Earticle

초록

영어

In this paper we propose a novel differential evolution algorithm based on self-learning, in order to improve the environment adaptive ability of the population in dynamic optimization. The proposed algorithm can monitor the environment changes using re-evaluation of individuals. We direct the population evolution based on the current best individual and another two random individuals, so that the convergence speed is faster and the diversity of the population is maintained. In this way we may reduce the influence from the frequent environment changes. Testing on six dynamic functions, we study the influences caused by period and dimensions. We also compared the proposed algorithm with existing algorithms, the experimental results show that our algorithm has a better environment adaptive ability and achieves better optimization result.

목차

Abstract
 1. Introduction
 2. Dynamic Function Design
 3. Adaptive Differential Evaluation Algorithm
  3.1. Environment Detection Method
  3.2. Individual Self-Learning Method
  3.3. Individual Crossover and Updating
  3.4. Parameter Adaptation
 4. Experimental Results
  4.1. Algorithm Performance under Low Dimension and Dynamic Environment
  4.2. Comparison of Algorithms Performances under High Dimension and Dynamic Environment
 5. Conclusion
 References

키워드

저자

• Feng Guiliang [ 1. School of Information Science and Engineering, Hebei North University,China / 2. Population Health Informazation in Hebei Province Engineering Technology Research Center, China / 3. Medical Informatics in Hebei Universities Application Technology Research and Development Center, China ]
• Cao Ning [ 1. School of Information Science and Engineering, Hebei North University,China / 2. Population Health Informazation in Hebei Province Engineering Technology Research Center, China / 3. Medical Informatics in Hebei Universities Application Technology Research and Development Center, China ]
• Zhang Xiao [ 1. School of Information Science and Engineering, Hebei North University,China / 2. Population Health Informazation in Hebei Province Engineering Technology Research Center, China / 3. Medical Informatics in Hebei Universities Application Technology Research and Development Center, 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