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An adaptive multiobjective estimation of distribution algorithm with a novel Gaussian sampling strategy

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Abstract

In most of existing multiobjective estimation of distribution algorithms (MEDAs), there exist drawbacks: incorrect treatment of population outliers; the loss of population diversity; and too much computational effort being spent on finding an optimal population model. To ease the drawbacks, this paper designs a novel clustering-based multivariate Gaussian sampling strategy and proposes an adaptive MEDA called AMEDA. A clustering analysis approach is utilized in AMEDA to discover the distribution structure of the population. Based on the distribution information, with a certain probability, a local or a global multivariate Gaussian model (MGM) is built for each solution to sample a new solution. A covariance sharing strategy is designed in AMEDA to reduce the complexity of building MGMs, and an adaptive update strategy of the probability that controls the contributions of the two types of MGMs is developed to dynamically balance exploration and exploitation. AMEDA is compared with four representative MOEAs on a number of test instances with complex Pareto fronts and variable linkages. Experimental results suggest that AMEDA outperforms the comparison algorithms on dealing with the test instances. The effectiveness of the clustering-based multivariate Gaussian sampling strategy and the adaptive probability update strategy is also experimentally verified.

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Acknowledgments

The authors would like to thank Dr. Jianyong Sun and Aimin Zhou for their helpful comments and suggestions on the original manuscripts. This study was funded by National Basic Research Program of China (Grant Number: 2012CB821205), Foundation for Creative Research Groups of the National Natural Science Foundation of China (Grant Number: 61021002), National Natural Science Foundation of China (Grant Number: 61174037) and Innovation Funds of China Academy of Space Technology (Grant Number: CAST20120602).

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Authors and Affiliations

  1. School of Astronautics, Harbin Institute of Technology, Harbin, China

    Tao Lin & Naigang Cui

  2. Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin, China

    Hu Zhang

  3. Beijing Electro-mechanical Engineering Institute, Beijing, China

    Ke Zhang & Zhenbiao Tu

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  1. Tao Lin
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  2. Hu Zhang
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Correspondence to Naigang Cui.

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Tao Lin, Hu Zhang, Ke Zhang, Zhenbiao Tu and Naigang Cui all declare that they have no conflict of interest.

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This article does not contain any studies with human participants performed by any of the authors.

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Communicated by A. Di Nola.

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Lin, T., Zhang, H., Zhang, K. et al. An adaptive multiobjective estimation of distribution algorithm with a novel Gaussian sampling strategy. Soft Comput 21, 6043–6061 (2017). https://doi.org/10.1007/s00500-016-2323-7

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