Patents
Literature
Patsnap Copilot is an intelligent assistant for R&D personnel, combined with Patent DNA, to facilitate innovative research.
Patsnap Copilot

61results about How to "Fast evolution" patented technology

Method for a connection through a core network

In a method for the establishment of a connection between a first node (RNC1) and a second node (RNC2), both are connectable to a core network which comprises interconnected core network nodes (CN1, CN2). The first node (RNC1) signals a call request to a first core network node (CN1). If the first node is an access node, it sends an initialization according to a framing protocol, wherein a set of parameters for the framing of information sent over the interface between the first access node (RNC1) and the first core network node (CN1). If the first node is no access node, a first control server (MSC1) defines said set of parameters. The set of parameters is transmitted to the first core network node (CN1) and the first core network node (CN1) stores the parameter set. The first core network node (CN1) initializes the connection (Co) to a further core network node according to said protocol and the further core network node stores the parameter set. The initialization of the connection (Co) to and the storing of parameters in further core network nodes is performed stepwise until a final core network node (CN2) is reached which is connectable to the second access node (RNC2). The final core network node (CN2) initializes the connection (Co) to the second access node (RNC2) according to said protocol and the second access node (RNC2) stores the parameter set. A core network node and a program unit embodying the invention are also described.
Owner:TELEFON AB LM ERICSSON (PUBL)

Adaptive genetic algorithm based on population evolution process

InactiveCN106934459AIncrease diversityFast global search capabilityGenetic algorithmsAlgorithmSelection operator
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.
Owner:NORTHWESTERN POLYTECHNICAL UNIV

Optimization method for heat-engine plant thermal on-line process identification and control algorithm based on dual-objective parallel ISLAND-HFC mixed model genetic programming algorithm

ActiveCN105487496AStrong anti-premature astringencyFast evolutionEnergy industryTotal factory controlField testsPID controller
The invention discloses an optimization method for a heat-engine plant thermal on-line process identification and control algorithm based on a dual-objective parallel ISLAND-HFC mixed model genetic programming algorithm. The steps comprise: 1, establishing a hardware platform; 2, the optimization method being completed by executing foreground interface software and background software by the hardware platform, the background software being formed by field test and data acquisition software, process identification software, and PID controller parameter optimization software. The method makes an ISLAND model and a HFC model organically combine together, anti-prematurity convergence property is good, multi-core CPU resources of an industrial control computer are fully used, multithreading run concurrently, evolution speed is fast, and the method is suitable to solve comprehensive problems. On one hand, dual-objective evolution controls errors between an evolution model and an ideal model, and on the other hand, the structure of an evolution individual is controlled, and finally, optimal individual structure and parameters satisfy requirements. For process identification, a field process model is accurately matched, and for parameter optimization of a PID controller, optimal proportion, integral, and differential parameters are obtained.
Owner:STATE GRID HEBEI ENERGY TECH SERVICE CO LTD +2

BESO topological optimization method based on dynamic evolution rate and adaptive grid and application of BESO topological optimization method

The invention discloses a BESO topological optimization method based on a dynamic evolution rate and an adaptive grid and application thereof, and the method comprises the steps: building a finite element model for a to-be-topologically optimized basic structure, and defining a design domain, a load, a boundary condition and a grid size; determining a constraint value and BESO necessary parameters; performing finite element analysis on the structure after mesh division, and calculating unit sensitivity under a target function and a constraint condition; filtering the unit sensitivity and updating the constrained Lagrange multiplier, and constructing the sensitivity of a Lagrange function; determining an evolution rate of the current iterative step based on a dynamic evolution rate functionof a Logistic function according to the volume rate of the current iterative step; and updating a design variable according to a set constraint function, judging whether constraint conditions and convergence conditions are met or not, if not, performing grid adaptive updating, then performing unit updating, and stopping iteration until the constraint conditions and the convergence conditions aremet. According to the invention, the calculation amount of single finite element analysis and the number of iterations required by topological optimization are effectively reduced while high calculation precision is ensured, so that the total calculation time consumption of topological optimization is greatly reduced.
Owner:GUANGZHOU UNIVERSITY

Dynamic evolution rate BESO topological optimization method based on arc tangent and application thereof

The invention discloses a dynamic evolution rate BESO topological optimization method on arc tangent and an application thereof. The dynamic evolution rate BESO topological optimization method comprises the steps: firstly, for a basic structure needing topological optimization, carrying out the discrete design of a domain through a finite element grid under a given boundary and loading condition,and obtaining a finite element model; determining initialized related parameters; executing finite element analysis for the finite element model, calculating the comprehensive sensitivity of each unitof the finite element model, and constructing the current dynamic evolution rate of the finite element model according to an arc tangent dynamic evolution rate function; determining the number of units needing to be updated in the current iterative step of the finite element model according to the current dynamic evolution rate of the finite element model; updating and optimizing the finite element model according to the number of units to be updated and the comprehensive sensitivity; and if the structure of the currently updated finite element model meets a constraint condition or a convergence condition, ending the optimization, and outputting an optimization model. According to the invention, the topology optimization of the basic structure can be accelerated by using the dynamic evolution rate, and the efficiency and flexibility of topology optimization are improved.
Owner:GUANGZHOU UNIVERSITY

User preference-based dynamic computing migration method and device for smart city

ActiveCN112214301ATo achieve the purpose of multi-objective optimizationFast convergenceProgram initiation/switchingResource allocationIntelligent citySimulation
The invention provides a user preference-based dynamic computing migration method and device for a smart city, and the method comprises the steps of initializing a set of input tasks; stipulating an algorithm stop standard, a population maximum iteration frequency, the number of neighborhood vector sets of each particle and a population initial migration strategy, and defining a group of weight vector sets required to be used in the algorithm; then, on the basis of an MOEA/D algorithm, continuously updating a migration strategy of the task by taking optimization of total energy consumption andtotal time delay of the mobile equipment task of the user side from generation to completion as a target; meanwhile, in order to meet the requirements of the user, adding an elitist strategy which can be changed in a directed mode according to the requirements and preferences of the user; according to the invention, an elitist strategy is adopted, energy consumption and time delay generated by task processing are comprehensively considered while user preferences are met, an appropriate calculation migration strategy is formulated for user tasks in an MEC environment, and the purpose of multi-objective optimization is achieved.
Owner:HUAQIAO UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products