37 results about "Gray wolf" patented technology

Embodiments of the invention disclose a multi-target-based improved gray wolf optimization algorithm which is used to solve the technical problems that a standard gray wolf algorithm falls into a local optimal value easily and has a low convergence speed and other defects while processing a multi-target optimization problem in the prior art. The method of the embodiments comprises the following steps: S1, setting a wolf pack initialization parameter and a direction correction probability, and randomly initializing wolves' individual positions; S2, calculating an adaptability value of each wolf individual according to a solving target, and selecting the three wolf individuals ranking top; S3, optimizing the wolves' individual positions of a wolf pack, generating moderate wolves, and updating a wolf pack position; S4, executing direction correction operation on the updated wolf pack, controlling the upgraded wolf pack to participate correction of the size of dimensions according to the direction correction probability, and obtaining a corrected wolf pack position; and S5, determining whether an iteration frequency reaches a preset maximum iteration frequency, outputting the corrected wolf pack position as a final optimization result if the iteration frequency reaches the preset maximum iteration frequency, and, if the iteration frequency does not reaches the preset maximum iteration frequency, turning to the S3 so as to continue performing iteration searching.

Disclosed are a variable weight grey wolf algorithm optimization method and an application. According to the method, social classes are set and act on the whole search and predation processes of a grey wolf population, and the grey wolf population surrounds a target in the search process and surrounds the target in the center in the predation process; and in the iterative search process, the positions of an alpha grey wolf, a beta grey wolf and a delta grey wolf with high social classes in the population are firstly, secondly and thirdly close to the target all the time, and in the iterative process, the positions of the grey wolfs in the population are described by combination of variable weight functions of the alpha grey wolf, the beta grey wolf and the delta grey wolf, wherein the weight w1 of the position of the alpha grey wolf is gradually reduced to 1/3 from 1, the weight w2 of the beta grey wolf and the weight w3 of the delta grey wolf are gradually increased to 1/3 from 0, and the use of w1, w2 and w3 meets the requirements that the sum of w1, w2 and w3 is equal to 1, w1 is greater than or equal to w2, and w2 is greater than or equal to w3. The method has the advantages that the search process is remarkably accelerated and the optimization calculation can be finished more quickly.

The invention discloses a grey wolf algorithm-based rectangular part layout method and device, and belongs to the field of structural part optimization blanking. The grey wolf algorithm-based rectangular part layout method comprises the following steps: setting an algorithm target function and constraint conditions; performing wolf pack initialization, decoding and positioning based on a lowest horizontal line method of fitness, solving a material utilization rate and determining alpha wolves through grade sorting; performing all grey wolves walk, solution of the material utilization rate andalpha wolf updating; performing the grey wolf attacking preys, solution of the material utilization rate and alpha wolf updating; and performing loop iteration until a termination condition is reachedto obtain an optimal solution, namely the position code Xalpha of the alpha wolf and the prey odor concentration Yalpha felt by the alpha wolf, namely the optimal layout scheme and the correspondingmaterial utilization rate. The grey wolf algorithm-based rectangular part layout method can improve the plate utilization rate and the solving speed, and has good practicability and applicability in solving the rectangular part layout problem.

The invention belongs to the technical field of data analysis and discloses a kernel incremental transfinite learning machine and a differential multi-population gray wolf mixed optimization method. Aiming at the problem that the kernel incremental transfinite learning machine (KI-ELM) has the redundant nodes with low learning efficiency and poor accuracy; At first, that invention utilize the differential evolution algorithm and the multi-population grey wolf optimization algorithm to propose a hybrid intelligent optimization algorithm--the differential multi-population grey wolf optimizationalgorithm, optimizes the node parameter of the hidden layer, and determine the effective node quantity, so as to reduce the network complexity and improve the learning efficiency of the network; Secondly, the depth structure is introduced into the kernel incremental transfinite learning machine, and the input data is extracted layer by layer to realize the high-dimensional mapping classification of the data and improve the classification accuracy and generalization performance of the algorithm. The simulation experiment results show that the hybrid intelligent depth kernel incremental transfinite learning machine provided by the invention has good prediction accuracy and generalization ability, and the network structure is more compact.

The invention provides an intelligent vehicle path tracking control system and method based on a Gray Wolf Optimizer (GWO). The intelligent vehicle path tracking control system includes a path planning system, a path tracking decision system and a vehicle chassis execution system which are connected in sequence, wherein the path tracking decision system includes two subsystems of a driver prediction controller and a GWO attitude controller, a preview-follower driver model based on the vehicle side acceleration feedback correction is adopted in the prediction controller, and the GWO is adoptedin the GWO attitude controller. According to the intelligent vehicle path tracking control system and method, an ideal path is planned by the path planning system, the path tracking decision system receives the ideal path information, the driving state of a vehicle is adjusted through a wire-controlled steering actuator and a hub motor brake actuator of the vehicle chassis execution system, the accurate tracking of the ideal path is realized, and the driving stability of the vehicle is improved.

The invention relates to a gray wolf optimization algorithm based frequency modulation PID control method of a doubly-fed induction wind-powered generation unit, and belongs to the technical field ofpower system control. The method comprises the following steps: performing PID control on parameters of a current converter at the power grid side of the doubly-fed induction wind-powered generation unit in an offline state; comparing the reference rotor speed of a generator and the reactive powder with the actual values; inputting the difference into the PID control to obtain corresponding reference current; comparing the obtained reference current with the actual current; inputting the difference into the PID control to obtain rotor voltage; optimizing the PID control link through the gray wolf algorithm; outputting the obtained voltage to the outside of the controller by adding compensation quantity; performing frequency modulation on the wind-powered generation unit through the outputvoltage; recycling the frequency modulation of the wind-powered generator in order to realize continuous tracking of the maximum power point. With the adoption of the method, the problem of local optimization of the maximum power point tracking target function in the frequency modulation process of the doubly-fed induction wind-powered generation unit can be solved.

The invention belongs to the technical field of computers and provides a data analysis method and device based on a grey wolf optimization algorithm. The method comprises steps of acquiring to-be-analyzed data; according to the preset searching space, through the gray wolf optimization algorithm, calculating a penalty coefficient and a nuclear width of a wolf group; and according to the penalty coefficient, the nuclear width and a preset analysis model, analyzing the to-be-analyzed data so as to obtain an analysis result. According to the invention, by introducing a reverse learning mechanism,the penalty coefficient and the nuclear width of the wolf group are calculated in the gray wolf optimization algorithm, so a parameter optimal solution of the preset analysis model is obtained. Thus,the specifies of the gray wolves in the wolf group are extended; the global detection ability of the algorithm is improved; and by analyzing the to-be-analyzed data is performed through the analysismodel, so the precise analysis result is rapidly obtained.

The invention relates to a robust multiuser detector design method, which solves the technical problem of high error rate of the traditional robust multiuser detector in an impact noise channel environment. The robust multiuser detector design method comprises the steps of: initializing algorithm parameters; using an opposition-based learning method to initialize a parent population, and determining three wolves in the parent population; updating the parent population by adopting an improved gray wolf algorithm position updating equation, and sorting population individuals according to fitnessvalues from large to small; generating offspring crossover mutants by utilizing the parent population, performing position information differential operation on an evolutionary direction of the offspring crossover mutants and successful crossover mutation probabilistic information when the fitness values of the offspring crossover mutants are superior to that of the parent population, acquiring new evolutionary direction information and saving the new evolutionary direction information, and updating positions of the three wolves. With the adoption of the robust multiuser detector design method adopting a Huber theory and utilizing a residual non-rapid-increasing function to design a multi-user detector in an impact noise channel, the mentioned problem is effectively solved, and the robustmultiuser detector design method can be used in multi-user detector design.

The invention relates to a photovoltaic MPPT (Maximum Power Point Tracking) method based on an improved grey wolf optimization algorithm. According to the technical scheme, the method comprises the steps: S1, selecting N random values from [0, 1] to serve as the initial position of a grey wolf population, wherein grey wolf individuals represent the duty ratio in a photovoltaic system; S2, acquiring the output power of the photovoltaic system under the control of each duty ratio, taking the output power as the fitness of gray wolf individuals, and recording the three gray wolves with the maximum fitness as alpha wolf, beta wolf and delta wolf in sequence; S3, shrinking the search range of the grey wolf population, and updating the upper and lower limits of a search interval; S4, performing position updating on the grey wolf population by using an improved position updating formula; S5, executing reverse search, comparing with gray wolf population fitness, and updating alpha wolf, beta wolf and delta wolf; S6, judging whether a termination condition is met or not, and if yes, stopping iteration and stabilizing the photovoltaic system at the duty ratio corresponding to the alpha wolf; otherwise, returning to the step S2, and continuing iteration.

Disclosed is a WSN three-dimensional coverage enhancement method based on an improved grey wolf optimizer. The contribution degree of an alpha wolf with the most abundant experience is enhanced by theweight calculation formula in the step 3, so that the hunting process of the wolf group is more accurate; to solve the defect that the early global search performance of a classic grey wolf algorithmis poor, the dynamic convergence factor in the step 4 is defined, so that the global optimization capability of the algorithm is enhanced while the complexity of the algorithm is not sacrificed; fora coverage rate threshold of a classic virtual force algorithm, the dynamic stepping formula in the step 4 is defined, so that the algorithm is higher in early optimization speed and more accurate inlater convergence position. To solve the defect that the earlier-stage optimization speed of a classic grey wolf algorithm is relatively low, a virtual force factor is introduced into the wolf pack position updating formula in the step 4, so that the optimal guidance quality of the global search process of the algorithm is enhanced, and the convergence speed of the algorithm is increased.

The invention relates to an Alpha-guided gray wolf algorithm and other applications in two-stage operational amplifier design, belonging to the improvement and application field of swarm intelligencealgorithm. It is characterized in that: Alpha guidance mechanism is introduced into the grey wolf algorithm, which provides the direction of potential prey for the wolf pack leading group, and furtherimproves the convergence speed of the algorithm; as that standard GWO fall into the local optimum for a long time, the population concentrates in the area near Alpha, Beta and Delta and is difficultto jump out of the area. The invention introduce Alpha mutation mechanism into the grey wolf algorithm, and provides a channel for the population to jump out of the local optimum; In the invention, the algorithm parameter adopts the scalar design, and the algorithm structure is simplified. In the optimization design of two-stage unbuffered CMOS operational amplifiers, it has a good effect of optimization.

The invention discloses an intelligent electric meter wireless sensor network layout method based on an improved grey wolf algorithm. The method comprises the following steps: S1, setting wolf populations including an original population and an assistant population, and initializing parameters including an original population scale M, an assistant population scale M, a maximum iteration frequencyIntermax, a search space dimension N, a search space upper bound ub and a search space lower bound lb; S2, initializing grey wolf positions of the original population and the assistant population; S3,calculating the fitness value of each grey wolf in the original population and the assistant population, and respectively selecting the wolf with the best fitness value in the first three populationsas a decision wolf; S4, disturbing the positions of the decision wolves in the original population and the assistant population; S5, sorting all decision-making wolves in the original population andthe assistance population, and selecting the first three wolves with the highest fitness values; S6, respectively updating the positions of the original population and the assistant population; S7, judging whether an iteration termination condition is met or not: if so, outputting the position of the alpha wolf as a layout coordinate of the wireless sensor, if not, repeating the steps S3 to S6.

The invention discloses a generator excitation system parameter identification algorithm based on an improved grey wolf algorithm. The generator excitation system parameter identification algorithm comprises: establishing an original model and an actual system model of an excitation system in a no-load state; identifying the actual system model entering the linear region through an improved grey wolf algorithm to obtain linear part parameters; inputting the linear part parameters into an actual system model; and identifying the actual system model which enters the non-linear region and is substituted with the linear part parameters through an improved grey wolf algorithm to obtain the non-linear part parameters. On the basis of the grey wolf algorithm, a convergence factor nonlinear decreasing strategy and a grey wolf grouping alternate chasing strategy are provided, population diversity of wolf groups is enhanced, and the defect that the algorithm is prone to falling into local optimum is overcome. The grey wolf algorithm is applied to identification of excitation system parameters, identification of the excitation system parameters is effectively achieved by improving the grey wolf algorithm, and an identification result proves that the identification precision and stability of the improved grey wolf algorithm are superior to those of a traditional grey wolf algorithm.

The invention provides a fixed-wing unmanned aerial vehicle cluster collaborative path planning method based on an optimal pheromone grey wolf algorithm. The method comprises the following steps: s1, establishing a three-dimensional map containing M threat sources; s2, establishing a cluster route planning cost function; s3, planning the tracks of all fixed-wing unmanned aerial vehicle clusters by adopting a grey wolf algorithm based on optimal pheromones. According to the method, a cooperative flight path planning problem of a fixed-wing unmanned aerial vehicle cluster in a three-dimensional scene is solved by adopting an optimal pheromone grey wolf algorithm, and a plurality of optimal grey wolves are selected based on a wolf pack grade system in the algorithm to update pheromones on flight path points; wherein, the exploration stage of each gray wolf follows the guidance of pheromones so as to improve the convergence speed and the solution stability of the algorithm, and the rotation angle constraint of the unmanned aerial vehicles and the collision avoidance constraint between the unmanned aerial vehicles are met. According to the technical scheme, the problem of fixed-wing unmanned cluster collaborative path planning in a high-dimensional and multi-constraint complex scene is solved.

The invention relates to a camera calibration method based on Levy flight and mutation mechanism grey wolf optimization, and the method comprises the following steps: S1, building a nonlinear camera model, and determining calibration parameters; S2, setting the number of grey wolf populations and the maximum number of iterations; S3, acquiring a calibration parameter upper limit and a calibrationparameter lower limit of the camera, and generating a grey wolf position; S4, establishing a target function, and obtaining a back projection error and positions of an alpha wolf, a beta wolf and a gamma wolf; S5, generating a grey wolf intermediate by using a grey wolf optimization algorithm based on Levy flight and a mutation mechanism; S6, updating the position of the grey wolf, and judging whether iteration is continued or not; and S7, obtaining a back projection error according to the target function, wherein the grey wolf position with the minimum back projection error is an optimal calibration parameter. Compared with the prior art, the algorithm can be combined with actual engineering cases, can be accurately and effectively used for multi-dimensional nonlinear problem optimizationsolution, effectively improves the calibration precision, and has good stability and accuracy.

The invention discloses a random forest and grey wolf optimized coal body gas content prediction method, which comprises two stages, namely construction of a random Sendor prediction model and use ofa grey wolf optimization algorithm, and aims to select and determine optimal values of relevant parameters of the random forest prediction model by using the grey wolf optimization algorithm. Therefore, the identification and prediction effects of the media gas content prediction model are enhanced. The gas basic data is initialized through two steps of missing value filling and feature selection.The geological characteristics of the coal bodies in different regions are different, and the characteristic selection can be used for carrying out targeted selection on the mine coal body data in different regions, so that the gas content prediction accuracy is enhanced, and the method is suitable for mines in different regions. According to the method, effective prediction of the gas content can be achieved, the working efficiency is improved, meanwhile, implementation of gas disaster prevention measures in the coal mining process can be guided, and the method has important significance inprevention and control of gas disaster accidents and accurate mining of coal.