1977 results about "Ant colony" patented technology

The invention discloses a mobile robot path planning method based on an improvement of an ant colony algorithm and particle swarm optimization. The method mainly solves the problems that in the prior art, the operating speed of an algorithm is low, and frequency of turning of an optimized path is high. The planning method includes the steps that modeling is carried out on a work environment of a robot; the particle swarm optimization is utilized to quickly carry out path planning, pheromones more than those around an obtained path are scattered on the obtained path, and guiding is provided for an ant colony; an ant colony algorithm optimized by the principle of inertia is adopted, and optimization is conducted on the basis of the particle swarm optimization; the motion path of the robot is output according to an optimization result. According to the planning method, comprehensive consideration is given to stability and robustness of the algorithm, iterations can be effectively reduced, searching efficiency is improved, the path length is shortened, the frequency of turning is reduced, path quality is substantially improved, and the planning method accords with an artificial planning intention and is suitable for autonomous navigation of various mobile robots in a static environment.

The invention discloses a routing method for the wireless sensor network with efficient energy, which is suitable for the layered sensor network structure. The routing method is composed of initialization, cluster building, adjacent clusters routing and routing maintenance, wherein, an initialization process of the protocol makes a Sink node obtain a topology and network average energy of the sensor network, and each node obtains hop counts from the node to the Sink node; in the stage of the cluster building, a repeated division method is used to divide sensor network clusters, the divided clusters are even, and a leader cluster node is undertaken by nodes with higher residual energy; the adjacent clusters routing uses an ant colony algorithm to determine the probability of using a link to send information according to the link pheromone concentration, and the link pheromone concentration is increased with the information transmission on the link and is reduced with the time going; and the routing maintenance stage is responsible for updating link pheromone concentration, and makes the nodes inside the cluster with higher residual energy undertake the leader cluster in turn. The routing method can reduce the consumption of the network total energy, can balance the consumption of the node energy and can prolong the network life cycle.

The invention discloses a collaborative flight path intelligent planning method for formation flying of unmanned planes under a dynamic environment. The method comprises the steps of offline intelligent planning of a formation pre-flying collaborative flight path of the unmanned planes, online replanning of the flight path for avoiding threats, collaborative rebuilding of a formation team and the like. The offline intelligent planning of the formation pre-flying collaborative flight path of the unmanned planes adopts an intelligent planning method based on a Voronoi graph and an ant colony algorithm, and an integrated optimal pre-flying flight path with collaborative time can be planned off line for the unmanned plane. The replanning of the online flight path adopts an intelligent flight path planning method based on an RRT (Rail Rapid Transit) algorithm, and quick flight path correction can be provided when the unmanned plane formation meets a sudden threat. The collaborative rebuilding of the formation team adopts a method combining unmanned plane formation member flying speed adjustment and coiling maneuvering. According to the method, pre-flying collaborative flight path offline planning and generation of online replanned flight path are provided for the unmanned plane formation, a collaborative rebuilding scheme is provided, and the online rebuilding problem of unmanned plane formation flying cooperativity is solved.

The invention discloses a heterogeneous multi-UAV (Unmanned Aerial Vehicle) cooperative scouting and striking task planning method, and belongs to the technical field of UAV task planning. The method comprises the steps of firstly establishing a task planning model which takes shortest UAV task performing flight and maximum target value as an objective function; and then solving the task planning model by using a heterogeneous multi-population ant colony algorithm so as to acquire an optimal task allocation plan which conforms a task completion time constraint, a UAV task type and capacity constraint and a condition that each task is only performed for one time. The method disclosed by the invention effectively solves a multi-UAV multi-task allocation problem under complex constraint conditions, can satisfy a task sequence proposed in order to improving the combat efficiency while meeting a sequential relation of different types of tasks, and has good flexibility in use.

The invention provides a global path planning method of a moving robot under a dynamic and complicated environment. The method includes the steps of building a global environment map according to an actual environment, building a dynamic barrier environment, obtaining a grid map by means of a grid method, converting a barrier distribution map obtained in the grid method into an empowered adjacent matrix of the map, carrying out global path planning on the environment by means of an ant colony algorithm, processing trap problems in the environment in a regressing method, judging whether a current position of a path is a target point, repeating the preceding steps if the answer is negative, and ending if the current position is the designated target point. The method is simple and easy to achieve, and the path planning effect is good.

Provided in the invention is a task scheduling method based on heredity and ant colony in a cloud computing environment. The method comprises the following methods: S1, initializing population; S2, selecting individuals according to a wheel disc type selection strategy; S3, carrying out crossover operation on the individuals according to crossover probability and carrying out reversion mutation operation according to a mutation probability so as to generate a new colony; S4, updating the new generated colony; S5, determining whether a dynamic fusion condition is met; S6, initializing ant pheromone by using an optimal solution found by heredity; S7, calculating probabilities of moving to next nodes by all ants and moving all the ants to the next nodes according to the probabilities; S8, enabling M ants to travelling N resource nodes and carrying out pheromone updating on an optimal ant cycle; S9, carrying out pheromone updating on all paths; and S10, determining whether an ant end condition is met and outputting an optimal solution. According to the invention, respective advantages of a genetic algorithm and an ant colony algorithm are drawn and respective defects are overcome; and on the basis of dynamic fusion of the two algorithms, time and efficiency of exact solution solving are both considered.

The invention discloses a method for solving a multiple-depot logistics transportation vehicle routing problem. The method comprises steps as follows: inputting multiple-depot problem basic parameters based on real-time traffic information, establishing a multiple-depot logistics transportation scheduling mathematic model based on the real-time traffic information, adopting a clustering analysis method, introducing the particle swarm optimization algorithm to adjust and optimize ant colony algorithm pheromones, optimizing ant colony algorithm heuristic factors with the particle swarm optimization algorithm, solving an optimal distribution route, and establishing a mathematic model according to the multiple-depot logistics transportation vehicle routing problem based on the real-time traffic information; taking distances between clients and parking lots as main factors, performing area division on the clients and the parking lots with the clustering analysis method, and converting a multiple-depot problem into a single-depot problem; introducing the particle swarm optimization algorithm to improve the ant colony algorithm to solve the model. The method has the better global and local optimization capacity and has higher efficiency and stability when solving the multiple-depot problem.

The invention relates to a method for planning a logistics distribution path and discloses a method for selecting a multi-user and multi-warehouse logistics distribution path. The method comprises the main steps of initializing an ant colony optimization method, setting up the path, updating information elements, initializing a taboo search optimization method, setting up a neighborhood path set, evaluating the neighborhood path set, updating the path, and updating a taboo list. According to the method, firstly the ant colony optimization method is utilized for obtaining the alternative scheme of the distribution path, then the distribution path is used as the initial path of the taboo search to conduct further optimization, the ant colony optimization technology is one of colony intelligent optimization technologies, a person is good at finding the area where the optimal path possibly exists, the taboo search technology belongs to a locus method, two processing technologies are mixed, therefore, respective advantages can be fully utilized, and the search performance of the method is improved. The method for selecting the multi-user and multi-warehouse logistics distribution path overcomes the defects in an existing path distribution optimization method and is more suitable to path optimization processing of multi-user and multi-warehouse logistics distribution.

The invention discloses an optimization method of a network variable structure with a distributed type power supply distribution system. The optimization method includes that basic static data of electric power network structure parameters, distributed type power supply distribution, load capacity, faulty lines and the like are extracted, under the condition of present line fault, the basic static data are utilized to perform islanding scheme calculation and correction, survivability indexes constructed by the method are combined to perform an online evaluation for islanding network safe operation performances and implement immediate control measures, finally, on account of residual network structure data after final islanding electric power network division is performed, by means of an ant colony algorithm, a reconfiguration optimal computation is performed for residual network with minimum network loss as a target, and finally the electric power network structure with high safe operation level is obtained. On the basis of the developed algorism and function modules, the invention further provides a large-scale power distribution network intelligent optimization decision system based on multi-stage computation correction and survivability evaluation, according to the technical scheme, the division and reconstruction of the network with the generalized model distributed type power supply distribution system are achieved, and accurate reference of network structure adjustment, schedule and operation can be excellently provided for regional electric power network schedule staffs.

The invention relates to a method for planning robot paths on the basis of path expansion ant colony algorithms. The method has the advantages that the ant colony algorithms are applied to the field of robot path planning, path expansion ant colony algorithm optimization strategies are proposed, the robot path optimizing efficiency can be optimized, information element distribution time-varying characteristics, information element updating strategies, path location inflection point optimization and local optimal path expansion are introduced, and location inflection point parameters and general evaluation are additionally used as evaluation standards for the paths; as verified by simulation analysis and practical experiments on the three algorithms, the method is high in robot path planning and searching capacity on the basis of the path expansion ant colony algorithm optimization strategies and is high in algorithm efficiency, and the found paths are short; phenomena that the algorithms run into local optimization can be effectively inhibited, the optimal paths of robots can be searched, and the robots can quickly avoid obstacles to safely arrive at target points.

The invention discloses a self-organizing method for a cooperative scouting and hitting task of a heterogeneous multi-unmanned-aerial-vehicle system. A heterogeneous multi-unmanned-aerial-vehicle system is decomposed into two isomorphic sub systems and a corresponding cooperative way between the two sub systems is also designed; and the two sub systems carry out task planning respectively and also carry out mutual cooperation. For an isomorphic reconnaissance type unmanned aerial vehicle system and an isomorphic scouting and hitting unmanned aerial vehicle system, a cooperative searching task self-organizing method and a cooperative scouting and hitting autonomous task planning method are designed respectively. According to the cooperative searching task self-organizing method, problem decomposition is carried out by using a method based on distributed model prediction control; and then solution is carried out by using a particle swarm algorithm. And according to the cooperative scouting and hitting autonomous task planning method, a normal flight mode without any detection of a threat and a threat avoiding mode with threat detection are designed during a local problem construction process of each unmanned aerial vehicle based on a distributed ant colony algorithm. Therefore, an on-line requirement can be met well.

The invention provides an ant colony optimization computing resource distribution method based on a cloud computing environment. The computing resource distribution method is based on ant colony optimization and characteristics of the cloud computing environment. The cloud computing resource distribution method comprises the steps of predicting computing quality of potential available nodes, analyzing influence of factors such as network bandwidth occupation, quality of a track, responding time, task cost and reliability on resource distribution according to characteristics of a cloud computing service mode, and then obtaining a set of optimized computing resources by means of the ant colony algorithm. According to the algorithm, shorter responding time and better running quality can be acquired compared with other distribution algorithms which aim at the network on the premise that cloud computing environment requirements are met, and therefore the ant colony optimization computing resource distribution method based on the cloud computing environment is more suitable for the cloud environment.

The invention provides a cloud data center task scheduling method based on an improved ant colony algorithm, and relates to the field of cloud computing. The method comprises the following steps that (1) a to-be-scheduled workflow task set submitted by a user and a virtual machine set rent by the user are input, (2) a scheduling problem that tasks are allocated to virtual machines to be executed is represented as a standard minimum value solving problem, and (3) the virtual machine task scheduling problem in a cloud computing environment is solved through the ant colony algorithm based on information element updating. The method can adapt to the dynamic nature of the cloud environment, time for task scheduling of a user is shortened, and virtual machine load in a cloud data center can be maintained in a relative balance state.

The invention belongs to the technical field of path or flight path planning of robots as well as low-altitude flight aircrafts, specifically relates to a path planning method of a passable area divided at unequal distance, and is used for solving the problem that existing planning algorithm has large time complexity in time and space complexity. The path planning method comprises the following steps of: calculating convex extreme points of each barrier curve; dividing the passable area by using each convex extreme point as a horizontal line; abstracting each small area obtained by dividing into a peak of a graph; forming an undirected graph by all peaks; finding out a peak serial number corresponding to the small area at which a starting point and a final point are located; finding out all paths for the undirected graph by breadth-first or depth-first scanning; finding out an actual to-be-travelled path of a moving object according to the situation on an actual map. The path planning method disclosed by the invention has the beneficial effect of overcoming the problems of algorithms of A* and the like on memory space and operation time, and overcoming a convergence problem of an ant colony algorithm at the same time. Besides, time complexity and space complexity are improved greatly in comparison with other algorithms.

The invention discloses a parking system path planning method based on an improved ant colony algorithm, and aims at solving the problem of AGV vehicle access path planning in an intelligent parking garage so that vehicle accessing can be completed in the shortest possible time, utilization rate of parking places can be enhanced, time of waiting for vehicle accessing can be reduced for social members and automatic management of parking equipment can be realized. The concrete planning steps are that an AGV working environment model in the intelligent parking garage is created by adopting a grid method; the conventional ant colony algorithm is optimized and improved by introducing of new node state transfer probability and an updating strategy of combination of local and global pheromones; and simulated testing is performed on the AGV vehicle access path planning process by applying the improved ant colony algorithm and the result is outputted. The method has high global search capability and great convergence performance, and can effectively enhance path search efficiency, shorten search path length and reduce the number of path turnings and can also enable the AGV to effectively avoid obstacles in the complex operation environment so as to search the optimal collision-free path.

The invention discloses a many-to-many computation migration method and system of the self-organizing cloud. A central scheduling manager collects and manages task requests sent from node devices, schedules task via a genetic ant colony task scheduling algorithm regularly, and returns a task scheduling result to a sound node device; the source node device receives the task scheduling result and then migrates the tasks to a target node device; and the target node device executes the tasks according to a task queue, and sends task output data back to the source node device after that the tasks are completed. According to the invention, the task scheduling problem in the peer-to-peer self-organizing cloud network environment is researched, a task scheduling model and a task scheduling strategy are described, and the task scheduling algorithm based on the genetic ant colony algorithm is designed for optimization objects as minimizing the average task execution time length, load balancing and minimizing average task execution energy consumption.

The invention relates to wireless self organized network energy saving needed route method based on ant colony optimization. The route building includes the following steps: broadcasting state information of asking ant to search network and store in each node reverse information pheromone list; responding the anti while receiving the request and selecting one neighbor node as the next step reverse source node; building the route from the source node to destination node. The route maintenance is that energy parameter is inducted; and new route finding process will be forced to do to avoid node from dying untimely while node energy consumption is overmuch.

The invention discloses a maximum-minimum ant colony optimization method and a maximum-minimum ant colony optimization system for solving a vehicle scheduling problem. The maximum-minimum ant colony optimization method comprises the following steps of acquiring address information of a customer according to delivery information in an order ticket; reading relevant information in a maximum-minimum ant colony algorithm; and performing initialization, condition termination judgment, path establishment, path improvement and information updating on the ant colony algorithm. The numerical value of pheromone polatility is changed dynamically, convergence is accelerated, a plurality of paths are searched, global searching capability of the algorithm is improved, and premature and stagnation are avoided. The maximum-minimum ant colony optimization method has the advantages that the method is easy to implement and the rapid convergence capability and the rapid searching capability are high when the method is used for solving a vehicle path problem.

The invention relates to a method for optimizing multi-star multitask observation dispatching under a complicated constraint condition and belongs to the technical field of deep space detection. The method is realized by virtue of designing an improved ant colony algorithm; a satellite resource rotating around the earth for one track loop is represented by a satellite, and an ant colony system is formed by all satellite resources in the track loop; in combination of observation restriction, energy demand predication and capacity demand predication are introduced to control the transfer possibility. Restrictions such as time, energy and storage capability are considered in state transition rules, observation dispatching tasks have priority levels, and the method is conductive to improvement of the data collection capability and application level in real satellite task dispatching; the improved ant colony algorithm can be used for converging in a practicable iteration range so as to obtain better solutions.

The invention relates to an AGV optimization scheduling method based on mixed particle swarm optimization. First of all, a mathematics model is abstracted from the work process of an AGV, and an object function of a scheduling scheme is determined, and secondly, the model is solved by use of the mixed particle swarm optimization based on a genetic algorithm, a stimulated annealing algorithm and ant colony optimization, and an optimization scheduling scheme is generated. According to the invention, a contrast analysis is made between the mixed particle swarm optimization and standard particle swarm optimization through examples, and the variation operation of the mixed optimization employs an ant colony optimization thinking mode, ensures intersection of individual best and group best in an intersection operation process, ensures the feasibility of the mixed particle swarm optimization and has the validity for solving large-scale scheduling tasks.

The present invention discloses a parking system path planning method on the basis of an improved ant colony algorithm. The method comprises: creating an AGV operation environment model through adoption of a link visible graph; planning the initial path of the AGV from an origin to a terminal point based on a Dijkstra algorithm; performing optimization improvement of the ant colony algorithm through introduce of a node random selection mechanism and a maximin ant system and changing of a sociohormone update mode; and selecting the improved ant colony algorithm to optimize the initial path, and completing the parking system path planning method. The parking system path planning method on the basis of an improved ant colony algorithm is able to allow an AGV to effectively avoid a barrier and then find out an optimal path through fusion of an ant colony algorithm; and moreover, a mixed algorithm shows up a high global searching ability and a good convergence, so that the path search efficiency is improved, the search path length is shortened, the search path quality is improved, the parking land occupation area is small, and the purposes of large number of effective parking and the intelligence are achieved.

The invention discloses a method for path planning based on an improved ant colony algorithm. Compared with the classical ant colony algorithm, the method has the following improvements: (1) a constant pheromone evaporation coefficient is adjusted to be an adaptive pheromone evaporation coefficient, and the size of the coefficient is changed adaptively along with increase in number of iterations of an ant colony method; a local optimum path is preferentially selected by adopting a rule that the number of inflection points is small on the basis that different paths are identical in length; (3) a path simplifying rule is adopted for the local optimum path, whether each passing node in the path and the starting node are neighboring nodes or not is judged, and redundant nodes on the path are eliminated; and (4) a pre-sorting rule is adopted when pheromone updating is performed on the path passing by an ant colony before, and only the top 1 / 3 of paths in path length sorting are updated. According to the above improvements, the method can effectively reduce the algorithm convergence time of the ant colony algorithm and improve the operating efficiency.

The invention discloses a hierarchical multi-source data fusion method for a pipeline linkage monitoring network, which comprises the following steps: carrying out data level preprocessing for various primary linkage detection signals acquired by a sensor at a common node of the monitoring network by using wavelet transformation, and extracting leakage-sensitive characteristic parameters; establishing a characteristic level data fusion model based on an ant colony neural network, processing the leakage characteristic parameters extracted by various sensors on the node, and constructing an elementary probability assignment function of evidence according to the output result of the ant colony neural network; and carrying out evidence synthesis at a cluster-head node according to an evidence combination rule, and making final decisions according to a maximum trust value method. The invention provides the hierarchical multi-source linkage detection data fusion method from the data level and characteristic level to decision level, and solves the multi-source data processing problem of the pipeline linkage monitoring network; and the method utilizes the linkage detection information acquired by various sensors in the network so as to effectively improve the accuracy rate of leakage identification.

The invention discloses an AGV (Automated Guided Vehicle) route planning method and system based on an ant colony algorithm and multi-intelligent agent Q learning, improving the global optimization ability, realizing a case that an AGV learns how to avoid an obstacle in the interaction process by introducing the multi-intelligent agent Q learning into a route planning research of the AGV, and canplay independence and learning capacity of the AGV better. The AGV route planning method and system is characterized in that according to a static environment, carrying out modeling on an AGV operation environment by utilizing a grid method, and setting an initial point and a target point; according to coordinates of the initial point and the target point of the AGV, generating a global optimal route by the ant colony algorithm; enabling the AGV to move towards the target point according to the global optimal route, and when detecting that a dynamic obstacle exists in a minimum distance, carrying out selection of an obstacle avoidance strategy by an environment state corresponding to the multi-intelligent agent Q learning so as to take a corresponding obstacle avoidance action, and after ending obstacle avoidance, returning to an original route to continuously move.

Apparatus, systems, and methods may operate to create a hypergraph of weighted vertices comprising computing resources and storage resources, and nets comprising workloads; to receive a plurality of requests to be addressed by a network associated with the hypergraph, at least some of the requests associated with data objects; to calculate partition schemes for the network based on the requests and the data objects according to an ant colony optimization heuristic; and to autonomously reallocate the workloads to the computing resources and / or the storage resources according to the partition schemes. The workloads may act as ants following a path defined by the vertices of the hypergraph. Further activities may thus include depositing pheromones along hyperedges of the hypergraph, wherein the hyperedges are used for swapping the vertices between the workloads. Additional apparatus, systems, and methods are disclosed.

The invention discloses a multi-mode intelligent configurable method for implementing optimization of a wireless network. Firstly, the network optimization demand and object are raised according to the user's own network and then the demand and object are analyzed and used as a basis for determining the network optimization mode and establishing a simple model of wireless network, and the network optimization plan and configuration network optimization parameters are prepared. Subsequently, the network is optimized by the cost functions (such as capacity, coverage and network quality) at five different angles (antenna, power, address, frequency and load balance) in combination with different optimization algorithms. The optimization algorithms include three heuristic algorithms (simulated annealing, particle bee colony and ant colony) and the conventional greedy algorithm, and increase the network performance to the ideal level. Finally, the optimization results of the wireless network are sorted to provide a network optimization plan to the user for reference and reality basis. The method is intelligent and configurable, can meet reasonable requirements of users, can also achieve the purpose of optimizing 2G / 3G dual-network coexistence, has strong flexibility, and provides a good reference for the present network performance.

The invention discloses a multi-constrained QoS (Quality of Service) routing strategy designing method for a software defined network. The multi-constrained QoS routing strategy designing method is implemented through three modules which comprise a traffic monitoring module, a QoS routing calculation module and a blocking traffic scheduling module, wherein the traffic monitoring module records bandwidth information, port load information and detailed information of issued traffic of a link; the QoS routing calculation module uses the bandwidth and port load information, which is acquired by the traffic monitoring module, of the link to act as a routing selecting indicator, and calculates an optimal path conforming to multiple constraint conditions by using an optimization algorithm; and a controller carries out precise control through the blocking traffic scheduling module when congestion occurs at a port. According to the invention, a path calculation function based on a multi-path ant colony algorithm in an SDN (software defined network) controller is realized; and meanwhile, by adopting the routing strategy designing method which combines routing calculation and blocking scheduling, the utilization rate of the link is effectively improved, and the network load is effectively balanced.

The invention relates to a mobile robot path planning method and system based on an improved ant colony algorithm. The mobile robot path planning method comprises the following steps of 1 environment modeling, 2 initial pheromone distribution and 3 optimal path searching and optimal path outputting. According to the mobile robot path planning method and system based on the improved ant colony algorithm, improvement is conducted on a previous traditional ant colony algorithm in the initial pheromone distribution aspect, ants can guide optimization of the ants at the beginning, and the early convergence speed is obviously increased; meanwhile initial parameters are reasonably selected, for instance, selection of pheromone evaluation, a result does not run into a locally optimal solution or has difficulty in forming an optimal solution, reasonable improvement is conducted on a pheromone update mode, the situation of running into the locally optimal solution can be effectively avoided, and the working efficiency and working reliability of the robot can be improved.

The invention discloses a method for finding an optimal path for an AODV (ad hoc on-demand distance vector) protocol in an Adhoc (self-organized) network based on an improved genetic-ant colony algorithm. Due to continuous changes of an Adhoc network topological structure, the performances of an existing routing protocol are very difficult to meet the needs of the network. In order to overcome the defects of being low in convergence rate, long in searching time, easy to get in locally optimal solution and incapable of reaching global optimum of a normal routing algorithm, the invention provides a method for finding an optimal path for an AODV protocol by taking the improved genetic-ant colony algorithm (IGAACA) as a core. The method comprises the following steps: firstly, finding a relatively optimal solution by utilizing global searching ability of a genetic algorithm; then, converting the relatively optimal solution into an initial information element of the colony algorithm; finally, adopting the advantage of quick converge of the colony algorithm, finding the routing global optimal solution. The algorithm can be adopted to quickly and effectively find the optical path, so that the network performances are improved.

The invention relates to a mobile robot path planning method and system based on a genetic ant colony algorithm. The mobile robot path planning method includes the steps that 1, modeling is conducted on the environment by establishing a coordinate system; 2, part of optimal solutions obtained through the genetic algorithm are converted to pheromones initial values of the ant colony algorithm; 3, optimum path search is conducted again through the ant colony algorithm, after optimum path search is ended, interlace operation is conducted on paths meeting the requirements of conditions, and the optimum path is finally obtained. The mobile robot path planning method and system overcome inevitable defects existing in a single ant colony algorithm, in other words, the ant colony algorithm is greater in blindness at the initial stage of search, the ant colony algorithm and the genetic algorithm are complementary in advantages, the search range of path search is shortened, and search efficiency of the optimum path is improved.