326 results about "Tabu search" patented technology

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.

Computerized scheduling methods and computerized scheduling systems according to exemplary embodiments. A computerized scheduling method may be stored in a memory and executed on one or more processors. The method may include defining a main multi-machine scheduling problem as a plurality of single machine scheduling problems; independently solving the plurality of single machine scheduling problems thereby calculating a plurality of near optimal single machine scheduling problem solutions; integrating the plurality of near optimal single machine scheduling problem solutions into a main multi-machine scheduling problem solution; and outputting the main multi-machine scheduling problem solution.

The invention relates to a virtual network function dynamic migration method based on deep belief network resource demand forecasting, and belongs to the field of mobile communication. The method comprises the following steps: (S1) in view of the dynamic features of SFC business resource demand in a slicing network, establishing a system overhead model of comprehensive migration overhead and bandwidth overhead; (S2) in order to realize spontaneous VNF migration, monitoring the resource utilization condition of virtual network function or link in real time, and discovering the deployed bottom nodes or resource hot spots in the link in time by using an online learning based adaptive DBN forecasting method; (S3) designing a topology awareness based dynamic migration method according to the forecasting result, so as to reduce system overhead; (S4) proposing a tabu search based optimization method to further optimize the migration strategy. The forecasting method provided by the invention not only increase the convergence rate of a training network, but also realizes a perfect forecasting effect; by combining the forecasting method with a migration method, the system overhead and the violation frequency of the service level agreement are effectively reduced, and the performance of network service is improved.

The method for generating an integrated guidance law for aerodynamic missiles uses a strength Pareto evolutionary algorithm (SPEA)-based approach for generating an integrated fuzzy guidance law, which includes three separate fuzzy controllers. Each of these fuzzy controllers is activated in a unique region of missile interception. The distribution of membership functions and the associated rules are obtained by solving a nonlinear constrained multi-objective optimization problem in which final time, energy consumption, and miss distance are treated as competing objectives. A Tabu search is utilized to build a library of initial feasible solutions for the multi-objective optimization algorithm. Additionally, a hierarchical clustering technique is utilized to provide the decision maker with a representative and manageable Pareto-optimal set without destroying the characteristics of the trade-off front. A fuzzy-based system is employed to extract the best compromise solution over the trade-off curve.

The invention discloses a method for scheduling parallel test tasks based on grouping and tabu search, belonging to the technical field of automatic test and measurement. The method successively comprises the following steps: a restrain relation among test tasks is determined and analyzed; a graph theory model is built to group the test tasks; a peak dyeing theory in the graph theory is adopted to process the grouped test tasks; an initial scheduling sequence of parallel test task scheduling is configured and tested in parallel according to the group result; and iteration search is carried out by using a tabu search method to search an optimum scheduling sequence, early test starting time of each task is successively determined according to the optimum scheduling sequence, thereby completing a task scheduling plan based on the shortest test time. In the method, the indeterminacy of initial value selection in the process of searching in the traditional method is solved by the method through combining the practical problem of parallel test task scheduling and analyzing the characteristics of the tabu search method, so that the initial scheduling sequence can be well configured and the search rate of the method can be improved, thereby fast finding the optimal task scheduling scheme.

The invention discloses a task allocation method for formation of unmanned aerial vehicles in a certain environment, belonging to the technical field of unmanned aerial vehicles. The task allocation method comprises the following steps of determining a coding sequence of a task allocation algorithm; determining a preponderant function of the unmanned aerial vehicles formed to execute a task; determining a speed update formula and a position update formula of a discrete particle swarm optimization; determining the flow of a tabu search; and determining the flow of hybrid optimization. According to the task allocation method for the formation of the unmanned aerial vehicles in the certain environment, the continuous particle swarm optimization is discretized, the algorithm is simply and conveniently operated on the premise that optimizing property can be guaranteed, and the effectiveness of the discrete particle swarm method is indicated through simulation. According to the task allocation method for the formation of the unmanned aerial vehicles in the certain environment, a supplement strategy of the tabu search algorithm is provided, and the local optimizing capacity of the algorithm is enhanced when the inertia weight [omega] of the particle swarm optimization is larger, i.e. the particle swarm embodies stronger variety, so that the original two algorithms realize complementing each other's advantages, the searching performance can be improved, and the judgment can be verified in multiple groups of simulated tests.

The invention discloses an indoor positioning method based on manifold learning and an improved support vector machine. The method comprises a step of determining a positioning area, dividing the positioning area according to an indoor structural characteristic and a layout characteristic, and obtaining a classification result, a step of obtaining offline training data, and collecting hotspot RSS signal values which can be received by the reference points in different classification area as a training data set, a step of using an isometric mapping algorithm to carry out training data characteristic extraction, a step of using the training data to carry out support vector machine classified training, using a taboo search algorithm to carry out support vector machine classification hyper parameter searching, and establishing the support vector regression model of each category at the same time, a step of carrying out online positioning, collecting the RSS signal value of each hotspot of a target, using a support vector machine classification model to carry out classification, and obtaining the rough positioning area of the target, and a step of carrying out the accurate positioning of the target by using the support vector regression model according to the classification result. According to the method, the time-varying characteristic of the wireless signal intensity is effectively suppressed, and the precision is obviously improved.

The invention provides a collaborative design method and system for a parent plate and a plate blank of a medium plate oriented to production order combined optimization. The method comprises the following steps: specific to a sub-plate one-dimensional combined optimization design in a medium plate production order, building an optimized control model under the consideration of the constraints of complicated production processes, equipment and the like specific to the aim of minimizing residual materials and material loss, deciding a sub-plate combination way, the length of the parent plate and the length of the plate blank, selectively coding the plate blank section of each order after orders are received, designing a decoding method based on column generation, and generating plate blank and parent plate design schemes; then, searching for a better code string in neighborhood through repeated iterations based on tabu search; and when an end condition is satisfied, selecting a current optimal code string for evaluation and adjustment, and outputting corresponding plate blank and parent plate design results lastly. A system controller controls the design processes of the parent plate and the plate blank of the medium plate according to the method. Through adoption of the collaborative design method and system, residual parent plates and material loss are reduced, and the production demand can be met better.

The invention discloses a method for scheduling a public slow system dynamically based on a rolling horizon scheduling algorithm, which comprises the following steps: 1) setting a time axis for expressing an overall scheduling cycle of a working day, wherein a key point is a leasing point which is receiving a service or the leasing point to which a transport vehicle is on the way; a time window is a time range in which a public bicycle leasing point is allowed to serve; and the satisfaction degree of the leasing point i is expressed as fi(ti); 2) establishing a scheduling model; and 3) adopting a rolling horizon-based dynamic scheduling method to divide the overall scheduling cycle into a plurality of sub-scheduling cycles; considering each sub-scheduling cycle as a static scheduling problem; and solving a plurality of static scheduling problems through a variable neighborhood tabu search algorithm and realizing dynamic vehicle scheduling through a method for rolling and updating a scheduling plan discontinuously. The method can perform automation statistic, has good real time and improves operating efficiency.

The invention relates to an improved particle swarm optimization method based on flow production scheduling of a fuzzy due date. The optimal solution of the existing algorithm can not be found easily. The method comprises the following steps: using the penalty function for neighborhood block design of key lines based on the needs of the flow shop scheduling of the fuzzy due date, establishing a taboo table, and adopting the neighborhood search strategy, thereby enhancing the effect of optimal solution of the improved particle swarm algorithm. Since the stagnation can easily occur to the particle swarm optimization, the concepts of exchange operators and exchange sequences are introduced to reconstruct the particle location formula and the speed optimization formula of the particle swarm optimization, and the introduction of the taboo search algorithm helps the particle swarm optimization to search the best solution in the local area. Simulation experiments on the flow production scheduling of the fuzzy due date demonstrate that the improved particle swarm optimization method facilitates the overall solution.

Provided is a scheduling method for cloud task loading balance based on a BP-Tabu search. The scheduling method for the cloud task loading balance based on the BP-Tabu search includes the following steps of formally describing a task scheduling problem of the loading balance in a cloud computing environment, and giving related definition of each element in the cloud computing environment; obtaining an initial solution of task scheduling based on the greedy algorithm thought, and utilizing an time greedy algorithm to compute the initial solution of task scheduling; according to different task allocation schemes, defining a benefit function for virtual machine utilization according to the processing capacity of a virtual machine in MIPS and the execution cost and delay cost of a demand; defining a merit function Bp in a task scheduling scheme P; combining the initial solution of task scheduling obtained by a greedy algorithm, measuring the merit function including a benefit value and a loading balance degree, and obtaining an allocation strategy of task scheduling after optimization based on a Tabu search algorithm. According to the scheduling method for the cloud task loading balance based on the BP-Tabu search, task allocation is balanced, and therefore the scheduling method for the cloud task loading balance based on the BP-Tabu search is suitable for the loading balance of task scheduling in the cloud environment.

The invention discloses a power distribution network route planning method based on tabu differential evolution and a GIS (Geographic Information System). Equal allotment capital recovery calculation is performed under the consideration of the time value of capital, a model is established specific to the aims of minimal annul investment and minimal running maintenance cost, a tabu differential evolution algorithm is put forward on the basis of the model, an obtained optimal solution is decoded, and a planned route is drawn on an electric power GIS platform according to the route number obtained by decoding. According to the method, a non-optimal solution is allowed to be accepted by using tobu search, the problem the differential evolution algorithm tends to falling into local optimum easily is solved by improving the capability of jump-out local optimum, and the planning process of a power distribution network line has the advantages of interactivity, more intuitive planning result and higher flexibility in adjustment of a planning scheme by using the electric power GIS system.

Provided are a pilot frequency scheduling method for a multiple-input-multiple-output system, and a cooperative device. The method comprises: determining an initial user sequence; according to the initial user sequence, conducting tabu search so as to obtain an optimized user sequence; and according to the optimized user sequence, conducting pilot frequency scheduling on UEs in L cells, wherein UEs of the same user combination belonging to the optimized user sequence in the L cells share the same section of pilot sequence. In the embodiments of the present invention, by searching for a plurality of user combinations of the L cells so as to obtain an optimized user sequence of the L cells and conducting pilot frequency scheduling on UEs of the L cells according to the optimized user sequence, a good pilot frequency scheduling effect can be obtained in the case where the algorithm complexity is relatively low, thereby taking account of the calculation overhead and comprehensive pilot frequency effect of a multiple-input-multiple-output system in balance.

The invention provides a task planning method for a swarm unmanned aerial vehicle system, and relates to the technical field of unmanned aerial vehicle application, which can give full play to the cooperative consistency of a swarm unmanned aerial vehicle system, enables the combat effectiveness to be optimal, and better meets the requirements of heterogeneous unmanned aerial vehicles for forminga formation to execute heterogeneous tasks and enabling the tasks to meet the requirements of swarm unmanned aerial vehicle task planning with certain time sequence constraints. The method comprises the steps of S1, establishing a task model of a swarm unmanned aerial vehicle system according to tasks issued by a superior, formulating a task planning principle according to characteristics of the swarm unmanned aerial vehicle system, and determining a target function and an optimization target; s2, determining constraint conditions of the model by comprehensively considering voyage constraints,endurance constraints, task execution capability constraints and collaboration constraints according to task characteristics and unmanned aerial vehicle performance of the swarm unmanned aerial vehicle system; and S3, solving the model through a tabu search algorithm to obtain an approximately optimal solution. The technical scheme provided by the invention is suitable for the task planning process of the swarm unmanned aerial vehicle system.

The invention discloses a hyper-heuristic algorithm-based satellite task planning method. The method includes the following steps that: 1, the priority dominance relation data of a target are obtained; and 2, a tabu search algorithm or a simulated annealing sub-heuristic algorithm is adopted to search priority combinations so as to obtain a planning result. According to the method of the invention, the sub-heuristic algorithm is adopted to search the combined application of the heuristic rule information of multi-angle priorities, and therefore, a super-heuristic algorithm-based and efficiently robust satellite imaging and transmission joint planning method can be provided.

The invention discloses a vehicle scheduling algorithm based on greedy tabu search, and the algorithm comprises the steps: establishing a vehicle scheduling basic model based on dynamic planning, solving, and obtaining a vehicle scheduling initial solution; establishing a loss function based on a tabu search algorithm to serve as a basis for iteration of an initial solution; performing local iterative search improvement based on a neighborhood structure thought to generate a plurality of reliable candidate solutions; in order to prevent local search from falling into a local optimal solution,adding various destruction operator disturbance initial solutions to enable the initial solutions to have relatively high quality; and filtering the candidate solutions according to a loss function adopted by a tabu search algorithm to obtain an optimal solution in the candidate solutions, and generating a final scheduling scheme. Cost of vehicles and drivers is considered, a scientific model algorithm, namely a greedy tabu search algorithm and a scheduling scheme for searching the minimum number of vehicles and conforming to scheduling habits, is applied, effectiveness of the algorithm is verified, and the algorithm is applied to practice.

The invention provides a method for remote sensing satellite observation task planning. The method comprises the steps of when a dynamic variable neighborhood tabu search algorithm is adopted, setting an initial tabu table T *, an initial tabu table length as shown in the description, a dynamic variable tabu table length as shown in the description and an initial solution x0, beginning from the number of iteration k=1, T* as shown in the description and the initial solution x0, adopting an improving neighborhood construction method to implement an iterative process of the tabu search algorithm, and after a stopping criterion is satisfied, outputting an optimal solution x * at the time point when the stopping criterion is satisfied; adopting an adjusting neighborhood method to implement the iterative process of the tabu search algorithm according to T * as shown in the description and x * at the time point when the stopping criterion is satisfied, and after the stopping criterion is satisfied, adopting a relatively optimal solution, which is about to be output and obtained by conducting a local search algorithm on the optimal solution x * which is output at the latest, as an optimal scheme of remote sensing satellite observation task planning. The method adopts the dynamic variable neighborhood tabu search algorithm to process remote sensing satellite observation task planning, and can improve the running efficiency of the algorithm and expand the search range of the solution, and the algorithm does not easily fall into a loop search state.

The invention discloses a method for optimizing resources of a static-dynamic mixed service in a three-layer network, which mainly solves the problem of transmission of the static-dynamic mixed service resource in the three-layer network, and comprises the following steps of: 1) inputting three-layer network topology information and a static service matrix; 2) taking a priority routing algorithm of request bandwidth total maximum as static service routing; 3) taking an ant colony optimization-based integrated routing algorithm as dynamic service routing; 4) determining whether to call an interlayer or intralayer resource optimization method or not according to grade parameters of the dynamic service and the joint routing result; 5) performing network periodic triggering on the global resource scheduling and optimizing method based on the adaptive period of the load intensity; and 6) carrying out the algorithm running result. In the invention, by introducing transport network layering,the ant colony optimization-based dynamic service joint routing algorithm, an interlayer resource coordination mechanism and a tabu search-based static service re-grooming method, global network resource optimal utilization is improved, the false rejection rate of a user connection request is reduced, and the requirement of end-to-end quality of service (QoS) is met.

The invention discloses a test task scheduling method based on critical paths and tabu search, belonging to the field of automatic testing system parallel test task scheduling. The method comprises the following steps of: firstly determining and analyzing a test task, initializing parameter setting, subsequently determining an initial test task sequence by adopting a priority coding mode, finding an optimal test scheme selection set under the test task sequence by using a method of tabu combination critical path so as to find an optimal test task sequence and a corresponding test scheme selection set through iteration of multiple times. According to the method, an initial task sequence which meets the constraint is provided while the tabu search is applied to solve a test task scheduling problem, an adjacent domain search range of the tabu is simplified, the redundant adjacent domain search operation is reduced, and the operation efficiency and optimizing efficiency of the test task scheduling method are improved ultimately.