Applied Intelligence

, Volume 49, Issue 2, pp 513–531 | Cite as

Surrogate model assisted cooperative coevolution for large scale optimization

  • Zhigang RenEmail author
  • Bei Pang
  • Muyi Wang
  • Zuren Feng
  • Yongsheng Liang
  • An Chen
  • Yipeng Zhang


It has been shown that cooperative coevolution (CC) can effectively deal with large scale optimization problems (LSOPs) through a ‘divide-and-conquer’ strategy. However, its performance is severely restricted by the current context-vector-based sub-solution evaluation method, since this method needs to invoke the original high dimensional simulation model when evaluating each sub-solution, thus requiring many computation resources. To alleviate this issue, this study proposes a novel surrogate model assisted cooperative coevolution (SACC) framework. SACC constructs a surrogate model for each sub-problem and employs it to evaluate corresponding sub-solutions. The original simulation model is only adopted to reevaluate a small number of promising sub-solutions selected by surrogate models, and these really evaluated sub-solutions will in turn be employed to update surrogate models. By this means, the computation cost could be greatly reduced without significantly sacrificing evaluation quality. By taking the radial basis function (RBF) and the success-history based adaptive differential evolution (SHADE) as surrogate model and optimizer, respectively, this study further designs a concrete SACC algorithm named RBF-SHADE-SACC. RBF and SHADE have only been proved to be effective on small and medium scale problems. This study scales them up to LSOPs under the SACC framework, where they are tailored to a certain extent for adapting to the characteristics of LSOPs and SACC. Empirical studies on IEEE CEC 2010 benchmark functions demonstrate that SACC can significantly enhance the sub-solution evaluation efficiency, and even with much fewer computation resources, RBF-SHADE-SACC can find much better solutions than traditional CC algorithms.


Cooperative coevolution (CC) Large scale optimization problem (LSOP) Surrogate model Radial basis function (RBF) Success-history based adaptive differential evolution (SHADE) 



This work was supported in part by the National Natural Science Foundation of China under Grant 61873199, in part by the Postdoctoral Science Foundation of China under Grants 2014M560784 and 2016T90922, and in part by the Fundamental Research Funds for the Central Universities of China.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Zhigang Ren
    • 1
    • 2
    Email author
  • Bei Pang
    • 1
  • Muyi Wang
    • 1
  • Zuren Feng
    • 2
  • Yongsheng Liang
    • 1
  • An Chen
    • 1
  • Yipeng Zhang
    • 1
  1. 1.Autocontrol Institute, School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’anChina
  2. 2.State Key Laboratory for Manufacturing Systems Engineering, School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’anChina

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