143 results about "Ant colony optimization algorithms" patented technology

The invention provides an unmanned aircraft route planning method based on combination of Voronoi diagram and ant group optimizing algorithm. Firstly a model is established in accordance with properties of various threaten sources, e. g. landform, radar, missile and anticraft gun, and the unmanned aircraft route cost includes threaten costs and fuel oil costs; then initial pheromone values are given to each edge of the Voronoi diagram, enabling an ant to begin the search from the Voronoi node nearest to the start point. The marching Voronoi edge is selected based ob the status shift rule, then the search at the Voronoi node nearest to the target point is finished; And when all the ants have finished their own candidate route selection, the pheromones at each edge of the Voronoi diagram are updated in accordance with the improving and updating rules, wherein the pheromones at edges with no ant passing by are evaporated, and the process is repeated until an optimal unmanned aircraft route is found out. The method is characterized by good real-time and high-speed performance, and the found route is closer to the actual optimal unmanned aircraft route.

The invention discloses an electric vehicle travel planning method based on multi-target optimization, generally comprising the following steps: (1) a travel planning problem model is established; (2) drivers provide travel information; and (3) an optimal scheme is solved based on a timed multi-target ant colony optimization algorithm. The problem model comprises a road network model, a vehicle model, and travel target and travel constraint definition. Travel information includes: not providing any information, providing constraint information, and providing optimization goal and constraint information. The ant colony optimization algorithm includes the steps of pheromone initialization, route transfer probability calculation, travel scheme search, air conditioner use determining, travel scheme ranking, pheromone updating, and loop optimization. A dynamic stochastic road network model is used to describe the traffic environment and plan the travel of electric vehicles, and target characteristics corresponding to different travel schemes can be reflected. The ant colony optimization algorithm ensures that a multi-target and multi-constraint optimized electric vehicle travel scheme is generated as the number of iterations increases.

The invention discloses a method for optimizing a plurality of QoS grid workflows by adopting an ACO, wherein, three kinds of QoS parameters are considered. The method can not only satisfy the QoS constraint given by a user but also optimize one QoS parameter given by the user. The method comprises the following steps that: firstly, ants are initialized, each ant can select a structural direction in random and establishes a task sequence in accordance with the preference constraint; next, each ant selects a kind of heuristic information on the basis of the pheromone by adopting the selection method of roulette and updates local pheromone. After the heuristic information is selected, the ants map tasks in a task list to the service process in sequence on the basis of the pheromone and the heuristic information. When all the ants complete the construction of solutions, each scheduling solution is evaluated and all the pheromone is updated.

The invention discloses an optimal allocation method of hub airport gate positions based on an improved ACO algorithm. The optimal allocation method relates to the technical field of airport resource allocation, is based on the analysis of airport gate position configuration conditions and airport operation management modes in China, aims at the problems that the existing gate position allocation problem takes single target into account and is not high in precision and efficiency of a solution algorithm, comprehensively considers the optimization targets of shortest total walking distance of passengers, the most balanced idle time of gate positions and the least number of parking aprons, establishes a gate position multi-target optimized allocation model, and adopts an improved ant colony optimization algorithm for solving the model, so that the gate positions can be reasonably and effectively allocated to flights.

The invention provides a characteristic spectrum area selection method for a near infrared spectrum. The characteristic spectrum area selection method comprises the following steps of: applying a Monte Carlo probability selection combined ant colony optimization algorithm to a characteristic spectrum area selection problem of the near infrared spectrum; setting a dynamic section range and initializing algorithm parameters to obtain each spectrum section of an object to be taken as an equivalent searching point; establishing a partial least square analyzing model by taking the quality or characteristics of the object to be detected as a standard reference; predicating a root-mean-square error by the model to repeatedly carry out weighting calculation to update pheromone vectors according to the predicated root-mean-square error; carrying out iterative computation and searching to obtain the optimal characteristic spectrum area of the near infrared spectrum; and carrying out multiple circulating calculation and automatically judging to obtain the optimal characteristic spectrum area of the near infrared spectrum. The characteristic spectrum area selection method disclosed by the invention combines the wholeness of Monte Carlo probability selection and ant colony optimization algorithm positive feedback so as to effectively avoid the disadvantages of experience selection in a modeling process and data redundancy of all selections, rapidly obtain a global optimum characteristic spectrum area, and improve the precision and the stability of modeling.

The invention discloses a base station system polling path automatization determination method based on shortest cycle station system, including: firstly mapping the base station geographic position information provided by MapInfo onto a base station system network map; determining the polling radius of the base station; adopting the ant algorithm to figure out the shortest cycle of each cluster in order to determine the optimal polling path in the cluster. Based on the mobile communication base station system operation features, the invention adopts a dynamic base station system polling path automatization determination method based on geographical cluster and shortest cycle station system, which can obtain effective and reasonable polling path according to a large number, widely distributed base station system. The method can be widely and conveniently applied to polling and maintenance of equipment in other areas such as electronic police, traffic lights, global eyes, and the like.

The invention discloses a method for determining automatically mosaic line of orthophoto and performing automatic seamless mosaic of orthophoto, including selecting the optimum route to evade obstacle area in a difference image using the positive feedback of ant algorithm and elicitation type search characteristic of swarm intelligence; obtaining the mosaic lines of two orthophotoes to be spliced, performing mosaic fusion to the orthophoto pairs using the mosaic lines, thereby implementing the automatic seamless mosaic of orthophoto. The method which determines automatically the position of mosaic lines in the orthophoto mosaic process is capable of improving greatly the production efficiency of orthophoto map in mapping industry by replacing the prior method for determining or modifying mosaic lines manually, in which the mosaic lines evades automatically ground feature higher than the ground, such as houses, crown of trees, and the like, and evades the areas with large contrast of imaging colors in adjacent orthophoto maps.

This invention relates to communication field and discloses one method, router device, business network to establish business connection, which comprises the following steps: determining this point to neighbor point transfer probability according to the linkage biological information concentration of each points and selecting router with maximum probability as this operation to next point router with its neighbor points as this point neighbor points.

The invention discloses a logistic distribution method based on a cloud computing platform, which applies an ant colony optimization algorithm to logistic distribution path planning, realizes parallelization processing of the ant colony optimization algorithm based on the hadoop cloud computing platform, and realizes large-scale logistic distribution. According to the method, the ant colony optimization algorithm is realized by an HDFS (Hadoop Distributed File System) of the hadoop platform and a MapReduce parallel programming frame, the ant colony optimization algorithm is parallelized by MapReduce, each iteration time is accelerated, the HDFS is used for storing parameters and intermediate files, the parameter read and update rate of the ant colony optimization algorithm is increased due to the high efficiency and high reliability of the HDFS, and a path search progress is accelerated.

The invention discloses an anti-colony-algorithm-based distribution network fault positioning method. The method comprises: an anti colony algorithm evaluation function is constructed; and fault positioning is carried out by using an anti colony algorithm. When the anti colony algorithm is used for carrying out fault positioning, the evaluation function serves as a basis of an evaluation solution performance; and the good performance expresses the selected path is short. When equipment with the fault in the distribution network is diagnosed, a hypothesis is found and all uploaded RTU or FTU information can be explained well; and thus deviation of the corresponding FTU or RTU information and actual uploaded information is minimized by finding the hypothesis, thereby realizing fault positioning of the distribution network.

The invention relates to the technical field of logistics distribution, and discloses a logistics route planning method, device and equipment and a storage medium, which are used for realizing the maximum loading distribution of vehicles, improving the matching degree of people and vehicles and the efficiency of collecting and distributing parcels, and reducing the logistics distribution cost of asingle salesman. The logistics route planning method comprises the following stepsoftraining the logistics historical processing information classified according to the salesmen through an ant colonyalgorithm; obtaining a path optimization model; in the path planning process, extracting corresponding express order information, delivery addresses and deliverable vehicle information from express information to be processed by salesmen, calculating matching parameters between the salesmen and the vehicle information by utilizing thepath optimization model by taking the information as constraintconditions of a planned path, and planning an optimal path on the basis of the matching parameters.

The invention provides a characteristic wavelength selecting method for a near infrared spectrum in an ant colony optimization algorithm. The method comprises the following steps: creating a partial least squares analysis model by utilizing all wavelength points of the near infrared spectrum as initial selection equivalent variables of the ant colony optimization algorithm and utilizing the quality or characteristics of a test object as reference standard, re-weighing and calculating to update a pheromone vector according to the predicated mean square error of the model, searching and obtaining the optimal near infrared spectrum wavelength combination through using iterative computations, performing circular computation for a plurality of times, and automatically judging so as to obtain the optimal characteristic wavelength of the near infrared spectrum. The characteristic wavelength selecting method provided by the invention adopts the global search and positive feedback mechanisms of the ant colony optimization algorithm so as to effectively avoid the defects of subjective wavelength selection in the modeling process, so that the model has strong robustness and applicability.

The invention relates to an ant colony optimization algorithm-based SBS (service-based software system) resource allocation method in a cloud environment. The method comprises the following steps of at first, acquiring a resource candidate resource allocation set of each component service in an SBS, acquiring a mapping relation between any resource attribute vector and component service average response time thereof, building a search map of SBS resource allocation application, and adjusting the resource candidate resource allocation set of each component service in the SBS by using a colony optimization algorithm to obtain the optimal combined resource allocation; finally, performing resource allocation on the SBS according to the optimal combined resource allocation. By the SBS resource allocation method, the resource using cost can be minimized under the condition that the allocation satisfies an SLA (service level agreement) constraint, so that the profit of a service provider is increased; when the scale of the SBS resource allocation is relatively large, the combined resource allocation approximate to optimal allocation can be guaranteed obtained by the method within relatively short time.

The invention discloses a logistics dispatching intelligent distribution method, device and equipment and a storage medium, and the method comprises the steps: carrying out the preprocessing of the business data of logistics dispatching, and obtaining a dispatching feature; constructing a freight scheduling intelligent distribution model for guiding logistics distribution work according to the scheduling features and the logistics scheduling constraint conditions; calculating and analyzing each process link and path, solving the freight scheduling intelligent distribution model by utilizing anant colony algorithm, and checking a solving result; and obtaining an optimal solution of the operation route by optimizing the operation parameters with the goals of the maximum contract number delivered by successful stowage on time, the minimum total mileage of all vehicles and the minimum number of used vehicles as goals according to the verification result. In this way, unnecessary repeatedtransportation can be reduced, the single-vehicle full-load rate is increased, reasonable distribution is achieved, the transportation frequency is reduced to the maximum extent, the transportation cost is saved, the professional capacity of cargo stowage optimization and on-time distribution is improved, and therefore the operation efficiency of a logistics system is improved.

The invention discloses a structural damage identification method which combines multi-objective ant lion optimization and sparse regularization. The method comprises the following steps: a finite element model is established according to structural design parameters, modal parameters such as natural frequency and vibration mode of the structure are extracted; according to the principle of model updating, the objective function is established by using the relative frequency variation of the damaged structure and the calculated structure as well as the modal confidence criterion; the objectivefunction is optimized by using weighting strategy and trace sparse regularization; multi-objective ant lion optimization algorithm is used to optimize the objective function until the iterative termination condition is reached; finally, the optimal solution is the result of damage identification. At the same time, the invention optimize a plurality of objective functions, more accurately search for an optimal solution, and introduces a trace sparse regularization and weighting strategy to respectively improve noise robustness and identification accuracy, reduce the influence of noise and damage sensitivity of measurement response on identification result, and has better noise robustness and higher identification accuracy.

The invention discloses a method for optimizing fault diagnosis rules based on an ant colony optimization algorithm, applied to intelligent fault diagnosis. In the invention, the ant colony optimization algorithm is adopted, the fault diagnosis rules (namely a fault pattern sample data vector) in a system fault characteristic pattern sample library are reduced and optimized by reducing the length of the fault pattern sample data vector, redundant condition entries in the fault diagnosis rules are eliminated, and optimal diagnosis rules with fewer condition entries and higher fault diagnosis accuracy are obtained, thus the accuracy rate for diagnosing the type of a fault in a diagnosis field can be improved. The invention also discloses a method for reducing the fault diagnosis rules.

The application provides a method and system for controlling flight based on a cellular network. The method comprises the following steps: receiving flight waypoint coordinate information, neighboringpoint range information, and communication base station location information; connecting the aircraft through an enhanced machine type communication cellular network, and obtaining the coverage of the enhanced machine type communication cellular network by utilizing the channel gain relationship of signal transmissions between the aircraft and the communication base station; determining the number of neighboring points at each point in the region according to the neighboring point range, obtaining distances between the neighboring points by the neighboring point range information, obtaining the minimum distance between regular waypoints by using the minimum distance cycle evolution; and obtaining the minimum distance when the aircraft traversing the coordinates of all flight waypoints, the flight order of flight points and the order of flight paths by using the artificial ant colony algorithm and the minimum distance between regular waypoints to control the flight of the aircraft. Themethod and system realize the minimum flight distance of the aircraft and improve the control efficiency of the flight.

The invention discloses a wireless sensor network non-uniform clustering routing method based on energy optimization. The method is mainly divided into two stages: a cluster initial establishment stage and a cluster stable transmission stage. The cluster initial establishment stage mainly solves the problems of cluster head election, cluster radius calculation and cluster formation. The cluster stable transmission stage comprises pheromone initialization, cluster head node adjacency list updating, transition probability formula improvement and pheromone updating, and a next hop cluster head node is effectively selected and a global optimal inter-cluster route is established based on a chaotic ant colony optimization algorithm. According to the invention, the network service life and the node energy efficiency are obviously improved.

An electric power system measurement and load parameter analysis and identification method relates to an electric power system parameter identification method, and comprises the following steps: firstly, clustering and analyzing loads according to load measurement information, and determining a load recording device installation place; secondly, constructing a comprehensive load model of the parallel static characteristic load of the motor, and establishing an identification target function; performing parameter identification by adopting a hybrid optimization algorithm combining an ant colonyalgorithm and a gradient algorithm through measured data of a load recording device; optimizing parameters on multiple identification results by utilizing unbiased and optimal estimation characteristics of a Kriging algorithm; and finally, counting load models of different stations to form a load model parameter library. The speed of parameter identification is increased, parameter optimization is realized for multiple identification results by adopting a Kriging algorithm, and the precision of parameter identification is improved. The method can provide a model basis for research in the fields of electric power special planning, electric power system stability analysis, electric power system energy efficiency evaluation and the like.

The invention discloses a 3D color point cloud registration method based on global optimization and multi-constraint condition iteration. The method comprises: firstly, effectively eliminating noise,outliers and outlier clusters by adopting a multi-state outlier algorithm based on marks; secondly, using a PSO algorithm for carrying out preliminary coarse registration processing; thirdly, using anant colony optimization algorithm to carry out global optimization processing; and finally, using a global multi-constraint condition iterative closest point precise registration algorithm to carry out precise registration processing. In the coarse registration process, the result is globally optimized by using an ant colony optimization algorithm, so that error pairing of coarse registration isreduced, the coarse registration precision is improved, an initial value with higher precision is initialized for subsequent fine registration processing, and the whole registration time is further shortened; in the fine registration process, ICP iterative fine registration processing is carried out by adopting Euclidean distance and curvature double constraint conditions between corresponding points, so that the registration precision is improved to a certain extent.

The invention relates to a method for optimizing combustion of a biomass furnace. At present, the online optimization can not be carried out according to the real-time change condition of the combustion of the biomass furnace. The method comprises the steps of: firstly collecting operating parameters of the biomass furnace and relevant characteristic indexes characterizing a combustion state of the biomass furnace, and establishing a database; secondly, selecting data as modeling data aiming at the given biomass furnace, wherein the data comprises conditions of feeding speeds of different biomass fuels; modeling aiming at the modeling data by adopting a least square support vector machine method, establishing a model between the characteristic indexes of the combusting state of the biomass furnace and each operating parameter of the furnace, and by using an ant colony optimization algorithm and combining with the established model, optimizing the configuration of the combusting parameters of the biomass furnace aiming at biomass fuel corresponding to the model and combusting characteristic indexes or index combination of combusting states of different biomass furnaces. According to the method provided by the invention, the optimizing efficiency and the comprehensiveness of the biomass furnace can be effectively improved, so that off-line optimization can be implemented and online real-time combustion optimization is also carried out.

The invention discloses a hybrid energy storage system optimal configuration method for improving comprehensive performance of a wind power plant. According to the provided optical configuration method, effective on-grid electric quantity benefit can be increased according to a cost model of an energy storage system and AGC service gain of the wind power plant; the service market can be assisted through divided period parameters of the wind power plant and the AGC; the effective power generation on-grid rate can be improved by improving the output fluctuation value of the wind power plant; themaximum comprehensive benefit of the whole wind power plant after the hybrid energy storage system is added is taken into consideration, so that the rated power and the capacity of the hybrid energystorage system are optimally configured. By combination of the electric power system AGC auxiliary service assessment reward rule, the wind power plant accessed power grid on-grid assessment rule, andthe step electricity price and the characteristics of the hybrid energy storage system, the comprehensive performance improvement and comprehensive benefit model of the wind power plant added with the hybrid energy storage system is built, and then the optimal economic configuration of the hybrid energy storage system is finally realized by adopting a chaotic ant colony optimization algorithm.

The invention belongs to the field of robots and particularly relates to a mobile intelligent tracking vehicle which comprises an upper computer (1), a lower computer (2), a vehicle body (3), a cradlehead (4), a camera (5), a voice module (6), a cradle head driver (7), a wheel driver (8) and a laser ranging module (9). The path map of a workplace is stored in the upper computer (1). An ant colonyalgorithm is used to plan a walking path. The upper computer (1) collects a current scene in real time by using the camera (5) and judges a current coordinate position in real time. A planned path iscompared, and the vehicle reaches a designated location by tracing. The mobile intelligent tracking vehicle can be used as a material delivery vehicle of a large factory workshop or a logistics distribution place.

The invention discloses a placement method for virtual machines in a cloud data center based on an ant colony optimization algorithm. A VMP (Virtual Machine Placement) problem is solved by use of theant colony optimization algorithm; when a virtual machine request reaches, the placement method for the virtual machines is found so that a total network bandwidth required by communications between the virtual machines is reduced while minimizing the total energy consumption of the cloud data center. The invention has the main characteristic that direct information exchange is performed between aplacement order of generation virtual machines and ants. The invention has the technical effect that a virtual machine deployment placement scheme which satisfies practical deployment requirements iscalculated with the minimum energy consumption as an optimization objective by use of the ant colony optimization algorithm on a given network topology. Simulation experiments and data analysis showthat, compared with a first fit decreasing algorithm, the ant colony optimization algorithm provided by the invention has obvious advantages on algorithm performances, the obtained deployment scheme of the virtual machines is capable of obviously reducing the total energy consumption of the cloud data center, and the feasibility and the advantages of the placement method for the virtual machines in the cloud data center based on the ant colony optimization algorithm are proven.

The invention relates to a production scheduling optimization method for a beer saccharification process, wherein beer enterprises are not ideal in scheduling effect because of formulating production scheduling schemes by virtue of artificial experience presently. The production scheduling optimization method disclosed by the invention comprises the following steps of: establishing a production scheduling mathematical model on the basis of a technological process mechanism and first, and then converting to a discrete form capable of solving by virtue of an ant colony optimization algorithm, and finally determining a production scheduling method for the beer saccharification process. The invention discloses a saccharification production scheduling method with a high global optimization capacity aiming at some problems in optimized scheduling for beer saccharification production, and the optimization method has the characteristics of openness, robustness, parallelism, global convergence, and no special requirements on problematic mathematical forms, and the like.

The invention discloses a multilayer network-oriented multi-QoS (Quality of Service) route constrained ant colony optimization algorithm. According to the ant colony optimization algorithm, the main improvement comprises the fact that for routes of source-destination nodes, when search is carried out, the routes between first-level nodes are searched through adoption of a hop count least routing method; for second-level nodes in the first-level nodes, the search is carried out through call of the ant colony optimization algorithm in each first-level node; and the shortest path searched in eachfirst-level node is connected and expanded as a complete routing path. According to the ant colony optimization algorithm, heuristic factors in the ant colony optimization algorithm are modified, delay, a bandwidth utilization rate and a loss rate are expressed as comprehensive values of various indexes, and the comprehensive values are recorded as cost. The ant colony optimization algorithm is applicable to route search of a multilayer network. A plurality of constraint conditions are taken into consideration. A computing process of the algorithm is enabled to be relatively reasonable.

The present invention discloses a flexible cable path intelligent planning method considering gravity constraint rules. The method comprises the following steps: carrying out modeling on the three-dimensional environment of the wiring space, and using the partitioned space division method to carry out partition division on the overall space of the product so as to obtain a to-be-wired space area that needs to be wired and a non-wiring space area; carrying out grid processing on the divided to-be-wired space area, establishing an obstacle bounding box model, and establishing a wiring space grid model and an abstract model of the wiring path point; and finally, carrying out intelligent search and optimization on the flexible cable path by using the ant colony optimization algorithm and by considering the gravity constraint rules. According to the method disclosed by the present invention, automatic obstacle avoidance can be realized, the wiring route satisfying certain engineering rules is generated, and the wiring efficiency of the flexible cable and the final overall performance and reliability of the product are improved.

The invention belongs to the field of cognitive wireless network technology, and discloses an agent-based intrusion detection method for a cognitive wireless network. The method comprises the following steps: selecting a node placement agent; determining whether the channel energy is greater than a threshold; detecting an internal node; performing attack detection; performing cooperative detection; and giving a response after the intrusion is detected. According to the agent-based intrusion detection method disclosed by the invention, attacks in a distributed cognitive wireless network are detected by using an agent-based intrusion detection system, the shortcoming of only performing single attack detection in existing security schemes can be overcome, and the overall security and robustness of the cognitive wireless network can be realized; and during detection, the attacks that occur inside the network can be detected through the coordination of multiple agents by using an ant colony optimization algorithm, the shortcomings that the computing power of secondary users is weak and a single agent cannot detect all known attacks can be overcome, and the high efficiency of the detection can be guaranteed.

The invention discloses an Internet of Things intelligent service system based on Kaa Project and an implementation method thereof. The implementation method of the Internet of Things intelligent service system based on the Kaa Project uses a complex business scenario in a ubiquitous environment as the driving, uses a resource representation model facing the Internet of Things business, a multi-terminal aggregation algorithm facing the Internet of Things business, and a heterogeneous network virtualization technology as supports, so as to construct the Internet of Things intelligent service system which faces all walks of life and breaks the isolation of "information islands" in a software-defined way based on a Kaa Project Internet of Things middleware platform, which can realize cross-industry, cross-platform information sharing. The system constructed by the implementation method of the Internet of Things intelligent service system based on the Kaa Project dynamically integrates heterogeneous terminal resources and network resources according to business requirements, and uses ant colony optimization algorithm to jointly optimize multi-dimensional resources; therefore, resourcesharing among different industries and departments is realized, and the future diversified development needs of the Internet of Things can be met.

The invention discloses a grading method used for an urban rail transit passenger flow congestion index, and relates to the field of urban rail transit operation management. The method comprises the steps of performing cluster analysis based on a rail transit passenger flow congestion degree big data sample value; performing optimization solving by adopting an ant colony optimization algorithm, and obtaining an optimal number of clusters and a key value of grading by adjusting an ant number and a pheromone evaporation rate; and therefore achieving the purpose of safely grading the urban rail transit passenger flow congestion index.