149 results about "Quantum particle swarm optimization" patented technology

The invention discloses a quantum-behaved particle swarm optimization (QPSO) based multi-objective dynamic workflow scheduling method, and belongs to the technical field of cloud computing. The method includes the steps: inputting a workflow and a QoS (quality of service) request; acquiring state information of virtual machines and transmission information among the virtual machines; setting a to-be-executed task set V', and setting objective functions of time, cost and reliability for a task schedule in the V'; allocating optimal resources to the to-be-executed tasks by the aid of QPSO, and judging whether total time, total cost and total reliability of task execution meet the QoS request of a user or not after the tasks are executed; dynamically updating the V', transmission speed among the virtual machines and operating speeds of the virtual machines. By means of dynamically partitioning the workflow and dynamically updating network bandwidth information, the optimal resources are allocated to the workflow tasks accurately, errors between the calculated time and actual execution time and the calculated cost and actual execution cost are reduced, time can be shortened, and cost is reduced while reliability is enhanced.

A method for diagnosing analog circuit fault based on vector-valued regularized kernel function approximation, includes steps of: step (1) acquiring a fault response voltage signal of an analog circuit; step (2) carrying out wavelet packet transform on the collected signal, and calculating a wavelet packet coefficient energy value as a characteristic parameter; step (3) utilizing a quantum particle swarm optimization algorithm to optimize a regularization parameter and kernel parameter of vector-valued regularized kernel function approximation, and training a fault diagnosis model; and step (4) utilizing the trained diagnosis model to recognize circuit faults.

The invention relates to a clustering method, in particular relates to an improved fuzzy C-mean clustering method based on quantum particle swarm optimization, and belongs to the technical field of data mining and artificial intelligence. The improved fuzzy C-mean clustering method comprises the steps of: firstly, based on the conventional fuzzy C-mean clustering algorithm, improving the fuzzy accuracy of the conventional clustering algorithm by using a novel distance standard in place of a Euclidean standard; meanwhile classifying singly and quickly through using an AFCM (Adaptive Fuzzy C-means) algorithm to replace a randomly distributed initial clustering center to reduce the sensitivity of the clustering algorithm on the initial clustering center; and finally, introducing a QPSO (AQPSO (Adaptive-Quantum Particle Swarm Optimization)) parallel optimization concept based on distance improvement in a clustering process, so that the clustering algorithm has relatively strong overall search capability, relatively high convergence precision, and can guarantee the convergence speed and obviously improve the clustering effect.

The invention discloses an electronic nose signal processing method based on a mixing characteristic matrix. The method comprises the steps that first, characteristic extraction is conducted on an original data matrix to establish the mixing characteristic matrix, then a binary quantum particle swarm optimization algorithm is used for conducting characteristic selection on the mixing characteristic matrix, at last, the mixing characteristic matrix is fed into a classifier, a decimal quantum particle swarm optimization algorithm is used for conducting synchronous optimization on mixing characteristic sub matrixes and the parameters of the classifier, mode recognition is conducted to determine the mixing characteristic sub matrix with the highest recognition rate, and the sensor characteristic corresponding to the mixing characteristic sub matrix is selected to be used as the optimized characteristic of an electronic nose signal for conducting mode recognition. The electronic nose signal processing method based on the mixing characteristic matrix has the advantages that the defect that the complete information of an entire response curve can not be reflected only by extracting a single characteristic is overcome; the operation complexity is lowered, the problem of redundancy between sensors is effectively solved, the recognition rate of wound infection detection of an electronic nose is improved, and beneficial guidance can be provided for a doctor to choose a proper treatment method.

The invention relates to the field of unmanned vehicle control, and provides a parallel design scheme using quantum particle swarm optimization, to ensure that the control output meets the physical constraints of the vehicle and the comfort requirement for a human body so as to enable the vehicle to preferably adapt to the current road condition. The technical scheme of the parallel design schemeusing quantum particle swarm optimization includes the steps: establishing a kinetic model based on an unmanned vehicle, and performing discretization on the kinetic model; based on the above step, constructing a generalized cost function with a punishment item and an encouragement item by using a generalized Lagrangian multiplier so as to convert the constraint problem into a nonrestraint problem; and performing parallel design of quantum particle swarm optimization, performing optimized solution on the cost function of model prediction control by means of the parallel design to obtain a series of controlled variables, and finally acting the first component of the controlled variables on the vehicle. The parallel design scheme using quantum particle swarm optimization is mainly applied tothe unmanned vehicle control occasion.

The invention discloses a two-dimensional dual-threshold SAR image segmentation method based on quantum particle swarm optimization. The method comprises the realization steps: (1) initializing the particle swarm population size M and the maximum iterations Tmax and generating the initial position of each particle randomly; (2) calculating a fitness function, and obtaining the optimal position of a current particle and an overall optimal position of iteration of this time according to the maximum in-cluster variance value; (3) calculating Pid and mbestd; (4) constructing a random number set; (5) setting a definition value, judging the relation between the random number set and the definition value, and upgrading the position of each particle according to a formula; (6) checking whether the end condition is achieved, achieving the end if yes, or else executing the steps from two to five; (7) segmenting an SAR image according to a pair of searched optimal thresholds stored in two dimensions of the particle pointed by the overall optimal position. Compared with a classical segmentation method, the segmentation method is good in effect of dividing the SAR image, and relatively small in time complexity.

The invention discloses an access point selection and resource distribution combined self-healing method in an ultra-dense network, and belongs to the field of the ultra-dense network. The method comprises following steps that firstly, a WNCU judges whether faults appear in access points or not; the WNCU records a name list of communication damaged users served by the access points and broadcasts the name list to adjacent access points if the faults appear in the access points; the adjacent access points divide self-healing subchannels from own normal subchannels dynamically; then, the communication damaged users select suitable adjacent access points so as to obtain service continuously according to self-healing channel dividing results and own speed requests; and finally the access points distribute resources to original users and newly accessing communication damaged users again by using a quantum particle swarm optimization algorithm. The method has the advantages that the self-healing function in the ultra-dense network is realized; when the faults appear in the access points, the service demands of the communication damaged users can be effectively ensured; the system energy efficiency is improved; and the operation cost is reduced.

The invention relates to a reactive power optimization method of a photovoltaic system based on a quantum particle swarm algorithm, belonging to the technical field of reactive power regulation of a photovoltaic power station. The method of the invention comprises the following steps: (1) analyzing the influence of grid-connected photovoltaic power station on the voltage of the distribution network system; 2, establishing a mathematical model for optimizing that reactive power compensation of a distribution network of a photovoltaic power station; Step 3, solving the reactive power optimization model of the photovoltaic system by using the quantum particle swarm optimization algorithm. The reactive power optimization method of the photovoltaic system based on the quantum particle swarm algorithm provided by the invention can effectively reduce the active power network loss, obviously increase the voltage value of each node, and enable the distribution network system to run more economically and stably.

The invention discloses an Internet of Vehicles traffic flow prediction method (MPSO-RBF) based on a quantum particle swarm optimization strategy, and solves the problem of how to accurately predict the future traffic flow of urban roads. The method comprises the following steps: establishing a traffic flow prediction mathematical model, namely establishing a corresponding model according to traffic flow data characteristics, optimizing an initial clustering center by using a simulated annealing algorithm and a genetic algorithm, and training an RBF network by using a fuzzy mean clustering algorithm; an improved quantum particle swarm optimization strategy is used, the randomness of particle positions is increased, and optimized neural network parameters are output; and applying the optimized algorithm to parameter optimization of the radial basis function neural network prediction model, and obtaining a data result needing to be predicted through high-dimensional mapping of the radialbasis function neural network. Test results show that the algorithm provided by the invention can reduce prediction errors and obtain better and more stable prediction results.

A vector-valued regularized kernel function approximation (VVRKFA) based fault diagnosis method for an analog circuit comprises the following steps: (1) obtaining fault response voltage signals of an analog circuit; (2) performing wavelet packet transform on the collected signals, and calculating wavelet packet coefficient energy values as feature parameters; (3) optimizing regularization parameters and kernel parameters of VVRKFA by using a quantum particle swarm optimization algorithm and training a fault diagnosis model; and (4) identifying a circuit fault by using the trained diagnosis model. In the invention, the classification performance of the VVRKFA method is superior to other classification algorithms, and optimization of parameters by the quantum particle swarm optimization (QPSO) algorithm is also superior to the traditional method of obtaining parameters. The fault diagnosis method provided by the invention can efficiently diagnose the component faults of the circuit, including soft faults and hard faults.