In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) will be discontinued after Dec. 31st, 2024.
See More
Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 12
3670-3673

Improved genetic algorithm using semi-initialization and probabilistic disturbance strategy

Guo Xiaojina,b
Guo Caixinga,b
Bai Linjianga
a. College of Information & Communication Engineering, b. Broadband Network & Information Processing Laboratory, Chongqing University of Posts & Telecommunications, Chongqing 400065, China
DOI: 10.19734/j.issn.1001-3695.2018.09.0608

Abstract

Aimed at the problem that the genetic algorithm is slow in convergence and easy to fall into the local optimal solution in the process of function optimization, this paper proposed a genetic algorithm called DIAGA with semi-initialization and probability perturbation strategy. Firstly, it introduced the probabilistic perturbation strategy to increase the population diversity in the late iteration of the algorithm, and used the semi-initialization fundamentally to change the limitation of the algorithm in the comparison process of the global optimal solution. Then this paper used the Markov chain theory to prove the convergence of DIAGA. Finally, it performed simulation experiments on six standard test functions. Simulation results show that the DIAGA algorithm is more effective in getting rid of local convergence and has more obvious advantages in search accuracy and convergence speed. For multidimensional test functions, the optimization accuracy has increased by about 29%.

Foundation Support

国家自然科学基金资助项目(61671094)
重庆市科委项目(CSTC2015JCYJA40032)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.09.0608
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 12
Section: Algorithm Research & Explore
Pages: 3670-3673
Serial Number: 1001-3695(2019)12-032-3670-04

Publish History

[2019-12-05] Printed Article

Cite This Article

郭晓金, 郭彩杏, 柏林江. 采用半初始化和概率扰动策略改进的遗传算法 [J]. 计算机应用研究, 2019, 36 (12): 3670-3673. (Guo Xiaojin, Guo Caixing, Bai Linjiang. Improved genetic algorithm using semi-initialization and probabilistic disturbance strategy [J]. Application Research of Computers, 2019, 36 (12): 3670-3673. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)