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An Improved Nonlinear Multi-Objective Optimization Problem Based on Genetic Algorithm

초록

영어

Genetic algorithms for multi-objective optimization problem to be solved were studied. Through the elitist strategy analysis, it is an improved multi-objective optimization algorithm. The algorithm uses a data warehouse to store the optimal solution produced by individuals in each generation, from the way individuals adopt measures to phase out the individual data warehouse identical or similar, the algorithm also improved selection operator, so that the algorithm adaptive capacity enhancement, the new algorithm improves the algorithm performance, improves the quality of understanding between sets, can get a lot of optimal and balanced.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Mathematical Models of Genetic Algorithms
  3.1. Basic Definition of Multi-Objective Optimization
  3.2. Right Weight Coefficient of Genetic Algorithm
 4. System Assessments
  4.1. Fitness Function and Operator
  4.2. Genetic Algorithm Design and Implementation
  4.3. Application Example
 5. Conclusions
 References

저자정보

  • Yali Yun College of Computer and Information Engineering, Luoyang Institute of Science and Technology, Luoyang 471023, China.
  • Yaping Li College of Computer and Information Engineering, Luoyang Institute of Science and Technology, Luoyang 471023, China.

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