435 results about "Artificial bee colony algorithm" patented technology

The invention discloses a cascade reservoir multi-objective optimization scheduling method based on an improved artificial bee colony algorithm. The method comprises the following steps that s11, the basic information data of a cascade reservoir system are acquired; s12, a multi-objective scheduling model including power generation, estuarine ecology and water supply is established according to the information of the reservoir system; and s13, the optimal scheduling scheme of the cascade reservoir system is solved by performing the improved artificial bee colony algorithm. Global optimization of the reservoir scheduling problem can be realized so that the calculation efficiency and accuracy can be enhanced, and a new approach can be provided for solving the multi-objective optimization scheduling problem of the cascade reservoir system.

The invention discloses a power transformer fault diagnosis method. The power transformer fault diagnosis method comprises the following steps of determining N fault types of a transformer, and determining corresponding fault characteristic quantities for diagnosing the N fault types; taking the fault characteristic quantities corresponding to the N fault types as a testing sample, and performing normalizing processing of testing sample data; combining any two of the N fault types, establishing X (described in the specification) SVM ( Support Vector Machine) secondary classifiers, and training the X SVM secondary classifiers, and optimizing a SVM kernel function using a method based on combination of K-fold cross validation and an artificial bee colony algorithm; calculating generalization errors of each SVM classifier according to the K-fold cross validation method; and diagnosing the N fault types using an improved reordering adaptive directed acyclic graph support vector machine method. The invention has a capability of well diagnosing the fault type of a transformer, can greatly improve the accuracy of transformer fault diagnosis, and provides a reliable basis for transformer maintenance.

The invention discloses a fan gear box fault diagnosis method based on an artificial intelligence algorithm. According to the method, structure features and fault types of a fan gear box are studied, and parameter optimization is performed on least square support vector machine (LSSVM) by an artificial bee colony algorithm to be applied to fault diagnosis of the fan gear box. By means of the method, the artificial bee colony algorithm is used for optimizing the LSSVM to excellently finish the fault diagnosis of the fan gear box, the recognition rate is high, and the reliability is good.

The invention discloses an ORP (optimal reactive power) method of a distribution network of an electric power system in the technical field of ORP of the distribution network of the electric power system. The method comprises the following main steps: introducing an accelerated evolution operation and an investigation operation in an ABC (artificial bee colony) algorithm to a basic differential evolution operation; and judging whether conditions of convergence of a hybrid algorithm are met, and ending the optimization and outputting the optimal result if the conditions of convergence are met.The hybrid algorithm for solving the ORP problem exerts the advantages that the operation is simple, robustness is good and the like, of the differential evolution algorithm, and can be used to shorten the running time of the algorithm and improve the probability of finding out the global optimal value.

The invention relates to the field of computer networks, especially the field of e-commerce and big data analysis, and more particularly relates to a modeling method and a modeling device for user behavior analysis and prediction based on a BP neural network. The modeling method applies user behavior data acquisition and analysis as well as a budgeting algorithm to parameter source modeling, adopts a three-layer neural network model for design, constructs a neural network prediction model under the three-layer neural network model for predicting mobile user behaviors, and regards behavior types of users as evaluation parameters. The modeling method comprises the steps of: regarding indexes of network user behaviors as parameters, inputting the parameters into the BP neural network in one-to-one correspondence; and subjecting all the input parameters to continuous iterative processing in a hidden layer, and outputting a result through an output layer. The modeling method is characterized by utilizing an artificial bee colony ABC algorithm to compensate for the shortcomings of the BP neural network prediction model, and applying the artificial bee colony ABC algorithm to the operation of the hidden layer and the output layer, thereby improving the convergence rate of the prediction model.

The invention discloses a group evacuation simulation system and method by combining an artificial bee colony and a social force model. The method comprises the following steps: acquiring an evacuation scene parameter to construct an evacuation scene three-dimensional model; finding all exits of the evacuation scene in the three-dimensional model; dividing to-be-evacuated crow in the evacuation scene into a plurality of groups according to the individual-to-individual relation and the position from the exit, screening the individual closest to the exit position in each group as a leader of each group; using each exit of the evacuation scene as the food source, and the leader as the leader bee in the group, thereby establishing one-to-one mapping with each parameter in the artificial bee colony; under the leading of the leader of each group, executing a parallel artificial bee colony algorithm to dynamically plan a path to move to the exit; and if the leader arriving the corresponding exit, waiting at the exit until the individual is inexistent in each group, and ending the crow evacuation simulation. Through the adoption of the method disclosed by the invention, the simulation efficiency and the channel efficiency in the public place are improved, and the assistance is offered for the real evacuation drill.

The invention discloses a manipulator dynamic model identification method based in an improved artificial bee colony algorithm. A manipulator linear dynamic model taking joint friction into consideration is established through utilization of an improved Newton-Euler method. A dynamic parameter identification algorithm is designed through importing of the improved artificial bee colony algorithm. An UR industrial robot is taken as an experiment subject; through design of a stimulation track, joint movement of the industrial robot is stimulated; the joint movement data is sampled; after torque is filtered, parameter identification is carried out through utilization of the improved artificial bee colony algorithm; the dynamic parameter estimation of the UR industrial robot is realized; and the dynamic model is verified according to the torque prediction precision. An experiment shows the accuracy and effectiveness of the industrial robot dynamic model the identified by the invention.

The invention provides a multi robot path planning method based on a multi-target artificial bee colony algorithm and belongs to the technical field of path planning. The method includes path planning problem environment modeling, multi-target artificial bee colony algorithm parameter initialization, three-variety bee iteration optimization path and non-inferior solution determination, good path reservation by sequencing and optimum path set outputting. By means of the method, the standard artificial bee colony algorithm is improved based on the concept of non-domination sequence of Pareto domination and crowd distance, and the multi-target artificial bee colony algorithm applicable to solving the multi-target optimization problem is provided. In the path planning process, multiple performance indexes of path length, smoothness and safety are considered in the algorithm, and a group of Pareto optimum paths can be acquired through one-step path planning. The path planning method belongs to meta-heuristic intelligent optimization methods, is different from the traditional single-target path planning method, and can well adapt to path planning tasks in complex environment.

The invention discloses a project constraint parameter optimizing method based on an improved artificial bee colony algorithm. According to the method, the problem of the project constrained parameter optimization is described by the adoption of an objective function and an equality / non-equality constraint; an artificial bee colony is initialized according to the value range of parameters; partial parameters in a parameter vector is selected according to the probability M to serve as the adjusted object, and step size in search is adjusted in a self adaptive mode, so that a guide bee can search nectar sources randomly in an intra area; according to the corresponding cost function value fi of the nectar sources, the fitness function value fiti is acquired through fi, the probability Pi of follow bees being transferred to the nectar sources is further acquired, and whether position updating is conducted or not is judged; the current optimal solution is recorded in every iterative search process, and the optimized estimated value of the parameters is acquired through the finite iterative search. The step size in search changes in a self adaptive mode with the times of search, on the premise that search accuracy is not affected, search time is reduced effectively, and search efficiency is improved.

The invention discloses a power two-stage interactive optimization scheduling system of a virtual power plant in a haze environment, and specifically relates to a distributed energy resource optimization scheduling method. The method comprises the following steps: A, establishing a photovoltaic power generation prediction and load prediction system considering the influence of the haze environment; B, establishing a power system two-stage interactive scheduling system considering a virtual power plant; C, establishing a mathematical model considering two-stage interactive scheduling of the virtual power plant in the haze environment; D, improving the artificial bee colony algorithm; and E, carrying out solving based on an optimization model of the improved artificial bee colony algorithm. With the system, the energy crisis can be effectively relieved, and the environment can be protected. As new energy and the smart grid technology develop, it is difficult for a power system to directly schedule a lot of grid-connected DERs. The embodiment demonstrates the validity of the model and the feasibility of the algorithm, embodies the superiority of the aggregation of various DERs in power system scheduling, proves the influence of the haze environment on photovoltaic output, load prediction and scheduling, and provides a feasible reference for grid optimization scheduling.

The invention discloses a complicated function maximum and minimum solving method by means of a parallel artificial bee colony algorithm based on a computer cluster and belongs to the field of computer parallel computing technique. According to the method, the purpose of complicated function maximum and minimum solving by means of the parallel artificial bee colony algorithm on a plurality of computers is achieved by combination of the method and a message passing interface (MPI) software package in the Linux system. Experimental results show that in a complicated function maximum and minimum solving process, the parallel artificial bee colony algorithm is higher in accuracy and higher in speed than an ordinary serial algorithm, and efficiency of computing function maxima and minima is improved greatly.

The invention discloses a design method for a nonlinear system controller of an aero-engine. The method is directed at control problems of the affine nonlinear system of the aero-engine within a large deviation range. The method comprises the following steps: linearizing the nonlinear system of the aero-engine based on the theory of exact linearization, adopting the variable structure control in designing a non-linear sliding mode controller, changing a control structure with a purpose by using a linearized state variable to enable the linearized state variable to move based on the designed sliding mode track so as to offset parameter perturbation and exterior interference, finally directed at the key problem of designing non-linear controller parameters, adopting the artificial bee colony algorithm in adjusting the controller parameters, and calculating the optimal parameter to optimize the control effect. According to the invention, the method is directed at the problem of designing complex controller parameters, and obviates the need for tedious manual debugging and repeated verification. By using the bee colony algorithm in designing a reasonable target performance function, the method enables an automatic calculation of the optimal controller parameters and enables the non-linear controlling system of the aero-engine to have a satisfied dynamic performance and robust stability.

The invention discloses a motor imagery EEG pattern recognition method based on the time-frequency parameter optimization of an artificial bee colony. The method comprises the steps of conducting the leads selection based on the linear decision rule, selecting time-domain and frequency-domain optimal parameters based on the artificial bee colony algorithm, extracting features based on the common spacial pattern algorithm, and finally classifying features based on the linear discriminant analysis algorithm. The result of the method shows that, a lead channel of larger inter-class distinction degree can be effectively selected based on the lead selection algorithm. At the same time, based on the time-frequency parameter optimization algorithm of the artificial bee colony, a time window and a frequency band of larger inter-class distinction degree can be automatically selected, so that a better classification effect is obtained. The method is capable of effectively recognizing different motor imagery modes. Compared with the traditional parameter manual selection method and the frequency-domain parameter automatic selection algorithm, global optimal parameters can be automatically searched in both time domain and frequency domain at the same time based on the above method. Therefore, the feature extraction and feature classification effect for motor imagery EEG signals is improved.

The present invention disclose a parallel machine batch scheduling method and system based on an improved artificial bee colony algorithm in a deterioration situation. With this method, a near-optimal solution for the parallel machine batch scheduling problem with deteriorating jobs and maintenance consideration can be obtained. The model of the present invention is derived from an actual production process with considerations of machine maintenance and batching as well as additional processing and maintenance time for jobs and machines over time in actual production. According to the present invention, the settlement of this problem is conducive to providing reliable decision support for the production and maintenance of an enterprise in complex real production conditions, thus reducing enterprise operation costs, increasing enterprise productivity, and promoting building of a modern smart factory of the enterprise.

The invention discloses an optimization algorithm for a vehicle routing problem with time windows. According to the optimization algorithm for the vehicle routing problem with time windows, a genetic algorithm and an artificial bee colony algorithm in a modern heuristic algorithm are organically integrated and improved, and an improved artificial bee colony algorithm is developed to solve a VRPTW problem.

The invention provides an improved artificial bee colony algorithm-based task scheduling method and belongs to the field of cooperative jamming. The method comprises the steps of firstly, giving a task scheduling assessment index set and jamming effect assessment index quantitative calculation method by integrating analysis jamming effects of a UCAV (Unmanned Combat Air Vehicle) on target radars, and performing normalization processing; secondly, determining task scheduling constraint conditions of cooperative jamming, and building a cooperative electronic jamming task scheduling model (CEJ-TSM); thirdly, solving the CEJ-TSM by adopting an improved global artificial bee colony (IGABC) algorithm; and finally, obtaining a cooperative electronic jamming task scheduling scheme according to a result of the IGABC algorithm. According to the method, the search capability of an optimal solution can be remarkably improved for the problem in task scheduling for cooperative jamming of the UCAV for the multiple enemy radars, and the efficiency and accuracy of generating the task scheduling scheme are improved.

The invention discloses an IMABC optimized support vector machine-based transformer fault diagnosis method. The method comprises the steps of 1, dividing a collected sample set S={(x1,x2),(x2,y2)...(xn,yn)}, with class tags, of an oil-immersed transformer into training samples and test samples, wherein xi represents sample attributes including five attributes of hydrogen, methane, ethane, ethyleneand acetylene, yi represents the class tags, and 1, 2, 3, 4, 5 and 6 correspond to a normal state, middle temperature overheat, high temperature overheat, local discharge, spark discharge and arc discharge respectively; 2, proposing an improved artificial bee colony algorithm, fusing population classification and gene mutation in the artificial bee colony algorithm, and optimizing parameters of asupport vector machine; and 3, taking Ci and sigma i as the optimized parameters of the support vector machine, building a multilevel support vector machine fault diagnosis model, and performing transformer fault diagnosis by utilizing data in the step 1. According to the transformer fault diagnosis method, the parameters of the support vector machine can be effectively optimized, so that the accuracy of binary classification is improved.

The invention puts forward a distribution network reactive power optimization method based on an improved artificial bee colony algorithm, specifically comprising the following steps: building a distribution network reactive power optimization model, and setting constraint conditions; setting a control variable and a state variable; initializing a population; randomly generating the initial position of the population, and calculating the fitting value; carrying out local optimization; updating the position of a nectar source, calculating the fitting value, and keeping an optimal solution; and judging the termination conditions, and outputting the optimal solution. The crossover and mutation mechanism in the difference algorithm is introduced into the artificial bee colony algorithm. The method is applicable to reactive power optimization of a distribution system. The convergence speed is increased. The local search ability of the artificial bee colony algorithm is improved. The effect of network loss is reduced. The operation cost of the power grid is lowered.

The invention relates to the technical field of intelligent control and specifically relates to a parameter setting method for the fractional order PI controller of a servo motor. the parameter setting method comprises the steps of S1, establishing a transcendental equation system containing three unknown parameters Krho, Ki and lambda of a fractional order PI controller; 2, determining an optimal interval for the above three unknown parameters Krho, Ki and lambda based on the artificial bee colony algorithm; 3, determining a target function of an artificial bee colony and a fitness value calculation method; 4, searching the optimal values of the above three unknown parameters Krho, Ki and lambda based on the search strategy-improved artificial bee colony algorithm. According to the technical scheme of the invention, the requirements of a servo control system on the quick response performance and the high-precision trajectory tracking performance can be met.

The invention discloses a highway traffic state estimation method considering the speed discrete characteristic. The highway traffic state estimation method comprises the following steps that S1: speed discrete characteristic indexes and traffic flow characteristic parameters are set; S2: traffic flow data are acquired and the traffic flow characteristic parameters are weighted by using a RelielfF method; S3: the clustering center of the traffic flow characteristic parameters is optimized by using an artificial bee colony algorithm; and S4: the optimized clustering center is outputted and the traffic estimation state is determined. The speed discrete characteristic parameters are introduced based on a fuzzy C-mean algorithm, the characteristic weight is determined by using the RelielfF method according to different contribution degrees of different characteristics on the state estimation result, and optimization of the clustering initial point is performed by using the artificial bee colony method so that estimation of the highway traffic state can be realized.

The present invention proposes a transmission network expansion planning method based on the improved artificial bee colony algorithm, and a local search method based on cross-operation with the global optimal solution to find a better honey source to replace the original honey source as the optimal honey source, while ensuring the optimization At the same time, through the binomial intersection of the current solution and the global optimal solution, the development ability of the algorithm is enhanced, so that it has good global search and local development capabilities, and has higher optimization accuracy and higher accuracy in the application of transmission network expansion planning. Faster convergence.

The invention discloses a multi-unmanned aerial vehicle three-dimensional formation reconfiguration method based on an artificial bee colony (ABC) algorithm, and belongs to the technical field of unmanned aerial vehicle control. The method comprises the following steps: firstly, establishing a motion model of an unmanned aerial vehicle and then giving out mathematical description of optimal time control of three-dimensional formation reconfiguration; and after piecewise linearization control input is carried out, carrying out unmanned aerial vehicle three-dimensional formation reconfigurationby using an ABC algorithm. Compared with the prior art, the method disclosed by the invention has the advantages that the searching time of unmanned aerial vehicle formation reconfiguration through the ABC algorithm is shortest, so that the rapidity of the unmanned aerial vehicle formation reconfiguration is realized; and the ABC algorithm is a global search method which can avoid falling into local optimization, so that the accuracy of the unmanned aerial vehicle formation reconfiguration is improved.

The invention relates to an EEG (electroencephalogram) signal feature classification method based on an ABC-SVM. The method comprises the steps: firstly carrying out EEG signal feature extraction through employing a CSP algorithm; secondly carrying out the optimization of a penalty factor C and a kernel parameter g of a SVM (support vector machine) through employing an artificial bee colony algorithm; finally carrying out the training of an SVM classifier through the obtained optimal parameter, and carrying out the classification prediction of samples through employing the trained classifier. Compared with an SVM classification recognition method optimized through a conventional algorithm, the method can effectively improve the classification recognition rate of an EEG signal, and is remarkably superior to a conventional classification recognition method.

The invention discloses a post-disaster unmanned aerial vehicle base station deployment method and system based on an artificial bee colony algorithm. The method comprises the steps of constructing an unmanned aerial vehicle base station three-dimensional scene with a preset size and distributed with a plurality of terminal users; calculating the network throughput of each terminal user in the scene area, and calculating the overall network throughput according to the capacity constraint of the unmanned aerial vehicle base station; calculating the maximum overall network throughput and the optimal flight position of the corresponding unmanned aerial vehicle base station in the air by adopting an artificial bee colony algorithm, and deploying the unmanned aerial vehicle base station.

The invention provides a building energy consumption prediction method based on an artificial bee colony algorithm and a neural network. The method comprises the steps that firstly, the artificial bee colony algorithm is utilized for conducting weight value optimization on the neural network; secondly, the optimized neural network is utilized for predicting building energy consumption. The artificial bee colony algorithm is an optimizing algorithm simulating a bee colony and has the advantages that control parameters are fewer, implementation is easy, and calculation is convenient; compared with a particle swarm algorithm, a genetic algorithm and other intelligent computing methods, the artificial bee colony algorithm has the prominent advantages that in each iterative process, global search and local search are both performed, the probability of finding an optimal solution is greatly increased, local optimum is avoided to a great extent, and global convergence is enhanced. Thus, when the artificial bee colony algorithm is adopted to optimize the initial weight value of the neutral network, the accuracy of the neutral network predicting the building energy consumption is improved, and meanwhile the defects existing in weight value optimization of the neutral network at present can be overcome obviously.

The invention discloses a method for realizing wireless network fault detection through an improved radial basis function neural network so as to realize better classification for nonlinear separableuser data. In the method provided by the invention, a decision tree based learner and a bagging method are used for performing feature selection from user data of space and time dimensions, an artificial bee colony algorithm combined with mutation operations is used for realizing global optimization of neural network parameters, thus, performance of a neural network classifier is improved, a monitoring function of neighboring base stations is introduced in a distributed cooperative detection method, detection accuracy rate is improved and data transmission consumption is reduced, and ideal performance is realized in a densely distributed sparse-user small base station fault detection problem.

The invention relates to a magnetic flux leakage testing defect reconstruction method based on an improved artificial bee colony algorithm. According to the method, a radial basis function neural network is used as a forward model, and an error square sum of a magnetic flux leakage signal predicted by the forward model and an actually measured magnetic flux leakage signal is used as a target function to improve the artificial bee colony algorithm; a current individual optimal solution and a global optimal solution are introduced to accelerate algorithm convergence speed; the improved artificial bee colony algorithm is used as an iterative algorithm to solve a reconstruction problem, and the finally obtained global optimal solution is a reconstructed defect outline. The magnetic flux leakage testing defect reconstruction method based on the improved artificial bee colony algorithm improves speed and precision of magnetic flux leakage testing defect reconstruction.

The invention provides a water quality evaluation prediction method based on a fuzzy wavelet neural network and aims to solve problems of slow convergence speed during water quality prediction, poor approximation effect and inaccurate prediction result existing in a BP neural network in the prior art. According to the method, a fuzzy wavelet neural network prediction model is constructed through utilizing the known water quality analysis index quantity, prediction index quantity and fuzzy rule quantity, and the fuzzy wavelet neural network prediction model comprises an input layer, a subordinate layer, a fuzzy rule layer, a wavelet layer, an output layer and a defuzzication layer; a subordinate function parameter and a wavelet parameter of the wavelet layer are adjusted, a cost function is further defined, a BP algorithm based on a gradient descent method is utilized to carry out parameter adjustment, problems of low convergence speed, easy-to-generate concussion effects and local optimum can be avoided, model stability is improved, an initial parameter is optimized through employing an artificial bee colony algorithm, and the method is mainly applicable to water quality index prediction.

Provided is a method for assigning collective fire of combined teams. The method comprises a step 1 of enabling a weapon-identifying sensor to acquire required data information; a step 2 of establishing an upper objective function with the intention that enemy targets have the smallest overall threat to a home side; a step 3 of establishing a lower objective function with the intention that the enemy targets have the lowest battlefield survival value; a step 4 of performing cross iteration on the upper objective function and the lower objective function by using a double artificial bee colony algorithm in order to obtain an optimal solution of collective fire distribution; and a step 5 of assigning the enemy targets to each weapon platform of the home side according to the optimal solution in order to achieve fire attack. A collective fire assigning scheme acquired through the double artificial bee colony algorithm achieves effective cooperation between a main attack force and a secondary attack force so as to increase an overall fire attack effect, reduce the overall threat of enemy targets to home side, and increase the overall battle effect of the home side.

The invention relates to a time-varying demand associated logistics transportation optimized dispatching method based on a bee colony algorithm. The method comprises the specific implementation steps that (1) a distribution center acquires known demand information of a client; (2) an initial solution is constructed; (3) optimization is performed on the initial solution through an artificial bee colony algorithm based on taboo table thought; (4) a pre-loop passing through all the vertexes of a complete undirected graph G is obtained; (5) the distribution center collects time-varying information and acquires demand information of the time-varying client; (6) the pre-loop is adjusted according to a certain rule, and constraint conditions are satisfied; (7) the process is ended. According to the method, a final dispatching scheme based on time-varying demand associated with transportation dispatching is obtained.