earticle

논문검색

Improved Multi-objective Genetic Algorithm Based on Parallel Hybrid Evolutionary Theory

원문정보

Zou Yingyong, Zhang Yongde, Li Qinghua, Jiang Jingang, Yu Guangbin

보안공학연구지원센터(IJHIT) International Journal of Hybrid Information Technology Vol.8 No.1 2015.01 pp.133-140
피인용수 : 0건 (자료제공 : 네이버학술정보)

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

저자정보

  • 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
  • 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

자료제공 : 네이버학술정보

    함께 이용한 논문

      ※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.

      0개의 논문이 장바구니에 담겼습니다.