Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Pareto set individual ranking method aiming at high-dimensional multi-objective optimization problem

A technology of target optimization and sorting method, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as difficult to obtain accurate preference weights, and achieve the effect of simple and effective calculation

Inactive Publication Date: 2015-07-15
NAT UNIV OF DEFENSE TECH
View PDF0 Cites 10 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Its defects mainly include: 1) It is difficult for the decision-maker to have a comprehensive understanding of the problem, so it is difficult to obtain accurate preference weights; 2) Once the preference of the decision-maker changes, the optimization search can only be performed again

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
  • Pareto set individual ranking method aiming at high-dimensional multi-objective optimization problem
  • Pareto set individual ranking method aiming at high-dimensional multi-objective optimization problem
  • Pareto set individual ranking method aiming at high-dimensional multi-objective optimization problem

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0034] Assume that in the current search process, a set of solution sets P has been obtained, and its objective function value is {(0.5,4),(1.5,3),(2,2),(3,1),(4,0.5), (2,3.5),(3.5,2),(3,3),(3,3.5),(4,3)}(N=10), such as figure 2 As shown, take this as an example below to introduce the specific implementation steps of the Pareto set individual sorting method for the high-dimensional multi-objective optimization problem of the present invention:

[0035] Step 1. Normalize the current solution set population to the interval [0,1] according to formula (1). Here max(f i )=3.5,min(f i )=1, the resulting normalized individual objective function value data are: {(0,1),(0.2587,0.7143),(0.4286,0.4286),(0.7143,0.1429),(1,0),( 0.4286, 0.8571), (0.8571, 0.4286), (0.7143, 0.7143) (1.0000, 0.7143), (0.7143, 0.8571)}.

[0036] Step 2. Perform non-inferiority stratification on individuals. The specific method is as follows. First, all non-dominated individuals are assigned to the first la...

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 a Pareto set individual ranking method aiming at the high-dimensional multi-objective optimization problem. According to the method, when the qualities of population individuals are compared, a new reference point population is introduced; by comparing the Pareto dominance relation between the population individual and a reference point, the quality relation among the population individuals, namely the individual fitness degree, is indirectly obtained; meanwhile, the reference point also obtains a fitness degree value according to the dominance relation between the reference point and the population individuals; therefore in the process of designing and evolving a multi-objective optimization algorithm, the individual population and a reference point population are subjected to concerdent evolution and are mutually promoted, and eventually the convergence of the algorithm is improved; the experience shows that the method is still effective in the high-dimensional multi-objective optimization problem.

Description

technical field [0001] The invention belongs to the field of artificial intelligence, and in particular relates to the field of intelligent optimization algorithm design. Specifically, it is a method for sorting the individual advantages and disadvantages of Pareto sets for multi-objective optimization problems. It is suitable for various multi-objective intelligent optimization algorithms, such as multi-objective genetic algorithm, multi-objective particle swarm algorithm, and ant colony algorithm. Wait. Background technique [0002] Many decision-making optimization problems in daily life, scientific research and engineering practice, such as urban area division, network optimization, job scheduling, etc., involve the optimization of multiple objectives, which are called multi-objective optimization problems (Multi-objective Optimization Problem, MOP) . Usually in MOP, multiple optimization objectives are coupled together and in a state of mutual competition, that is, th...

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): G06F19/00
Inventor 王锐史志超张涛刘亚杰雷洪涛张福兴查亚兵
Owner NAT UNIV OF DEFENSE TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com