Hybrid biogeography-based optimization with enhanced mutation and CMA-ES for global optimization problem

  • Fuqing ZhaoEmail author
  • Songlin Du
  • Yi Zhang
  • Weimin Ma
  • Houbin Song
Original Research Paper


In recent years, scheduling problems have attracted enormous attentions from practitioners and researches in manufacturing systems, for instance, the scheduling of computing resource in cloud infrastructure and cloud services. The scheduling problems in cloud services, big data and other service-oriented computing problems are regarded as non-separable problems. In this paper, a hybrid biogeography-based optimization with the enhanced mutation operator and CMA-ES (HBBO-CMA) is proposed to enhance the ability of exploitation on non-separable problems and alleviate the rotational variance. In the migration operator, the rotationally invariant migration operator is designed to reduce the dependence of BBO on the coordinate system and control the diversity of population. In the mutation operator, an enhanced mutation operator, which is sampled from the mean value and stand deviation of the variables of population, is employed to effectively escape the local optimum. Furthermore, the CMA-ES, which has outstanding performance on the non-separable problem, is applied to extend the exploitation of HBBO-CMA. Experimental results on CEC-2017 demonstrated the effectiveness of the proposed HBBO-CMA.


Biogeography-based optimization CMA-ES Non-separable problems Enhanced mutation operator Rotational variance 



This work was financially supported by the National Natural Science Foundation of China under Grant Numbers 61663023. It was also supported by the Key Research Programs of Science and Technology Commission Foundation of Gansu Province (2017GS10817), Lanzhou Science Bureau project (2018-rc-98), Zhejiang Provincial Natural Science Foundation (LGJ19E050001), Wenzhou Public Welfare Science and Technology project (G20170016), respectively.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2020

Authors and Affiliations

  1. 1.School of Computer and Communication TechnologyLanzhou University of TechnologyLanzhouChina
  2. 2.School of Mechanical EngineeringXijin UniversityXi’anChina
  3. 3.School of Economics and ManagementTongji UniversityShanghaiChina

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