Improved genetic algorithm for complex computing based on fast matching mechanism

A matching mechanism and complex computing technology, applied in the field of improved genetics, can solve problems such as inability to solve complex optimization, constraints, etc., to reduce the time for algorithm optimization, speed up the search speed, and enhance the effect of diversity

Inactive Publication Date: 2017-01-11
JINGDEZHEN CERAMIC INSTITUTE
View PDF0 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide an improved genetic method based on a fast matching mechanism for complex calculations, aiming to solve the problem that traditi

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Improved genetic algorithm for complex computing based on fast matching mechanism
  • Improved genetic algorithm for complex computing based on fast matching mechanism
  • Improved genetic algorithm for complex computing based on fast matching mechanism

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment approach

[0116] The fast matching genetic algorithm proposed in the present invention is referred to as FGA for short, and the standard genetic algorithm used for comparison is referred to as SGA for short, and the test function is described as follows:

[0117] Table 3 Test function

[0118]

[0119] Each chromosome uses 8-bit binary 0-1 encoding, therefore, the weight coefficient of each gene w i The normalization process is performed according to the position code of the corresponding gene bit in the chromosome, as shown in Table 4:

[0120] Table 4 Weight coefficient table

[0121]

[0122] The parameter settings used in the two test functions of the FGA and SGA algorithms are shown in Table 5.

[0123] Table 5 Parameter settings of FGA and SGA in different test functions

[0124]

[0125] In order to compare the performance difference between the two algorithms of FGA and SGA, the method of the present invention inspects three important moments in the entire algorithm...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses an improved genetic method for complex computing based on a fast matching mechanism. A three-dimensional feature vector is obtained through feature extraction; during a population evolution process, a built similarity matching evaluation mechanism is used for calculating the fitness value and the confidence value of each chromosome; the chromosome is built to a spatial simplex through a simplex mutation operator; after equivalent effect space expansion at a certain proportion is carried out, mutated individuals are taken out according to a sequence, and corresponding mutation operation is carried out; and finally, in the late evolution stage, an optimal solution or an approximate optimal solution is generated according to a set stop condition. The individual evaluation times during the evolution process can be reduced, the algorithm optimal solution search speed is quickened, the method is intuitive, clear and universal, and the algorithm optimization time can be greatly reduced.

Description

technical field [0001] The invention belongs to the technical field of genetic algorithms, in particular to an improved genetic method based on a fast matching mechanism for complex calculations. Background technique [0002] At present, the genetic algorithm (Genetic algorithm, GA) is a stochastic optimization search method that draws lessons from the evolution of the biological survival competition selection law mechanism in nature. Currently, GA has been widely used in various system engineering optimization problems (Liang Y, Leung K S. Genetic algorithm with adaptive elitist-population strategies for multimodal function optimization. Applied SoftComputing, 2011, 11 (2): 2017-2034) . However, when this population-based stochastic optimization method is applied to large-scale complex system optimization problems (such as high-dimensional multi-objective optimization problems, dynamic optimization problems), it often costs more time to obtain better accurate solutions , ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06N3/12
CPCG06N3/126
Inventor 汤可宗于保春舒云
Owner JINGDEZHEN CERAMIC INSTITUTE
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

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap