Evolutionary Algorithm for Single-Objective Optimization Based on Neighborhood Difference and Covariance Information
Author:
Affiliation:

Clc Number:

Fund Project:

National Natural Science Foundation of China (61370102); Guangdong Natural Science Funds for Distinguished Young Scholar (2014A030306050); Ministry of Education-China Mobile Research Funds (MCM20160206); Guangdong High-Level Personnel of Special Support Program (2014TQ01X664); The PhD Start-Up Fund of Dongguan University of Technology (GC300502-3); Higher Education Innovation Strong School Project of Guangdong Province of China (2017KQNCX190)

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    Complex single-objective optimization problem is a hot topic in the field of evolutionary computation. Differential evolution and covariance evolution are considered to be two of the most effective algorithms for this problem, as the difference information similar to the gradient can effectively guide the algorithm towards the optimal solution direction, and the covariance is based on statistics to generate an offspring population. In this paper, the covariance information is introduced to improve the difference operator, then an evolutionary algorithm based on neighborhood difference and covariance information (DEA/NC) is proposed to deal with complex single-objective optimization problem. The two commonly used difference operators generated by random selection individuals and combined by the current optimal solution are analyzed. With the first approach, the difference information cannot be used as a local gradient information to guide the search of the algorithm when the Euclidean distance between randomly selected individuals is large. Meanwhile, the second approach will make the population search in the direction of the current optimal solution, which will lead the population to quickly fall into local optimum. In this paper, a neighborhood difference method is proposed to improve the effectiveness of the differential operator while avoiding the diversity of population loss. In addition, a covariance is introduced to measure the correlation between individual variables, and the correlation is used to optimize the difference operator. Finally, the algorithm tests the single-objective optimization problem in cec2014, and compares the results with the existing differential evolution algorithms. The experimental results show the effectiveness of the proposed algorithm.

    Reference
    Related
    Cited by
Get Citation

李学强,黄翰,郝志峰.基于邻域差分和协方差信息的单目标进化算法.软件学报,2018,29(9):2606-2615

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:April 26,2017
  • Revised:July 10,2017
  • Adopted:September 26,2017
  • Online: November 13,2017
  • Published:
You are the firstVisitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address:4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code:100190
Phone:010-62562563 Fax:010-62562533 Email:jos@iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063