perturbations after completing the pheromone update.The second is the use of adaptive pheromone heuristics.Experimental results illustrate that the proposed Bayesian ACO algorithm has faster convergence and higher precision and recall than the traditional ant colony algorithm,due to the improvement of the pheromone utilization rate.Moreover,Bayesian ACO algorithm outperforms the other comparative methods in edge detection task.
文摘An adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of updating trail information. The algorithm can keep good balance between accelerating convergence and averting precocity and stagnation. The results of function optimization show that the algorithm has good searching ability and high convergence speed. The algorithm is employed to design a neuro-fuzzy controller for real-time control of an inverted pendulum. In order to avoid the combinatorial explosion of fuzzy rules due tσ multivariable inputs, a state variable synthesis scheme is employed to reduce the number of fuzzy rules greatly. The simulation results show that the designed controller can control the inverted pendulum successfully.
基金supported by the the Youth Science and Technology Innovation Fund (Science)(Nos.NS2014070, NS2014070)
文摘Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective function,and an air route network node(ARNN)optimization model was developed to circumvent the restrictions imposed by″three areas″,also known as prohibited areas,restricted areas,and dangerous areas(PRDs),by creating agrid environment.And finally the objective function was solved by means of an adaptive ant colony algorithm(AACA).The A593,A470,B221,and G204 air routes in the busy ZSHA flight information region,where the airspace includes areas with different levels of PRDs,were taken as an example.Based on current flight patterns,a layout optimization of the ARNN was computed using this model and algorithm and successfully avoided PRDs.The optimized result reduced the total length of routes by 2.14% and the total cost by 9.875%.
基金Project(41272304)supported by the National Natural Science Foundation of ChinaProject(51074177)jointly supported by the National Natural Science Foundation and Shanghai Baosteel Group Corporation,ChinaProject(CX2012B070)supported by Hunan Provincial Innovation Fund for Postgraduated Students,China
文摘Based on structural surface normal vector spherical distance and the pole stereographic projection Euclidean distance,two distance functions were established.The cluster analysis of structure surface was conducted by the use of ATTA clustering methods based on ant colony piles,and Silhouette index was introduced to evaluate the clustering effect.The clustering analysis of the measured data of Sanshandao Gold Mine shows that ant colony ATTA-based clustering method does better than K-mean clustering analysis.Meanwhile,clustering results of ATTA method based on pole Euclidean distance and ATTA method based on normal vector spherical distance have a great consistence.The clustering results are most close to the pole isopycnic graph.It can efficiently realize grouping of structural plane and determination of the dominant structural surface direction.It is made up for the defects of subjectivity and inaccuracy in icon measurement approach and has great engineering value.
基金supported by Sub-project of Key National Science and Technology Special Project of China(Grant No.2011ZX05056)
文摘The problem of fault reasoning has aroused great concern in scientific and engineering fields.However,fault investigation and reasoning of complex system is not a simple reasoning decision-making problem.It has become a typical multi-constraint and multi-objective reticulate optimization decision-making problem under many influencing factors and constraints.So far,little research has been carried out in this field.This paper transforms the fault reasoning problem of complex system into a paths-searching problem starting from known symptoms to fault causes.Three optimization objectives are considered simultaneously: maximum probability of average fault,maximum average importance,and minimum average complexity of test.Under the constraints of both known symptoms and the causal relationship among different components,a multi-objective optimization mathematical model is set up,taking minimizing cost of fault reasoning as the target function.Since the problem is non-deterministic polynomial-hard(NP-hard),a modified multi-objective ant colony algorithm is proposed,in which a reachability matrix is set up to constrain the feasible search nodes of the ants and a new pseudo-random-proportional rule and a pheromone adjustment mechinism are constructed to balance conflicts between the optimization objectives.At last,a Pareto optimal set is acquired.Evaluation functions based on validity and tendency of reasoning paths are defined to optimize noninferior set,through which the final fault causes can be identified according to decision-making demands,thus realize fault reasoning of the multi-constraint and multi-objective complex system.Reasoning results demonstrate that the improved multi-objective ant colony optimization(IMACO) can realize reasoning and locating fault positions precisely by solving the multi-objective fault diagnosis model,which provides a new method to solve the problem of multi-constraint and multi-objective fault diagnosis and reasoning of complex system.
基金Projects(41161020,41261026) supported by the National Natural Science Foundation of ChinaProject(BQD2012013) supported by the Research starting Funds for Imported Talents,Ningxia University,China+1 种基金Project(ZR1209) supported by the Natural Science Funds,Ningxia University,ChinaProject(NGY2013005) supported by the Key Science Project of Colleges and Universities in Ningxia,China
文摘To develop a better approach for spatial evaluation of drinking water quality, an intelligent evaluation method integrating a geographical information system(GIS) and an ant colony clustering algorithm(ACCA) was used. Drinking water samples from 29 wells in Zhenping County, China, were collected and analyzed. 35 parameters on water quality were selected, such as chloride concentration, sulphate concentration, total hardness, nitrate concentration, fluoride concentration, turbidity, pH, chromium concentration, COD, bacterium amount, total coliforms and color. The best spatial interpolation methods for the 35 parameters were found and selected from all types of interpolation methods in GIS environment according to the minimum cross-validation errors. The ACCA was improved through three strategies, namely mixed distance function, average similitude degree and probability conversion functions. Then, the ACCA was carried out to obtain different water quality grades in the GIS environment. In the end, the result from the ACCA was compared with those from the competitive Hopfield neural network(CHNN) to validate the feasibility and effectiveness of the ACCA according to three evaluation indexes, which are stochastic sampling method, pixel amount and convergence speed. It is shown that the spatial water quality grades obtained from the ACCA were more effective, accurate and intelligent than those obtained from the CHNN.
基金the Liaoning Technical University Outstanding Youth Science Foundation(jx09-10)
文摘It took that the weight minimum and drive efficiency maximal were as double optimizing target,the optimization model had built the drilling string,and the optimization solution was used of the ant colony algorithm to find in progress.Adopted a two-layer search of the continuous space ant colony algorithm with overlapping or variation global ant search operation strategy and conjugated gradient partial ant search operation strat- egy.The experiment indicates that the spiral drill weight reduces 16.77% and transports the efficiency enhance 7.05% through the optimization design,the ant colony algorithm application on the spiral drill optimized design has provided the basis for the system re- search screw coal mine machine.
基金Supported by the National Natural Science Foundation of China(No.61273035,71471135)
文摘Buffer influences the performance of production lines greatly.To solve the buffer allocation problem(BAP) in serial production lines with unreliable machines effectively,an optimization method is proposed based on an improved ant colony optimization(IACO) algorithm.Firstly,a problem domain describing buffer allocation is structured.Then a mathematical programming model is established with an objective of maximizing throughput rate of the production line.On the basis of the descriptions mentioned above,combining with a two-opt strategy and an acceptance probability rule,an IACO algorithm is built to solve the BAP.Finally,the simulation experiments are designed to evaluate the proposed algorithm.The results indicate that the IACO algorithm is valid and practical.
基金National High Technology Research and Development Program of China(No.2007AA120305)National ScienceFoundation of China(No.40771145)+2 种基金Special Project of Ministry of Science and Technology of China(No.GYHY20070628)Subtopics of Ministry of Land and Resources Project of China(No.KD081902-03)Scientific Research and Innovation Project of Graduate School of Shanghai University,China(No.SHUCX101033)
文摘How to extract river nets effectively is of great significance for water resources investigation,flood forecasting and environmental monitoring,etc.In the paper,combining with ant colony algorithm,a new approach of extracting river nets on moderate-resolution imaging spectroradiometer(MODIS)remote sensing images was proposed through analyzing two general extraction methods of river nets.The experiment results show that river nets can be optimized by ant colony algorithm efficiently,and difference ratio between the experimental vectorgraph and the data of National Fundamental Geographic Information System is down to 8.7%.The proposed algorithm can work for extracting river nets on MODIS remote sensing images effectively.
