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Extending a class of continuous estimation of distribution algorithms to dynamic problems

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Abstract

In this paper, a class of continuous Estimation of Distribution Algorithms (EDAs) based on Gaussian models is analyzed to investigate their potential for solving dynamic optimization problems where the global optima may change dramatically during time. Experimental results on a number of dynamic problems show that the proposed strategy for dynamic optimization can significantly improve the performance of the original EDAs and the optimal solutions can be consistently located.

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Authors and Affiliations

  1. Division of Informatics, Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, People’s Republic of China

    Bo Yuan

  2. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, 4072, Australia

    Maria Orlowska & Shazia Sadiq

Authors
  1. Bo Yuan
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  2. Maria Orlowska
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  3. Shazia Sadiq
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Correspondence to Bo Yuan.

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Yuan, B., Orlowska, M. & Sadiq, S. Extending a class of continuous estimation of distribution algorithms to dynamic problems. Optimization Letters 2, 433–443 (2008). https://doi.org/10.1007/s11590-007-0071-4

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  • DOI: https://doi.org/10.1007/s11590-007-0071-4

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