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MP-EDA: A Robust Estimation of Distribution Algorithm with Multiple Probabilistic Models for Global Continuous Optimization

  • Jing-hui Zhong
  • Jun Zhang
  • Zhun Fan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6457)

Abstract

Extending Estimation of distribution algorithms (EDAs) to the continuous field is a promising and challenging task. With a single probabilistic model, most existing continuous EDAs usually suffer from the local stagnation or a low convergence speed. This paper presents an enhanced continuous EDA with multiple probabilistic models (MP-EDA). In the MP-EDA, the population is divided into two subpopulations. The one involved by histogram model is used to roughly capture the global optima, whereas the other involved by Gaussian model is aimed at finding highly accurate solutions. During the evolution, a migration operation is periodically carried out to exchange some best individuals of the two subpopulations. Besides, the MP-EDA adaptively adjusts the offspring size of each subpopulation to improve the searching efficiency. The effectiveness of the MP-EDA is investigated by testing ten benchmark functions. Compared with several state-of-the-art evolutionary computations, the proposed algorithm can obtain better results in most test cases.

Keywords

Estimation of Distribution Algorithm Evolutionary Computation Histogram Multivariate Gaussian Distribution Global Optimization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jing-hui Zhong
    • 1
    • 2
    • 3
  • Jun Zhang
    • 1
    • 2
    • 3
  • Zhun Fan
    • 4
  1. 1.Dept. of Computer ScienceSUN yat-sen UniversityP.R. China
  2. 2.Ministry of EducationKey Laboratory of Digital Life (Sun Yat-Sen University)P.R. China
  3. 3.Key Laboratory of Software TechnologyEducation Department of Guangdong ProvinceP.R. China
  4. 4.Denmark Technical UniversityDenmark

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