Earticle

초록

영어

Based on the analysis on the basic principles and characteristics of the existing multi-objective genetic algorithm (MOGA), an improved multi-objective GA with elites maintain is put forward based on non-dominated sorting genetic algorithm (NSGA). NSGA-II algorithm theory and parallel hybrid evolutionary theory is described in detail. The design principle, process and detailed implementations of the improved MOGA are given. IMNSGA-II algorithm and NSGA-II algorithm are applied to test the performance of the two algorithms for different test function, experiments of example are preformed. Experimental results show that the improved MOGA achieved the optimal between the convergence and diversity.

목차

Abstract
 1. Introduction
 2. Improved NSGA-II ALGORiTHM
  2.1. NSGA-II Algorithm Theory
  2.2. Parallel Hybrid Evolutionary Theory
 3. Example Experiment
 4. Experimental Results and Analysis
 5. Conclusions
 Acknowledgments
 References

키워드

저자

• Zou Yingyong [ Intelligent Machine Institute, Harbin University of Science and Technology, Harbin 150080, China, Mechanical Engineering College, Changchun University, Changchun 130022, China ]
• Zhang Yongde [ Intelligent Machine Institute, Harbin University of Science and Technology, Harbin 150080, China ] Corresponding author
• Li Qinghua [ Mechanical Engineering College, Changchun University, Changchun 130022, China ]
• Jiang Jingang [ Intelligent Machine Institute, Harbin University of Science and Technology, Harbin 150080, China ]
• Yu Guangbin [ Intelligent Machine Institute, Harbin University of Science and Technology, Harbin 150080, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605