88 results about "Selection operator" patented technology

The present invention relates generally to a method of identifying data delivery parameters relating to delivery of data accessible from a network location via a communications service provider. Embodiments of the invention are particularly well suited to identifying delivery parameters when the delivery of data is metered, such as when data are delivered to terminals connected to mobile networks.

The invention relates to a mobile-robot route planning method based on an improved genetic algorithm. A raster model is adopted to preprocess a working space of a mobile robot, in a rasterized map, an improved rapid traversing random tree is adopted to generate connections of several clusters between a start point and a target point, portions for the mobile robot to freely walk on in the working space are converted into directed acyclic graphs, and a backtracking method is adopted to generate an initial population which is abundant in diversity and has no infeasible path on the basis of the directed acyclic graphs. Three genetic operators, namely a selection operator, a crossover operator and a mutation operator, are adopted to evolve the population, wherein the selection operator uses a tournament selection strategy, the crossover operator adopts a single-point crossover strategy, and the mutation operator adopts a mutation strategy which displaces an aberrance point with an optimal point in eight-neighbor points of the aberrance point. A quadratic b-spline curve is adopted to smooth an optimal route, and finally, a smooth optimal route is generated. According to the method, the route planning capability of the mobile robot under a complex dynamic environment is effectively improved.

An apparatus for processing information, includes a memory storing a plurality of content items different in type and metadata containing time information of the content items, a cache processor for fetching from the memory the content item and the metadata of the content item to be displayed on a display and storing the fetched content item and the metadata thereof on a cache memory, a display controller for displaying on the display the metadata of the content items from the cache memory arranged in accordance with the time information and a selection operator selecting metadata corresponding to a content item desired to be processed, out of the metadata displayed, and a content processor for fetching from the cache memory a content item corresponding to the metadata selected by the selection operator by referencing the cache memory in response to the selected metadata, and for performing a process responsive to the fetched content item.

A short-term traffic flow forecasting method based on road network space relation constraint Lasso is characterized in that road network constraint is realized by the least absolute shrinkage and selection operator Lasso, road network short-term traffic flow is forecast in three steps of road network analysis, model training and real-time forecasting. The invention provides the short-term traffic flow forecasting method based on the road network space relation constraint Lasso, actually measured expressway traffic flow data are combined, an Lasso algorithm is utilized to forecast the short-term traffic flow, a road network correlation analysis and variable selection method is given, and the expressway short-term traffic flow can be forecast relatively, accurately and rapidly.

The invention discloses a method and a device for route optimization of logistics delivery vehicle, and belongs to the technical field of logistics. The method comprises the following steps of: initializing a congestion matrix alpha and a distance matrix D, generating a delivery route weight matrix omega=alpha D, and initializing a population module N<ZQ>; selecting a population size N<X>, a maximum number of generations N<G>, a crossing-over rate beta, a mutation rate gamma and a number of generations n=0, generating an initial route r1 through a greedy algorithm, and performing mutation operation on the initial route r1 to generate N<ZQ>-1 new routes; calculating fitness A<n> of each route of a first generation population formed by the initial route and the new routes, selecting N<X> routes with the highest fitness from the current population by adopting selection operators, and performing crossover and mutation operations on the N<X> routes to generate a population of next generation; updating n=n+1, when n=N<G>, calculating the fitness A<n> of all the routes in the latest population, and selecting the delivery route with the highest fitness in the current population as the optimal route. According to the invention, when the logistics delivery vehicle delivers goods, the delivery time can be as less as possible, and the delivery route can be as short as possible.

Provided is a single-instruction-set heterogeneous multi-core system static task scheduling method. The method includes five steps: step 1, population initialization; step 2, fitness value calculation; step 3, selection operator operation; step 4, cross operator operation; and step 5 variation operator operation. A local sequence represents an executing sequence of two tasks without depending relations, population initialization efficiency and effective individual are greatly improved, the executing sequence of the tasks is determined through a pre-order-relation matrix, and defects of a traditional height value method are overcome. The method can widen a hunting range of optimal individuals. When the population scale is large enough, a part of optimal solutions missed by the height value method can be found so as to obtain a more optimal scheduling sequence. For the same task set, finishing time of the whole task set is short, power consumption is low, and a purpose of energy conservation and consumption reduction is achieved.

The invention discloses a secondary structure spatial distance constraint-based protein conformation search method. According to the method, feature vectors are formed by utilizing a spatial length ofeach secondary structure in a target protein and information of spatial distance between central residues of every two adjacent secondary structures so as to serve as space limitation conditions under a basic framework of a genetic algorithm, so that a solution space is searched in a relatively small conformation space under the condition that an energy function is given; and meanwhile, the spatial distance information is added in a selection operator, so that the inaccuracy of the energy function is made up and then the accuracy of structure modeling is effectively improved. The invention discloses the secondary structure spatial distance constraint-based protein conformation search method which is relatively high in sampling efficiency, relatively high in prediction accuracy and low incalculation cost.

The invention discloses an improved method for extracting Fourier transformation infrared spectrum characteristic variable of multi-component gas by aid of TR (Tikhonov regularization). The method includes resolving a characteristic variable extracting model into weight sum of the difference of multiple spectral line values; converting an original TR objective function into an objective function based on the model; then adding a bound term of the difference of spectral line positions in the objective function based on the model; realizing optimal functional solution by means of an LASSO (least absolute shrinkage and selection operator) arithmetic based on an Engl's criterion so as to obtain the optimal value of a regressive vector; and obtaining the characteristic variable capable of overcoming interferences caused by spectrum baseline deviation. The accuracy of online multi-component gas analysis results can be improved by the aid of the improved method. The improved method for extracting Fourier transformation infrared spectrum characteristic variable of multi-component gas by aid of TR can be used for multi-component gas quantitative spectrometric analysis application in the fields of gas logging for petroleum and natural gas exploration, quality control and fault diagnosis of products, hardware, chemical engineering and environmental protection.

The invention discloses a method for predicting ship fuel consumption according to sea conditions and manipulating conditions. The method for predicting the ship oil consumption comprises the steps of firstly acquiring a sample data set through data screening, data integration and normalized processing, then building a multivariate linear regression prediction model, defining a cost function by adopting a least shrinkage and selection operator (LASSO algorithm) based on the sample data set, performing variable shrinkage and parameter selection by combining cross validation and a least angle regression (LARS) algorithm, finally solving LASSO estimation by adopting an Osborne dual algorithm, and calculating to acquire the ship fuel consumption. The method provided by the invention for predicting the ship fuel consumption according to sea conditions and manipulating conditions can establish a function relation between the ship fuel consumption amount and each influence factor, solves a problem of multicollinearity in fuel consumption calculation, improves the ship fuel consumption calculation accuracy and has importance significance for energy conservation and emission reduction of traffic on the sea.

The invention relates to a wind farm multi-model draught fan optimized arrangement method based on a genetic algorithm. The method includes the following steps that (1) a wind farm region is divided into square meshes which are the same in size according to the diameter of a draught fan, and an integer matrix which is the same in line and row is generated randomly to be used as the initial solution of the algorithm; (2) the individual fitness value of a current generation is calculated; (3) parent individuals participating in crossover are selected through even random selection operators, and then filial generation individuals are generated by the adoption of improved crossover and mutation operators; (4) repairing operators are introduced to the individuals in a population; (5) a Tabu operator is introduced to an optimal solution of the current generation of the population, the optimal solution is used as the initial solution of a Tabu algorithm, and the neighborhood solution of the optimal solution is searched for; (6) whether the biggest number of iterations is reached or not is judged, if yes, the multi-model draught fan optimized arrangement is completed, and if not, the step (2) is executed again. Compared with the prior art, the wind farm multi-model draught fan optimized arrangement method based on the genetic algorithm has the advantages of being visual in coding mode, good in performance index, high in local search capacity, high in expansibility, high in practicability and the like.

The invention discloses a satellite remote sensing image fusion method comprising that: natural color waveband combined intensity I, a ratio R of near-infrared waveband intensity to the natural color waveband combined intensity I, and a normalized difference vegetation index NDVI are respectively calculated with regard to a multispectral remote sensing image with a near-infrared waveband, a red light waveband, a green light waveband and a blue light waveband, and then a basic enhancement operator k1 is established according to the ratio R. A characteristic selection operator k2 and a characteristic smooth operator k3 are established according to the NDVI. A spectrum integrated compensation coefficient S is formed by the production of k1, k2 and k3, and then each waveband value in the image is multiplied by (1+S) so that the obtained result is the result after fusion of all the wavebands. The near-infrared waveband acts as a data source for enhancement processing, and the enhancement operator with clear physical meaning is designed to perform selective characteristic fusion enhancement on the remote sensing image so that the method is simple, operation is rapid, and the method is applicable to multiband satellite images with near-infrared, red, green, and blue spectral data.

The present invention provides a multi-intelligent robot task distribution method facing a dynamic task which mainly solves the multi-task distribution problem of a task state quantity with time-variant characteristics. The method comprises the steps of: obtaining dynamic task feature parameters, combining intelligent robot ability parameters, and establishing a feature equation of a task point state quantity; according to the feature equation, designing an intelligent robot revenue function; according to the revenue function, designing a genetic algorithm fitness function; further designing agenetic algorithm difference selection operator and a local mutation operator, and providing an algorithm repair strategy; and finally, employing the genetic algorithm to generate an intelligent robot task distribution scheme to complete multi-task distribution. The multi-intelligent robot task distribution method takes obtaining of system maximum return as a target to achieve dynamic multi-taskrapid distribution, solve the algorithm chromosome deadlock problem and avoid that search falls into local optimum, and through a multi-stage distribution strategy, the method can fully deploy intelligent robots in the system to participate in task completion so as to improve the whole efficiency of the system.

The invention discloses a self-adaptive genetic algorithm based on the population evolution process, including the first step, setting the parameters of the BAGA algorithm, setting the number of iterations of the algorithm, the number of populations in each generation, the discrete precision of the independent variable, and the total number of shooting times , a constant; the second step is to use binary code to generate the initial population; the third step is to judge whether the maximum number of iterations is satisfied, and if so, output the optimal individual of the last generation, which is the optimal value found, otherwise turn to the fourth step; The fourth step is to establish the relationship between the objective function and the fitness function, and then calculate the fitness of each individual and the average fitness of contemporary individuals, save the individual with the largest contemporary fitness, and calculate the evolutionary degree of the contemporary population, the degree of population aggregation, and Balance factor, crossover probability and mutation probability; the fifth step, selection, crossover and mutation operations to generate new populations, the selection operator uses roulette technology, the crossover operation uses univariate crossover, and the mutation operation uses basic bit mutation; the sixth step, Find the best individual in the contemporary population, keep it, and then go to the second step.

The invention discloses a formula inputting method for interactive formula typesetting software and belongs to the field of printing and typesetting technology. In the interactive formula typesetting software, the efficiency of formula inputting is low. The method classifies and nominates an operator or a sign, establishes a mapping relation form between the name of the operator or sign and the operator or sign and inputs the name of the operator or sign in a character inputting region of a formula inputting method interface; according to the mapping relation, the operator or sign matched with the input name is displayed in a formula display region of the formula inputting method interface; and the operator or sign is selected and inserted into a type face. The method can rapidly input a formula and improve the efficiency of formula typesetting.

The invention discloses a multi-population genetic algorithm-based power distribution network fault section location algorithm. The algorithm includes the following steps that: 1) the fault current ofa power distribution network containing distributed power sources is coded with binary codes; 2) on the basis of satisfying the requirements of the multi-power source network, a distributed power source switching coefficient is introduced to represent power source switching, the application of the algorithm to a complex power distribution network is considered, a corresponding switching functionis established; 3) the construction of a fitness function is completed for the fault section location problem of the power distribution network according to a fault current code and the switching function of the power distribution network containing the distributed power sources; and 4) a multi-population genetic algorithm (MPGA) is implemented, so that population initialization, control parametersetting, immigration operator, manual selection operator and convergence condition determination are completed. With the algorithm of the invention adopted, the fault section of the power distribution network can be located accurately. The algorithm is suitable for a complex power distribution network containing distributed power sources, and has certain effectiveness and fault tolerance.

The invention relates to a genetic algorithm-improved power grid reactive capacity optimization configuration method based on real-time information of a Wide Area Measurement System (WAMS) and an Energy Management System (EMS) of a power grid. The method includes: step S1. obtaining power grid measurement information from the power grid WAMS in real time; step S2. obtaining from the EMS the power grid structure, parameters and running section information in the EMS of a current time section, and forming an admittance matrix of a whole network containing all load node equivalent admittance; step S3. performing hybrid coding on a control variable of power grid reactive optimization; step S4. establishing a fitness function; step S5. executing a selection operator; step S6. executing a cross and mutation operator; and step S7. determining the rate of convergence and convergence termination criteria. The method in the invention makes improvements on the basis of a traditional genetic algorithm, makes improvements in multiple aspects to improve defects of the traditional genetic algorithm in reactive optimization, improves the rate of convergence and quality of a solution, and prevents the problem of occurrence of a local optimal solution.

The invention discloses a rough set attribute reduction method based on genetic algorithm. The method comprises: (1) obtaining a core of a decision table, and generating an initial population randomly by initialization; (2) controlling the initial population to correspond to the gene position thereof through the attribute core, and calculating a fit value according to fitness function; (3) repairing the population by adopting modification operator; (4) judging whether to satisfy the algorithm terminating condition, if yes, outputting the best individual, and if no, proceeding to (5); (5) performing survival-of-the-fittest operation to individuals in the population by using a selection operator in the genetic algorithm; (6) performing interlace operation to the population with interlace probability by adopting one-point interlace rule; and (7) performing mutation operation to the population according to a heuristic mutation operator with the gene position corresponding to the attribute core not mutated. The technical scheme of the invention is designed to rapidly and efficiently obtain the smallest reduction of attribute in the decision table and efficiently improve information accuracy.

The invention provides a controlled source audio-frequency magnetotellurics one-dimensional inversion method using an improved genetic algorithm. By applying the genetic algorithm to CSAMT one-dimensional data inversion, a target function based on the combination of frequency weighting and phase weighting is proposed, a selection operator based on the combination of fitness value ranking selection and the simulated annealing method is proposed, segmented crossover probability upper and lower limits are proposed, and a crossover operator based on the combination of the self-adaptive crossover probability based on fitness values and a mutation operator based on the combination of fitness and gene weighting are proposed; by using the probability-based optimal individual preservation strategy, the individual with the smallest fitness value is replaced by the individual with the largest fitness value with a certain probability. Compared with the conventional CSAMT data inversion method, the method prevents inversion failure caused by the emergence of ill-conditioned matrixes, can avoid the problems of prematurity and the condition of being liable to be caught in a local extremum in the standard genetic algorithm, and enables chromosomes to move in the optimal solution direction rapidly to find the optimal solution.

The invention relates to a cross-region power system protection communication network planning method, comprising the steps of initializing a first generation of populations through adoption of a variable-length chromosome coding method; setting a fitness function, and evaluating fitness of chromosomes according to features of target populations; selecting parental chromosomes according to the fitness function, specifically, selecting the parental chromosomes through adoption of a selection operator, thereby enabling the parental chromosomes to be inherited by a next generation; carrying out cross processing on the parental chromosomes, and generating new chromosomes, thereby improving diversity of species; carrying out variation processing on the parental chromosomes after the cross processing, thereby improving searching capability of the populations; and combining the parental chromosomes and the newly generated chromosomes, as descendants of a current generation of populations, carrying out a next round of evolution until the preset generation number is realized, exiting a loop, and obtaining a final planning scheme, namely the chromosomes with the highest fitness in the last generation of populations. According to the method, a demand of line protection business for delay can be satisfied.