303 results about "Firefly algorithm" patented technology

The invention discloses a short-term electric power load prediction method considering meteorological factors, and belongs to the technical field of electric power load prediction. The method includes: collecting historical load data and meteorological data, and detecting and correcting abnormal data; analyzing the relevance between the load data and the meteorological factors, and determining key meteorological factors; establishing comprehensive meteorological factors according to the relevance between the load and the key meteorological factors; summarizing change characteristics of a daily load curve of a regional power grid, and finding out typical similar days of a prediction day; establishing an Elman neural network short-term load prediction model by employing the selected load and the comprehensive meteorological factors, and training network parameters by employing a firefly algorithm; inputting the comprehensive meteorological factors of a to-be-predicted moment and the corresponding load data to the Elman neural network short-term load prediction model, and outputting a load prediction value of the to-be-predicted moment; and displaying the load prediction value. According to the method, the load data of weekdays, weekends, and official holidays can be accurately predicted, the prediction precision is high, the applicability is high, and reliable basis is provided for making of generation plans for operation personnel of the power grid.

The invention provides a route planning method based on a multi-target glowworm swarm algorithm and belongs to the technical field of route planning. The method includes: modeling a route planning problem, initializing the multi-target glowworm swarm algorithm, updating the glowworm position, determining a non-inferior solution set, updating an external archived file, judging whether a preset maximum iteration number is achieved or not, and determining a Pareto optimal route. The basic glowworm swarm algorithm is improved based on the Pareto dominant conception, and global searching and parallel computing capacities of the glowworm swarm algorithm are well used. Multiple route performance indexes are considered simultaneously during planning, a group of Pareto optimal solution sets can be obtained by planning once, and high flexibility is achieved. Further, the route panning method is different from traditional route planning methods for a single target and route planning methods of using a weighing method to convert multiple targets into the single target, and can better meet practical requirements of route planning.

The invention discloses a naval vessel path planning method based on a firefly algorithm, concretely comprising the following steps of: (1), determining an initial point and a target point of a path according to task information and determining a navigation zone according to the initial point and the target point; (2), establishing a new coordinate system by adopting the initial point as the origin and adopting the connecting line of the initial point and the target point as the axis of abscissas; (3), simplifying and combining barriers in the navigation zone and generating a restricted navigation zone; (4), searching an optimal path through the firefly algorithm; (5), converting the coordinate of each path point in the optimal path into a coordinate under an O-XY system; and (6), obtaining the optimal path of a naval vessel and ending the path planning. By planning the path of the naval vessel through the adoption of a new natural heuristic algorithm, namely the firefly algorithm, as an optimization algorithm, the naval vessel path planning method based on the firefly algorithm has high executing efficiency and can plan navigation paths which meet the practical demands.

The invention relates to the technical field of network security evaluation, in particular to a network security situation prediction method based on a combination of the chaos theory and a neural network. The method comprises the following steps: carrying out processing of normalized network security situation value sequence sets through the mutual information method and the cao method to obtain the optimum embedded dimensions of network security situation sample values, carrying out phase-space reconstruction, and analyzing the maximum Lyapunov exponent of reconstructed samples to determine whether the evaluated samples have chaos predictability or not; determining the numbers of nodes of an output layer and a hidden layer of a BPNN according to characteristics of a nonlinear time sequence and experience; carrying out parameter optimization through an improved firefly algorithm, so as to determine network weights and offset values and establish a network security situation prediction model; and inputting test set samples into the BP neutral network for prediction, and carrying out denormalization of obtained prediction values. The method provided by the invention has the advantages that a network security situation can be more precisely predicted, and the network security situation prediction convergence rate can be increased.

The invention belongs to the technical field of three-dimensionalpath planning for underwater vehicles, particularly relates to animproved fireflyalgorithm based AUV (autonomous underwater vehicle) three-dimensional track planning method and provides an improved fireflyalgorithm based AUV three-dimensional path planning method. The method comprises the steps as follows: performing modeling and fireflypopulation initialization on the three-dimensionalpath planning for the underwater vehicles; calculating objective function values; calculating self-adaptive parameters; comparing thebrightness among fireflies, and updating positions of the fireflies; adding auxiliary planning operators; outputting optimal pathswhen meeting algorithmiteration stop conditions, ending the three-dimensionalpath planning for the underwater vehicles, and outputting an optimal path of the last iteration. Themethodis more flexiblethan a traditional path search algorithm, the auxiliary planning operators are added, and quick planning of AUV three-dimensional paths can be realized.

The invention proposes a traffic flow prediction method based on firefly algorithm and RBF neural network. The method comprises: performing normalization to the sample data so that the input data and output data are on the same order of magnitude; initializing the firefly algorithm parameters; utilizing the random method to initialize the firefly populations and encoding each individual in the populations; using the firefly algorithm to train the RBF neural network to obtain the best individual in the populations; decoding the best individual in the populations to obtain the trained RBF neural network; and utilizing the trained RBF neural network to predict the traffic flow data sample. Compared with the traditional traffic flow prediction method, the method of the invention makes full use of the advantages of the firefly algorithm in the RBF neural network training so that the RBF network possesses a more accurate prediction capability, achieves even faster training efficiency and better generalization capability. The invention belongs to the traffic transportation information engineering technology field and can be used for the predictions of road traffic flows in an intelligent traffic system.

The invention provides a ship route planning method based on tidal current and tide prediction information. The ship route planning method comprises the following steps of: 1, determining the start point, the target point, the planning leaving time and the planning arriving time of a route according to task information, and determining the sailing region according to the start point and the target point, and determining the prediction time range of tidal current and tide according to the leaving and arriving the leaving and arriving times; 2, predicting tidal current and tide according to the sailing region and the prediction time range which are determined in the step 1, acquiring tidal current and tide prediction data which is required by route planning, and storing the data into a file; 3, interpolating based on discrete water depth data in an electronic chart, and acquiring grid water depth data within the sailing region; 4, dividing the sailing region into a seaworthiness region and a prohibited navigation region according to the grid water depth data, the tide prediction data and barrier information in the sailing region; and 5, searching for a best route by an improved firefly algorithm, thus acquiring a route meeting economical and safety requirements. According to the invention, the economical, safe and practical sailing route can be planned.

The invention discloses a shared car sharing site layout and selection model. The implementation process of the model includes the following steps of: establishing a shared rental car site selection cost model; establishing a network effect-based shared car rental profit model; establishing the objective function model of marginal costs and marginal profits; and using a firefly algorithm to simulate the objective function model. With the model adopted, problems in site layout with a strong network effect can be solved; and an improved firefly algorithm-based site selection model represented by sharing shared cars is built.

The invention relates to a flexible job-shop scheduling optimization method, which applies the Metropolis criterion and the sinusoidal adaptive step length to a firefly algorithm so as to optimize andsolve a discrete problem. On the basis of building a mathematical model, an initial solution population of a discrete combination problem is randomly generated, then local search in an individual domain is performed according to the Metropolis criterion in simulated annealing to generate a new individual, the internal energy difference between the new individual and the original individual is calculated, the new individual is accepted according to a certain probability, and global search is performed on each generation by using the discrete firefly algorithm with the sinusoidal adaptive steplength until an optimal solution is searched. The method can better search an optimal solution of the FJSP (Flexible Job-Shop Scheduling Problem) in the global space and has better search precision, search efficiency and stability, thereby having important significance and significant engineering practical application values for solving discrete problems such as job-shop scheduling.

The invention discloses a wind-driven generator three-phase rotor current micro-fault diagnosis method. The method comprises the first step of extracting fault information of three-phase rotor current to obtain a training data set, a confirmed data set and a testing data set; the second step of constructing a double-layer sparse bayes extreme learning machine model; the third step of creating a pairing multi-label classification method, and constructing a classifier set based on a micro-fault diagnosis algorithm model through the combination of a double-layer sparse bayes extreme learning machine; the fourth step of using the training data set to train the micro-fault diagnosis algorithm model, using the confirmed data set and a firefly algorithm to conduct dynamic optimum-seeking iteration, and finally determining an optimal parameter to complete the micro-fault diagnosis algorithm model; the fifth step of inputting the testing data set into the micro-fault diagnosis algorithm model to obtain a diagnosis result of a micro-fault. According to the wind-driven generator three-phase rotor current micro-fault diagnosis method, the wind-driven generator three-phase rotor current micro-fault can be diagnosed, and the wind-driven generator three-phase rotor current micro-fault has the advantages of being high in diagnosis efficiency, and high in diagnosis precision.

The invention discloses a power transformer fault diagnosis method based on an improved firefly algorithm (PFA) optimized probabilistic neural network (PNN). The power transformer fault diagnosis method comprises the following steps: firstly, collecting fault characteristic gas by using a gas chromatographic analysis method and carrying out pretreatment by using a fused DGA algorithm; initializinga PNN neural network, a firefly algorithm and a two-dimensional particle swarm; taking the PNN smoothing factor as a firefly individual, and calculating the position and brightness of the firefly; feeding the solving result of each firefly algorithm back to the particle swarm algorithm, carrying out fitness evaluation on each particle, and updating the positions and speeds of the particles; carrying out loop iteration, substituting the obtained optimal smoothing factor into the PNN to carry out fault prediction, and training a PNN model after PFA optimization; inputting a test sample, and outputting a fault type result, thereby achieving the fault diagnosis of the power transformer. The method is high in search speed, high in diagnosis precision, small in error, and obvious in classification effect.

The invention discloses a firefly grouping method, as well as a power dispatching system and a power dispatching method based on the same. The firefly grouping method comprises the following steps: setting each parameter and initializing groups; figuring out a target value of each firefly particle and sequencing the groups according to levels of the target values; carrying out grouping operation on the groups; independently carrying out evolutionary optimization on each sub group in parallel according to a firefly algorithm; after number of iterations is reached, stopping evolutionary optimization of each sub group; combining all sub groups into one group; then carrying out optimized evolution on the total group according to the firefly algorithm; and after the number of iterations is reached, repeating the above steps until the iterations with the number of iterations are finished or requirements on the experimental accuracy are satisfied. According to the invention, the capacity of each unit is reasonably configured, so that load requirements can be satisfied through power generation of the power system, and low cost is obtained; moreover, through grouping and clustering circulation, information is shared, so that groups are prevented from falling into local extreme value; and finally, a better dispatching scheme can be obtained.

The invention provides an opportunity network evolution algorithm for promoting node cooperation. The algorithm comprises the following steps: firstly, establishing a socialized distributed credit model, including creditability quantification, a direct credit computation method, and an indirect credit evaluation method; secondly, establishing a corresponding incentive model based on creditability, including a creditability-based payoff function design, a creditability optimization based self-adaptive learning mechanism, and the like; thirdly, establishing a multi-population asymmetric evolution model, including multi-population classification, a strategy updating mechanism, and the like; and finally, designing and achieving the evolution algorithm based on the models, including a security cooperation evolution algorithm, and a firefly algorithm for solving the problem of searching the optimum among multiple objects. The evolution model, the credit incentive model and the credit evaluation method provided by the invention are applicable and reliable, and a dynamic security mechanism is operable.

The invention, which relates to the task planning design field, provides an improved-firefly-algorithm-based multi-unmanned-aerial-vehicle cooperative coupling task distribution method. A hybrid discrete firefly algorithm with a special coding-decoding structure is provided; and on the basis of the study on multi-unmanned-aerial-vehicle cooperative task distribution with a time coupling constraint and a special coupling constraint simultaneously, a mathematic model is put forward and task resolving is carried out. The method provided by the invention has high universality. On the basis of the analysis of data obtained by multi-times simulation and verification, the model is perfected, the iterative process becomes short, and the convergence speed is fast. The multi-unmanned-aerial-vehicle cooperative task distribution plan is expressed effectively by means of segmented integer coding by using minimization of the maximum range of the unmanned aerial vehicle as an overall optimization objective; and an optimal solution is searched in the solution space based on an improved DE-DFA algorithm, so that a multi-unmanned-aerial-vehicle task distribution problem in a coupling task environment is solved and a solution is provided for solving a multi-unmanned-aerial-vehicle task distribution problem in a coupling task environment.

The invention provides a power distribution network state estimation method based on a firefly algorithm. The method comprises steps as follows: Step 1, generating a power distribution network node admittance matrix; Step 2, initializing fluorescein and dynamic decision domains of fireflies; Step 3, updating the fluorescein of the fireflies; Step 4, calculating the distance between the fireflies to acquire neighborhoods; Step 5, calculating the movement probability of the fireflies; Step 6, updating positions of the fireflies; Step 7, updating the dynamic decision domains of the fireflies; Step 8, judging whether a convergence condition is satisfied or not, if the convergence condition is satisfied, ending the process, executing Step 9, and otherwise, executing Step 3; Step 9, outputting an optimal solution. According to the method, node voltage is used as a state variable, the node injection power is calculated, an objective function value of the least square method is taken as a firefly fitness function value and converted into the fluorescein of the fireflies, the state variable is updated continuously, and the firefly position with the highest fluorescein is taken as the optimal state estimation result. Experiments indicate that the method has good accuracy and adaptability.

The present invention discloses an analog circuit fault diagnosis method based on a chaos cloud model adaptive firefly algorithm. The method comprises the steps of: applying a certain test stimulus toa tested circuit, and collecting output response signals of the tested circuit at a measurable node of the circuit; employing the wavelet fusion method to perform extraction of a circuit fault feature set from the output response signals; employing the CCAFA-LSSVM (Chaos Cloud Model Adaptive Firefly Algorithm-Least Squares Support Veotor Maohine) to perform fault diagnosis of the fault feature set to achieve classification and location operation of the circuit faults. The analog circuit fault diagnosis method employs a cloud model with a capacity of processing object fuzziness and randomnessto perform corresponding improvement of the firefly algorithm and allow the firefly algorithm to have a good generalization ability and a high robustness. Therefore, when the fault diagnosis is performed, the resolution ratio of the fault mode is high, the diagnosis performance is good, and the fault elements can be accurately located, and the diagnosis performance and the efficiency of the testedanalog circuit can be improved.

The invention discloses a sewage energy saving processing optimization control method based on an improved firefly algorithm and a least squares support vector machine, and belongs to the field of intelligent control. The method comprises steps of using a multicore least squares support vector machine to model energy consumption and water quality of discharged water of a sewage processing factory; using the improved firefly algorithm to optimize established model parameters; and using the improved firefly algorithm to optimize a set value of the controller. According to the invention, the least squares support vector machine is used for modeling energy consumption and water quality of discharged water of a sewage processing factory; a multi-core idea is introduced; the improved firefly algorithm is used for optimizing model parameters, so accuracy of an energy consumption model and a discharged water quality model is greatly improved; the improved firefly algorithm is used for carrying out online optimization on set values of dissolved oxygen concentration and nitrate nitrogen concentration of the controller, so under the premise of meeting the discharged water quality, the energy consumption of the sewage processing factor is reduced; an objective of saving energy and carrying out optimization in the sewage processing process is obtained; and compared with other algorithms, the method is characterized by simple algorithm, few used parameters and high convergence accuracy.

The invention provides a river water level prediction method based on chaotic fireflies and a gradient lifting tree model, and relates to the technical field of information and hydrological conditionprediction. Firstly, data is collected, and required data is divided into five classes; and then data preprocessing is carried out, including abnormal value elimination, missing value processing and data normalization. The improved chaotic firefly algorithm is used for optimizing training parameters of the gradient lifting tree model, and the improved gradient lifting tree model is applied to river water level prediction research of structural data. Finally, constructing a training sample set;randomly adopting a part of five types of data obtained after processing for model training; accordingto the method, a GSO algorithm is used for optimizing and parameter tuning is carried out to obtain a GBDT model under optimal parameters, the generalization ability is better, the water level prediction precision of the model is improved, finally, a test set is combined for carrying out model inspection, errors between an obtained actual value and a calculated value are compared and analyzed, and the good performance of the model is verified.

Systems and methods for a localization system are provided. In one aspect, a RF signature map for a geographical area is determined using a Gaussian Process (“GP”) model. Training RF measurements are taken at some locations within the area to train the GP using the Firefly Algorithm (“FA”). The RF measurements for other locations of the area are predicted using the conditional probabilities of the trained GP and without taking RF measurements at those other locations. The RF signature map is used for fingerprinting localization. In one aspect, a reference RF signature map is constructed for one, some, or all access points (“APs”) covering the area. A location of a user device, such as, for example, a smart phone, is then estimated by comparing the RF signals received by the user device from one or more APs with the determined one or more reference RF signature maps using a combined likelihood function.