Journal of Northeastern University(Natural Science)

Journal of Northeastern University ›› 2007, Vol. 28 ›› Issue (5): 639-642.DOI: -

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Dynamic problem optimization using the improved primal-dual genetic algorithm

Wang, Hong-Feng (1); Wang, Ding-Wei (1); Liu, Li-Li (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110004, China
  • Received:2013-06-24 Revised:2013-06-24 Online:2007-05-15 Published:2013-06-24
  • Contact: Wang, H.-F.
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Abstract: The PDM (primal-dual mapping) as a key operation in PDGA (primal-dual genetic algorithm) that has successfully been applied to the dynamic 0-1 optimization problems is improved, and a new adaptive PDM scheme is proposed. Then, the statistical information on the allele distribution in each locus over the population is used to calculate the probability of PDM in the corresponding locus. Simulation results from a set of dynamic benchmark problems showed that the improved PDGA outperforms the original algorithm in dynamic environment.

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