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Multi-population Based Univariate Marginal Distribution Algorithm for Dynamic Optimization Problems

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Abstract

Many real-world problems are dynamic optimization problems in which the optimal solutions need to be continuously tracked over time. In this paper a multi-population based univariate marginal distribution algorithm (MUMDA) is proposed to solve dynamic optimization problems. The main idea of the algorithm is to construct several probability models by dividing the population into several parts. The objective is to divide the search space into several regions to maintain the diversity. Concretely, MUMDA uses one probability vector to do the search in the promising areas identified previously, and uses other probability vectors to search for new promising optimal solutions. Moreover the convergence of univariate marginal distribution algorithm (UMDA) is proved, which can be used to analyze the validity of the proposed algorithm. Finally, the experimental study was carried out to compare the performance of several UMDA, and the results show that the MUMDA is effective and can be well adaptive to the dynamic environments rapidly.

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Authors and Affiliations

  1. School of Science, Xidian University, Xi’an, 710071, China

    Yan Wu

  2. School of Computer Science and Technology, Xidian University, Xi’an, 710071, China

    Yuping Wang

  3. College of Automation, Northwestern Polytechnical University, Xi’an, 710072, China

    Xiaoxiong Liu

Authors
  1. Yan Wu
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  2. Yuping Wang
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  3. Xiaoxiong Liu
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Correspondence to Yan Wu.

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Wu, Y., Wang, Y. & Liu, X. Multi-population Based Univariate Marginal Distribution Algorithm for Dynamic Optimization Problems. J Intell Robot Syst 59, 127–144 (2010). https://doi.org/10.1007/s10846-009-9392-0

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  • DOI: https://doi.org/10.1007/s10846-009-9392-0

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