Earticle

초록

영어

In this paper, chaos theory and the traditional multi-objective optimization evolutionary algorithm is put forward, "Chaos-based multi-objective evolutionary algorithm", combines a variety of optimization strategies. The traditional multi-objective evolutionary algorithm for repeating individual causes of variation is based on chaotic analysis of multi-objective evolutionary algorithm and demonstration. According to the characteristics of chaotic map tent, NSGA-II algorithm in this paper on the basis of chaotic map was proposed based on chaotic tent initialization and chaotic mutation multi-objective evolutionary algorithm. The original NSGA-II algorithm is improved, and the introduction of adaptive mutation operator and a new crowding distance is calculated and applied to the design of the algorithm. Analysis and experimental results show that these methods can better improve the distribution of population performance.

목차

Abstract
 1. Introduction
  1.1 Chaotic Mutation
  1.2 Improved Calculation of Crowding Distance
  1.3 Dynamic Mutation Probability
  1.4 Mutation Probability based on the Number of Iterations
  1.5 Algorithm Performance Evaluation
  1.6 Algorithm for the Evaluation Function
  1.7 Algorithm Evaluation Criteria
  1.8 Experimental Results and Analysis of Algorithm Performance
 2. Conclusion
 References

키워드

저자

• Xue Ding [ School of Enrolment and Job-guidance Center, Yunnan Normal University, Kunming, 650500, China ] Corresponding Author
• Chuanxin Zhao [ Information Center, Yunnan Transportation Department, Kunming, 650031, 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