100 results about "Travelling salesman problem" patented technology

The invention discloses a data analysis-based automatic route programming method and a system thereof. The method comprises the following steps: obtaining user's interest point information, matching the user's interest point information with interest point information preserved in a database in advance, and outputting a coordinate list containing the interest point coordinate information; clustering interest points according to the coordinate list; carrying out travelling salesman problem solving for the interest points contained in each of clustered group results generated after the clustering and the coordinate list, and outputting a shortest path; calculating the ideal time consumption of the shortest path; calculating whole-course ideal consumption days according to preset play time every day, dividing, and determining the initial starting point and the initial ending point every day; selecting a residence place having a shortest distance to the initial ending point that day and the initial starting point the next day, and adding the residence place to the shortest path; and adding residence places to all the divided days until route programming is wholly completed.

The invention provides an unmanned aerial vehicle (UAV) flight path generation method and device, a storage medium and equipment. The method includes the steps of generating at least one ordered key angle of view subset based on a key angle of view set of landmarks; generating local flight paths of the landmarks using the ordered key angle of view subset; constructing and solving a generalized travel salesman problem based on the local flight paths to obtain at least one global flight path accessing at least one landmark; and generating an overall cost function based on a cost function of eachlocal flight path and a cost function of the global flight path, and selecting the local flight path and the global flight path to generate a UAV flight path according to the solving result of the overall cost function. According to the invention, to specify the order of key angles of view is unnecessary, and the difficulty of UAV control is reduced.

The invention discloses an electric power inspection robot path planning method based on a simulated annealing ant colony algorithm. The method includes the steps: building a map according to an outdoor transformer substation environment, planning the running path and stop points of an electric power inspection robot and building a topological map according to coordinate information of the stop points; acquiring the local shortest path of two optional inspection points by the aid of a classical Dijkstra algorithm according to inspection tasks, and building an undirected graph; transforming a global optimal path planning problem into a multi-target traveling salesman problem according to path lengths and the number of passing stop points, and planning a global optimal path on the basis of the undirected graph by the simulated annealing ant colony algorithm; replacing a local path in the global optimal path by the local shortest path to obtain a complete final path. The method is high inconvergence rate and global searching ability and has the advantages of low complexity, high feasibility and high speed.

Systems and methods to solve combinatorial problems employ a permutation network which may be modeled after a sorting network where comparators are replaced by switches that controllably determine whether inputs are swapped or are left unchanged at the outputs. A quantum processor may be used to generate permutations by the permutation network by mapping the state of each switch in the network to the state of a respective qubit in the quantum processor. In this way, a quantum computation may explore all possible permutations simultaneously to identify a permutation that satisfies at least one solution criterion. The Travelling Salesman Problem is discussed as an example of a combinatorial problem that may be solved using these systems and methods.

The present invention relates to image satellite planning, and more particularly to a method for allowing a deeper search for high value targets in a time-limited planning environment. In an exemplary embodiment, a method of computing an ordered subset of targets includes using an approximation for the time needed for the satellite to re-orient to a new target, rather than calculating each maneuver time between targets. By approximating the maneuver time rather than calculating it, the calculation time is reduced. Each iteration through the traveling salesman problem takes less time, and more iterations can be accomplished between imaging windows. The iterative process can search deeper into the traveling salesman problem to find a better solution.

The invention discloses a method for solving the traveling salesman problem based on an improved genetic algorithm. The steps include: aiming at the TSP problem, encoding the path using a decimal number string; calculating the total length, and then judging the total length; after encoding On the search space U of the decimal number string path, define the fitness function f(x), and define the population size n, the crossover probability Pc, the mutation probability Pm and the number of iterations T; in the search space U, randomly generate n individuals s1, s2, s3, ..., sn, constitute the initial population S0 = {s1, s2, s3, ..., sn}, set the current iteration number t = 0; according to the fitness function f(x), evaluate the individual fitness in the population , if t<T, then end the step, otherwise perform the genetic operation step; the individual with the highest fitness obtained through the genetic operation step is the optimal solution of the traveling salesman problem solving method. Based on the traditional genetic algorithm, the present invention optimizes the traveling salesman problem to achieve the purpose of improving the shortcoming that the algorithm is prone to premature convergence and optimizing the search efficiency.

The invention discloses an ant colony optimization-differential evolution fusion method for solving traveling salesman problems, which comprises the following steps: (1) algorithm parameters are initialized; (2) an ant colony is initialized; (3) a first iteration is carried out; (4) a mutation operation and an interlace operation are carried out to the pheromones of various squads from the second generation, so as to generate new pheromones; (5) the first squad is selected; (6) the ants of each squad establish the respective optimal path in accordance with the primitive pheromones; (7) the ants of each squad establish the respective optimal path in accordance with the new pheromones; (8) the two optimal paths are compared to pick out the pheromones with a better result of path optimization; (9) the pheromones of various ant squads are updated and passed down to the next generation; (10) the sixth step is carried out again until all squads finish the calculation; (11) the optimal path of the current generation and the length thereof are determined; (12) the fourth step is carried out again to carry out the calculation of the next generation until the termination condition is met; and (13) the whole optimal path and the length thereof are determined. The method has better astringency and stronger global optimization capability and is an effective way to solve the large-scale and complicated optimization problems such as traveling salesman problems, etc.

The invention discloses a method for arranging heat in a steel making continuous casting production process, and the method comprises the following steps sequentially: establishing a local database and selecting plate blanks from a pool according to the contract; selecting a furnace plate blank to be arranged from the pool and establishing a heat planning model; converting the heat planning model into a prospective traveling salesman problem; solving a heat planning prospective travel business problem model based on an intelligent optimization algorithm and finding an optimal heat plan; and arranging the plate blanks into the furnace as a main character according to the determined optimal heat plan. By using the method for arranging heat, the heat arranging plan is arranged without pre-determining the time of heat to be arranged. By using the invention, one plan is arranged in several seconds, the time can be precise to second, the utilization rate of the equipment is greatly improved and the steel making continuous casting yield is remarkably improved.

The invention discloses a method for planning logistics paths on the basis of bisectors of store point groups. The method includes steps of firstly, generating a clockwise initial path and an anticlockwise initial path; secondly, adding all passing points into the two initial paths to form a clockwise distribution path and an anticlockwise distribution path; thirdly, comprehensively considering unloading quantities and the distances of the paths and determining ultimate distribution paths. The method has the advantages that the angle bisectors of passing point groups are generated at first, the deterministic clockwise initial path and the deterministic anticlockwise initial path are obtained, then the passing points are added into the initial distribution paths according to given sequences and modes to obtain the complete distribution paths, and the unloading quantities and the distances of the paths are integrated with one another, so that the ultimate distribution paths can be determined; logistics distribution knowledge is effectively utilized as compared with ordinary optimal search algorithms, space distribution characteristics of stores are sufficiently utilized, accordingly, traveling salesman problem solving can be simplified, complicated search and comparative computation of intelligent optimization algorithms can be omitted, and the method is high in computation efficiency and good in stability.

The invention belongs to the technical field of unmanned aerial vehicle systems, and particularly relates to a multi-unmanned aerial vehicle cooperative task planning method based on clustering and a genetic algorithm. According to the method, point cluster division of a plurality of task points is completed based on a K-means clustering algorithm, a multi-unmanned aerial vehicle collaborative planning energy consumption optimal track problem is simplified into a traveling salesman problem of a plurality of single unmanned aerial vehicles, a genetic algorithm is improved, a UAV waypoint planning optimization algorithm is provided based on the improved genetic algorithm to carry out track optimization, so that the energy consumption of the unmanned aerial vehicles is optimal, the problem that the actual energy consumption value is increased due to the environmental influence in the flight process and the route planning cannot be executed is avoided, and the flight efficiency and the energy utilization rate are improved.

The invention discloses a self-adaptive particle swarm optimization method solving a traveling salesman problem. On the basis of existing standard particle swarm optimization, backward learning is adopted for initializing particle population, and an inertia weight w and a learning factor c of particle swarm optimization are regulated adaptively along with increase of number of iteration, so that the optimization capability of particle swarm optimization is improved; and in the later period of particle swarm optimization, chaotic local search is introduced so as to prevent particle swarm optimization from falling into local optimum. The improved algorithm is tested through a standard function, an obtained result is obviously superior to that of the standard PSO, and improved particle swarmoptimization is applied to optimization solution of the traveling salesman problem, so as to obtain a better optimization result.

The invention discloses an unmanned aerial vehicle cooperative reconnaissance path planning method based on energy consumption fairness, and the method comprises the steps: calculating a path coveringall target points through employing an algorithm of a traveling salesman problem, and enabling the total energy consumption of the path to be minimum; distributing the N reconnaissance target pointsto K unmanned aerial vehicles by using a path decomposition algorithm and obtaining an initial path of each unmanned aerial vehicle; and forming a closed path of the K unmanned aerial vehicles by adding the initial path of each unmanned aerial vehicle and the path of the unmanned aerial vehicle flying from the starting point to the reconnaissance area. According to the invention, the maximum energy consumption of the unmanned aerial vehicles is effectively reduced, the energy consumption fairness between the unmanned aerial vehicles is improved, and smooth completion of a reconnaissance task is ensured.

The invention relates to a negative feedback self-adaptive mechanism kinematic chain isomorphism identification method for an ant colony algorithm. The method comprises the following steps of forming a topological graph corresponding to the structure of the mechanism kinematic chain; ranking the mechanism framework of the kinematic chain according to structural feature, wherein the step of ranking mainly comprises two steps of layering of the topological graph and initial ranking in the layer; obtaining structural feature set of the mechanism, and converting into a depressed TSP (traveling salesman problem); introducing negative feedback mechanism and self-adaptive parameter adjustment into the ant colony algorithm, and working out condition maximum structural codes corresponding to the structural feature set of the two mechanisms through the improved anti colony algorithm; judging whether the condition maximum structural codes are equal, wherein if the condition maximum structural codes are equal, the two mechanisms are isomorphism, and if the condition maximum structural codes are not equal, the two mechanisms are not isomorphism. According to the method, the defect of the ant colony algorithm that local optimum is likely to be converged is overcome, and the global searching ability and rate of convergence of the ant colony algorithm in operation can be guaranteed.

An article processing apparatus, having a movable member for article processing, includes an information processor configured to set a plurality of clusters each of which includes, as a plurality of nodes, a plurality of candidates of a transition order of states of the movable member with respect to each of a plurality of target regions of an article, set a plurality of costs respectively to a plurality of combinations of two nodes which respectively belong to two mutually different clusters of the plurality of clusters, and generate a transition order of states of the movable member over the plurality of target regions by obtaining, as a solution of a traveling salesman problem, an order of traveling through a plurality of nodes each selected from each of the plurality of clusters, based on the plurality of clusters and the plurality of costs.

The invention relates to a method and device for collecting data by sink nodes of a wireless sensor network, belonging to the technical field of network communication. The method comprises the following steps of: firstly, generating a network topology of sensor nodes according to position information and communication radiuses of the sensor nodes and determining a minimum connected dominating set according to the generated network topology; then acquiring the shortest traversal path length of the minimum connected dominating set according to a TSP (Travelling Salesman Problem); and finally, if the shortest traversal path length of the minimum connected dominating set is smaller than or equal to a threshold value, considering the minimum connected dominating set as a sink node set, and collecting data of all sink nodes in the sink node set through the shortest traversal path of the minimum connected dominating set. According to the embodiment of the invention, multi-hop transmission of a wireless sensor is effectively reduced under the condition of ensuring the data delay requirement in the sensor network, the data transmission accuracy is ensured and the energy consumption is reduced.

A method determines a sequence for drilling holes in a workpiece according to a pattern by first partitioning the holes in the pattern into packets. A global sequence of the packets is determined by solving a global traveling salesman problem (TSP), and a local sequence of the holes in each packet is determined by solving a local TSP for each packet. Then, the local sequences of the holes are joined according to the global sequence of the packets to determine a complete sequence for drilling the holes.

An NP-complete problem can be transformed in polynomial time into any known NP problem. The Hamiltonian circuit problem may be transformed into any other known NP problem (such as the Traveling Salesman problem) and has applications in any context that can be represented by a graph, map, or network structure. The reverse calculation of this transformation from any NP problem into the NP-complete Hamiltonian circuit problem also has a polynomial running time. The composition of this reverse calculation from any known NP problem to the Hamiltonian circuit problem with the polynomial running time of the given algorithm together form a polynomial running time algorithm. Therefore, with this polynomial running time calculation result given for detecting the presence of a Hamiltonian circuit in an undirected graph, it has been shown that P equals any known NP problem or NP. Hence the existence of this Hamiltonian circuit detection algorithm proves P=NP.

The present application relates to an unmanned aerial vehicle migration trajectory generation method and device, an electronic device and a storage medium. The method comprises the following steps ofobtaining a drawing path on a map, preprocessing the drawing path to generate a first path; determining a candidate region of interest and a sampling viewpoint in three-dimensional space according tosample points of the first path; determining local candidates according to the candidate region of interest and the sampling viewpoint, and obtaining a local candidate cost function of the local candidates; generating the local migration trajectory according to the path between different local candidates, and obtaining the local migration trajectory cost function of the local migration trajectory;and according to the local candidate cost function and the local migration trajectory cost function, constructing the cluster traveling salesman problem, and obtaining the global migration trajectoryby solving the cluster traveling salesman problem. The unmanned aerial vehicle can shoot the safe and continuous aerial video by flying according to the migration trajectory generated via the method.

The invention discloses a multi-UAV/UGV collaborative long-term operation path planning method based on multi-objective optimization. The method comprises the following steps: converting a multi-taskfixed charging point problem into a traveling salesman problem for solving through a construction method of a graph of a multi-task fixed charging point problem; according to the construction method of the graph of a multi-task discrete charging point problem, converting the multi-task discrete charging point problem into an equivalent generalized traveling salesman problem to be solved by utilizing a graph conversion algorithm; using a heuristic algorithm ALFG for directly solving the generalized traveling salesman problem; and solving the UAV/UGVs long-term multi-target path planning problemby using a solving algorithm MOALP. According to the multi-UAV/UGV collaborative long-time operation path planning method based on multi-objective optimization, the operation efficiency, the operation duration and the operation range of a ground-air collaborative robot can be effectively improved, more operation task requirements are met, and the higher autonomy effect is achieved.

The invention discloses a load balancing scheduling method based on an improved MMAS. An ant colony algorithm has high efficiency and feasibility in the aspects of a combinatorial optimization problemand an NP hard problem, and has positive feedback performance, high robustness, high distribution performance and high expansibility. Aiming at the problems that the basic ant colony algorithm is long in calculation execution time and is easy to fall into a local optimal solution to cause stagnation, the invention provides an improved maximum and minimum ant colony algorithm, which effectively eliminates the defect of long algorithm execution time in a bidirectional convergence pheromone updating mode; and then by limiting upper and lower limits of a pheromone concentration allowable value, the premature stagnation of the algorithm is overcome, the understanding range is enlarged, and the optimization is improved. By verifying a classic traveling salesman problem, the algorithm has the advantages of the basic ant colony algorithm, and experiments show that the improved algorithm has higher execution efficiency and better calculation stability.

The invention discloses a ship piloting and scheduling method based on an improved discrete particle swarm optimization algorithm. On the basis that various piloting and scheduling rules and habits are analyzed, a mathematical model for solving the ship piloting and scheduling problem is provided. For solving the model, the improved discrete particle swarm optimization algorithm based on dynamic particle sub-swarms is provided. For solving the problem that a large amount of non-feasible solutions exist in a conventional solving method, a method for performing particle fitness calculation by means of a pseudo travelling salesman problem solving method is provided. The model is feasible, the provided algorithm has the advantages of being high in searching speed and searching accuracy and high in stability and is suitable for solving on complicated piloting and scheduling problems.

Provided in the invention is a multi-robot path planning method based on an ant colony algorithm. The method comprises: calculating distances between all tasks according to position coordinates of different tasks; arranging M robots at positions of first M tasks and setting the first M tasks in a taboo table to indicate execution; calculating a transition probability of an ith robot from an ith task to a jth task not in the taboo table; selecting a largest probability value based on the transition probability and setting one corresponding task in the taboo table; and under the condition of allocating all tasks and adding a path, calculating the total length of the path and updating pheromone concentrations on paths between tasks based on the total length. According to the invention, on thebasis of the ant colony algorithm of transforming a multi-traveling-salesman problem into a traveling salesman problem without node adding, the effect of searching for a shortest path of multiple robots or a shortest path of the multi-traveling-salesman problem is good. The stability and optimized precision are high in path planning on a data set.

The embodiments of the invention disclose a method and apparatus for applying artificial fish swarm algorithm parallel processing to Traveling Salesman Problem (TSP) problems based on a MIC card. The method includes the following steps: using a MIC card to conduct a fish warm initialization and initialization MPI processing; using MPI to identify sense of smell based on the behavior rules of an artificial fish swarm which is randomly generated after the initialization of the fish swarm, and determining the number of neighbor artificial fish; and using MPI to determine the behavior of tail-chasing, and conducting the behavior of clustering processing and the behavior of rooting processing; using MPI to acquire an optimal solution artificial fish swarm state value which is determined after the behavior of tail-chasing, the behavior of clustering, and the behavior of rooting processing. The method overcomes the limitation of current algorithms which only support serial arithmetic and of algorithms which require large amount of operation and thus result in low efficiency. The method can perfectly optimize the artificial fish swarm algorithm in solving the TSP problem and optimal results. Also, the method overcomes low efficiency of executing parallel processing when large-scale computing operation is required for a CPU due to the number limitation of chip computing units.