An Improved Monarch Butterfly Optimization with Equal Partition and F/T Mutation

  • Gai-Ge WangEmail author
  • Guo-Sheng Hao
  • Shi Cheng
  • Zhihua Cui
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10385)


In general, the population of most metaheuristic algorithms is randomly initialized at the start of search. Monarch Butterfly Optimization (MBO) with a randomly initialized population, as a kind of metaheuristic algorithm, is recently proposed by Wang et al. In this paper, a new population initialization strategy is proposed with the aim of improving the performance of MBO. Firstly, the whole search space is equally divided into NP (population size) parts at each dimension. And then, in order to add the diversity of the initialized population, two random distributions (T and F distribution) are used to mutate the equally divided population. Accordingly, five variants of MBOs are proposed with new initialization strategy. By comparing five variants of MBOs with the basic MBO algorithm, the experimental results presented clearly demonstrate five variants of MBOs have much better performance than the basic MBO algorithm.


Benchmark Monarch butterfly optimization Equal partition F mutation T mutation 



This work was supported by the Natural Science Foundation of Jiangsu Province (No. BK20150239) and National Natural Science Foundation of China (No. 61503165 and No. 61673196).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Gai-Ge Wang
    • 1
    Email author
  • Guo-Sheng Hao
    • 1
  • Shi Cheng
    • 2
  • Zhihua Cui
    • 3
  1. 1.School of Computer Science and TechnologyJiangsu Normal UniversityXuzhouChina
  2. 2.School of Computer ScienceShaanxi Normal UniversityXi’anChina
  3. 3.Complex System and Computational Intelligence LaboratoryTaiyuan University of Science and TechnologyTaiyuanChina

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