Earticle

초록

영어

In order to improve the convergence and diversity of multiobjective optimization algorithms, the harmonic average distance is employed to improve the aggregating function combined L-rank value. Selection model and searching scheme of artificial bee colony algorithm and diversity maintaining scheme are improved in this paper. This novel many objectives optimization method based on improved artificial bee colony algorithm (ABC) in this paper is compared with other three many objectives optimization methods on 3 to 8 objectives DTLZ. Simulation results show that the proposed algorithm is superior to other algorithms in the diversity and convergence of solutions.

목차

Abstract
 1. Introduction
 2. Improved Methods in ABC
  2.1. Improved Fitness Evaluation Method
  2.2. Improved Selection Mode of Onlookers
  2.3. Forced Mutation Operation of the Employed Bee
  2.4. Diversity Maintaining Scheme
 3. Steps of the Proposed Algorithm
 4. Experimental Results
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Wu Chunming [ School of Information Engineering, Northeast Dianli University, Jilin, China ]
• Li Tingting [ School of Information Engineering, Northeast Dianli University, Jilin, China ]
• Wang Yanjiao [ School of Information Engineering, Northeast Dianli University, Jilin, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605