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A fast elitism Gaussian estimation of distribution algorithm and application for PID optimization.

Estimation of distribution algorithm (EDA) is an intelligent optimization algorithm based on the probability statistics theory. A fast elitism Gaussian estimation of distribution algorithm (FEGEDA) is proposed in this paper. The Gaussian probability model is used to model the solution distribution....

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Published in:Scientific World Journal pp. 597278 - 597279
Main Authors: Xu, Qingyang, Zhang, Chengjin, Zhang, Li
Format: research Journal Article
Published: Wiley-Blackwell 2014
Online Access:View this record in EBSCOhost
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        1537744X
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      jtl: Scientific World Journal
      issn: 1537744X
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      dt: 2014
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1155/2014/597278
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        103826482
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        atl: A fast elitism Gaussian estimation of distribution algorithm and application for PID optimization.
      aug:
        au:
          Xu, Qingyang
          Zhang, Chengjin
          Zhang, Li
        affil: School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, China.
      sug:
        subj:
          Algorithms
          Learning
          Probability
      ab: Estimation of distribution algorithm (EDA) is an intelligent optimization algorithm based on the probability statistics theory. A fast elitism Gaussian estimation of distribution algorithm (FEGEDA) is proposed in this paper. The Gaussian probability model is used to model the solution distribution. The parameters of Gaussian come from the statistical information of the best individuals by fast learning rule. A fast learning rule is used to enhance the efficiency of the algorithm, and an elitism strategy is used to maintain the convergent performance. The performances of the algorithm are examined based upon several benchmarks. In the simulations, a one-dimensional benchmark is used to visualize the optimization process and probability model learning process during the evolution, and several two-dimensional and higher dimensional benchmarks are used to testify the performance of FEGEDA. The experimental results indicate the capability of FEGEDA, especially in the higher dimensional problems, and the FEGEDA exhibits a better performance than some other algorithms and EDAs. Finally, FEGEDA is used in PID controller optimization of PMSM and compared with the classical-PID and GA.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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