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Soft Computing

, Volume 21, Issue 20, pp 6043–6061 | Cite as

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

  • Tao Lin
  • Hu Zhang
  • Ke Zhang
  • Zhenbiao Tu
  • Naigang Cui
Foundations
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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.

Keywords

Estimation of distribution Adaptive multiobjective optimization Clustering analysis Multivariate Gaussian sampling 
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Notes

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).

Compliance with ethical standards

Conflict of interest

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

Ethical approval

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

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.School of AstronauticsHarbin Institute of TechnologyHarbinChina
  2. 2.Center for Control Theory and Guidance TechnologyHarbin Institute of TechnologyHarbinChina
  3. 3.Beijing Electro-mechanical Engineering InstituteBeijingChina

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