32 results about "Niche genetic algorithm" patented technology

The invention relates to a network-based system for analyzing opinion information in a discrete text, belonging to the field of network information safety. The system comprises the following modules: a discrete text information acquisition module which acquires network information in a preset analysis cycle, a discrete text information tracking and restoring module which restores ellipsis and remote anaphora in the original content to obtain a text which contains a relatively complete text structure and semantic information, a semantic information mining and characteristic extracting module which realizes semantic information mining and characteristic extracting on text information by utilizing a latent semantic indexing technology, an opinion information clustering module which realizes information clustering by combining a niche genetic algorithm with a K-Means method, a hot opinion event discovery module which mines the hot opinion in the obtained topic and event, and a background information processing and data supporting center which analyzes data and provides a repertoire specially for a network, new words in the network, the existing class information and the existing hot topics. By applying the invention, the problem that information analysis is influenced as the text structure of the existing network opinion information is incomplete, ellipsis and remote anaphora are more and the new works in the network are more is solved, and the accuracy for discovery of the opinion and hot event is improved by adopting a high-efficiency clustering method.

The invention relates to a method for programming a transmission network based on a determinacy two-layer programming mode. The programming mode takes a transmission network investment cost as an economy target, and takes load shedding sum of a regular operation and a single fault operation of the system as a reliability target. An underlayer target is the reliability target; an underlayer restriction is an operation restriction of the regular operation and the single fault condition of the system; an upperlayer gives priority to the economy target; the underlayer reliability target is added to the upperlayer target in a way of penalty function, and the upperlayer restriction is an awaiting frame line number restriction. An improved arithmetic mixed with a niche genetic algorithm with a primal-dual interior method together is adopted to calculate the mode; the niche genetic algorithm is used for processing a integer variable of the upperlayer programming and has a global optimization; the primal-dual interior method is adopted to have a quick calculation to improve the arithmetic speed and a convergence. The invention is able to add the reliability issue to the economy programming in a restriction way and realize an economy optimization of the programming proposal under a high reliability condition.

The invention relates to a method for inverting anisotropy parameters using variable offset vertical seismic profile data. The method includes: calculating vertical longitudinal wave speed and vertical transverse wave speed of each layer of a vertical seismic profile; collecting symbol layer positions and determining intersections of each layer and well trajectory; building up a surface layer model and an observing system; randomly initiating the anisotropy parameters of layers to be determined and cubic coefficient of a top layer interface to be determined; forming anisotropy speed models to be optimized; screening receiver inter-layer points ; collecting shot first arrival to calculate polarizing angles; building up a target function using P wave first arrival travel time and the polarizing angles; using niche genetic algorithm to optimize the target function; finishing optimization of the anisotropy parameters of the layers to be determined and configuration of the top layer interface to be determined; calculating layer by layer 'from shallow deprocessing to deep deprocessing' to finish optimization of the anisotropy parameters of the whole model. By the method, a reflecting interface is close to actual stratum, linear convergence ray tracing is high in calculation efficiency, complicated internal relations of anisotropy media are simplified, iterative test ray tracing modeling is guided by guide search of the niche genetic algorithm, and the anisotropy parameters and interface configurations are inverted and optimized nonlinearly.

The invention relates to an improved particle filtering method based on the niche genetic algorithm. The method comprises the following steps of: (1) sampling based on the initial probability distribution to obtain initial particles and setting the initial weight; (2) based on the filtering estimations of M particles at (k-1)th moment, carrying out EKF or UKF on each sampled particle to obtain the mean value and the covariance matrix corresponding to the kth moment, and respectively sampling n particles from each disposal distribution by using Gaussian density as the proposal probability density and using the mean value and the covariance matrix of each particle as the mean value and the covariance matrix of the distribution to obtain a set formed by nM particles; wherein n and M are natural numbers; (3) respectively updating the weights of the Nm particles to obtain the weight of each particle; and (4) when the obtained particle set has particles are less than the effective sample capacity, resampling with the niche genetic algorithm. The invention improves the particle filtering, inhibits the degeneracy phenomena and the particle-lack problem caused by simple random resampling, and improves the diversity and the adaptability of the particles, thereby improving the performance accuracy of the particle filtering.

The invention discloses a method for planning a distribution network based on full life cycle cost management. The method comprises the process that a calculation model for the full life cycle cost of a distribution network planning scheme is provided under the premise of meeting safe and stable running of a power grid, distribution network path planning is optimized by using a clustering crowding niche genetic algorithm by taking the minimum full life cycle cost as an objective function, a distribution planning scheme with the minimum full life cycle is acquired through performing comparative analysis on the full life cycle cost of different switch arrangement patterns, and finally the feasibility and effectiveness of the method are proven through calculating examples. The method provides a new analysis method and an assistant decision-making theory basis for distribution network planning, running and transformation and operation benefits of electric power companies, power grid formulation technologies are optimized, and a planning scheme with the optimal economical efficiency is acquired.

A microsatellite group formation consumption optimization method mainly includes the step: determining satellite orbital transfer time and factors related to fuel consumption as transfer time and a final position; determining an inter-satellite encoding structure in a satellite group; forming a satellite orbital transfer energy consumption calculation method; performing niche genetic algorithm analysis for formed satellite orbital transfer energy consumption to form optimal orbital transfer energy consumption data, and converting the data into corresponding double-pulse engine energy consumption control parameters. By the aid of niche technology and genetic algorithm, energy consumption is taken as an optimization objective, the method is applicable to a single surrounding satellite, multi-satellite synchronous formation and multi-satellite asynchronous formation, the algorithm does not require satellite group release sites, and flexibility of microsatellite group release can be enhanced. The method retains the advantages of high robustness, global search, parallelism and the like of a genetic algorithm, and the shortcomings of poor local search capacity of the genetic algorithm are overcome.

The invention discloses a multi-target flexible job shop scheduling method based on an improved niche genetic algorithm. Constructing a production scheduling sequence according to the process data ofall the workpieces in the multi-target flexible job shop, taking the production scheduling sequence as an individual, and generating a primary population; calculating a total objective function valueof the individual, and calculating a fitness value of the individual by using an improved niche method; selecting an individual set in a roulette mode according to the fitness value; implementing crossover operation and mutation operation of the genetic algorithm; forming a new population by the obtained individuals and the individuals with the highest fitness value in the generation population; repeating the steps until a termination condition is met, outputting an optimal individual in the last generation population, and arranging processing treatment by adopting a scheduling sequence of theoptimal individual, so as to realize multi-target flexible job shop scheduling. The improved ecological niche genetic algorithm is adopted to solve the scheduling problem in the production process, ahigh-quality scheduling result can be stably obtained, workshop resource allocation is optimized, and therefore the production efficiency of a workshop is improved.

The invention relates to the field of protein structure prediction and discloses a protein structure prediction method based on the improved niche genetic algorithm. According to the method, the niche genetic algorithm is introduced into protein structure prediction, and the genetic algorithm is improved to a certain extent in terms of selection and variation. Based on data obtained from experiments and results obtained through comparison with other methods, the method has the advantages that the corresponding minimum free energy value of protein can be searched more comprehensively, so that a more stable protein structure is obtained; operation time is shortened greatly, meaning that the method has high time efficiency.

The invention discloses a prediction method combining an improved niche genetic algorithm (INGA) and a wavelet neural network (WNN). The method comprises the following steps: selecting the WNN with a stronger nonlinear fitting capacity and a better fault-tolerant performance to predict a security situation; carrying out optimization of traditional WNN parameters through the adaptive genetic algorithm; introducing a novel niche technology with dynamic fuzzy clustering and elimination rules, carrying out niche classification of a population through dynamic fuzzy clustering to form a plurality of niches, and adjusting adaptive values of individuals through a punishment mechanism; and calculating the adaptive value of each niche individual according to classified niches, comparing the adaptive value of each individual with the adaptive value of the optimum individual of a current generation, and eliminating the niches of which the adaptive values are much different from the adaptive value of the optimum individual of the current generation so as to achieve overall elimination of the niches. The method provided by the invention has the advantages that the optimization capacity and convergence rate of the genetic algorithm are improved, the problem of high possibility of premature convergence of the genetic algorithm is solved through a higher population diversity, and the network security situation can be more accurately predicted.

The invention discloses a method for identifying a broadband wireless transmitter based on the Hammerstein-Wiener model: the first step: modeling the broadband wireless transmitter; the second step: using an improved adaptive niche genetic algorithm (or grid adaptive direct search (mads) algorithm, adaptive niche genetic algorithm and other model identification algorithms) carry out Hammerstein-Wiener model parameter identification, and obtain the estimated model parameter vector; the third step: broadband wireless transmitter identification. The invention has strong global optimization and local optimization capabilities, and has the advantages of good robustness and high identification accuracy, and its effectiveness is verified in the identification of broadband wireless transmitters.

The invention discloses a resource scheduling method based on an optimized niche genetic algorithm. The method comprises the following steps: S1, building a resource scheduling optimization mathematicmodel based on the building of a multi-objective function and a multi-constraint condition; s2, performing weighting processing on the multi-objective function based on a weight particle swarm algorithm, and converting the multi-objective model into a problem of a single-objective function; s3, dividing the population into K clusters according to a K-means clustering algorithm, and determining aclustering center; s4, selection, self-adaptive crossover, self-adaptive variation and niche elimination operation; and S5, judging whether a termination condition is met or not to obtain a final resource scheduling mode. The method aims at solving the problems that existing multiple targets are difficult to solve and prone to falling into a local optimal solution in resource scheduling. Accordingto the resource scheduling method based on the optimized niche genetic algorithm, the three processes of determining the weight of a multi-objective function, the radius of the niche and crossover and mutation operators are improved, the cost of a resource scheduling mode is effectively and remarkably reduced, and the processing time is shortened.

The invention discloses a classification prediction method based on a multi-stage hybrid model, which adopts a multi-population niche genetic algorithm and combines a plurality of filtering methods and classifier prediction prior knowledge respectively in the process of feature selection and classifier selection, thereby obtaining an optimal feature subset and an optimal classifier subset. Then, the classifier ensemble method is used to integrate the optimal classifier subset with the optimal feature subset into the overall model for the final prediction. Finally, the hybrid model is applied to the field of credit rating to verify its forecasting performance in binary classification problem. The experimental results show that the multi-stage method used in the hybrid model plays a positiverole in improving the prediction performance of the model, and the final prediction performance of the model is better than that of other comparative models.

The invention relates to a niche genetic algorithm-based coherent source fast localization estimation method for a single-vector hydrophone. According to the method of the invention, a niche genetic algorithm can be utilized to optimize a solution process, so that a global optimal solution can be quickly obtained; estimated quality can be evaluated qualitatively and quantitatively through using the time-process graph of an azimuth; and therefore, the quality of a calculation result, namely, the confidence of the calculation result, can be qualitatively evaluated directly according to the fluctuation of the time-process graph, and the calculation result can be also quantitatively evaluated according to the numerical values of the numerical characteristics of the time-process graph. Since avector z is introduced, the design scheme of a multivariate niche genetic algorithm is provided, and therefore, the univariate niche genetic algorithm can be extended into the multivariate niche genetic algorithm with the complexity of the algorithm not increased.

The invention discloses a topology optimization method of an annular tensioning integral structure based on a niche genetic algorithm. According to the determined span, the equivalent fraction and thenumber of joints per segment of the annular tensegrity structure model, with the topological relationship as the control variable, the minimization of the total mass of the annular tensioned structure as the control objective, and with the prestress as the whole feasible, the shaped bar as the non-crossing, the load stress control and the displacement limit as the constraint conditions, the nichetechnology is combined, the optimal solution of the topological relationship is searched. The invention can optimize the topology of an annular tensioning integral structure with arbitrary span, arbitrary equal fraction and arbitrary number of nodes in each section, and has the advantages of strong universality and low steel quantity in the obtained result.

The invention relates to a subarray-level distributed frequency control array sidelobe suppression method based on an improved genetic algorithm, and belongs to the field of array antenna signal processing. According to the method, grating lobes or high side lobes of an angle dimension and a distance dimension of a distributed frequency control array are suppressed at the same time by using an improved genetic algorithm; the improved genetic algorithm is a niche genetic algorithm based on a pre-selection mechanism, a competition mechanism is introduced between a parent and a child, only when the fitness value of a child individual is higher than that of the parent individual, the child individual can correspondingly replace the parent individual, otherwise, the parent individual is still reserved in a next-generation group, and the niche genetic algorithm is a niche genetic algorithm. Optimizing local and global optimal solutions in a feasible solution space; and the tabu search is introduced to accelerate the convergence rate, and the premature phenomenon and the stagnation of the genetic algorithm are avoided, so that the local optimal solution is jumped out. According to the method, the optimization result of the subarray position and the subarray frequency offset of the subarray-level distributed frequency control array is improved, and a better sidelobe suppression result is realized from the distance dimension and the angle dimension.

The invention discloses a high voltage insulator numerical optimization method based on a small population real-coded genetic algorithm and belongs to the field of optimization technology. The methodcomprises the following steps: based on a niche genetic algorithm of a sharing function, adjusting the fitness of each individual in a population; based on numerical values of the obtained fitness, solving a minimum value problem by adopting a reverse selection operator; guiding cross pairing by adopting Euclidean distances among the individuals, and obtaining a parent population selection operator; based on the obtained parent population selection operator, modifying a cross algorithm containing an amplification factor, thus a novel cross algorithm is obtained; and modifying a mutation operator in a real genetic algorithm, thus a modified mutation algorithm is obtained. A genetic algorithm is combined with a finite element method, a basic genetic algorithm is improved, an improved geneticalgorithm maintaining population diversity is provided, and electric field distribution of an insulator is beneficially and effectively improved, so that the structure of the insulator and the structure of a grading ring are more reasonable; and scale of the required population is small, so that optimization time is shortened.

The invention discloses a free-form surface structure multi-working-condition Pareto solution set optimization method based on a changing ecological niche. The method comprises the steps: 1, inputtingloads corresponding to multiple working conditions; 2, setting an optimization target and a target function under various working conditions; 3, arranging NURBS control points, and setting the coordinate value range of the control points and NURBS parameters; 4, starting a change niche genetic algorithm to solve the target function in the step 2; and step 5, outputting a Pareto solution set for adesigner to select and use. According to the method, under complex multi-working-condition conditions, the niche genetic algorithm is used for optimizing the shape of the free-form surface to obtaina Pareto optimal solution set, so that a designer can select a satisfactory structural form from the Pareto optimal solution set according to actual working conditions of a project. According to the method, the ecological niche radius is changed along with the change of the sum of fitness values of all individuals in the population by setting the ecological niche change function, so that the search speed and precision are improved.

The invention discloses a free curve profile error evaluation method based on a hybrid evolutionary algorithm, which is a data processing method for free curve profile error evaluation based on a least square method, a non-uniform rational B-spline interpolation function and a multi-dimensional hybrid evolutionary algorithm. According to the invention, a hybrid evolutionary algorithm of a particleswarm algorithm with parallel adaptive weights and a niche genetic algorithm based on DC is adopted, the adaptive adjustment of an actually measured coordinate system and a theoretical coordinate system is realized according to the criterion of the least square method, and the position error is eliminated from the profile error. The method does not require a preset initial value, the influence ofthe preset initial value on the final position error result is avoided, the convergence speed of the optimization algorithm is accelerated, the ability of the optimization algorithm in local optimization is improved, local optimum is avoided during global search, and the position error is eliminated from the profile error result, so as to ensure the accuracy of the evaluation line profile error.