Modified and Hybridized Monarch Butterfly Algorithms for Multi-Objective Optimization

  • Ivana Strumberger
  • Eva Tuba
  • Nebojsa Bacanin
  • Marko Beko
  • Milan TubaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 923)


This paper presents two improved versions of the monarch butterfly optimization algorithm adopted for solving multi-objective optimization problems. Monarch butterfly optimization is a relatively new swarm intelligence metaheuristic that proved to be robust and efficient method when dealing with NP hard problems. However, in the original monarch butterfly approach some deficiencies were noticed and we addressed these deficiencies by developing one modified, and one hybridized version of the original monarch butterfly algorithm. In the experimental section of this paper we show comparative analysis between the original, and improved versions of monarch butterfly algorithm. According to experimental results, hybridized monarch butterfly approach outperformed all other metaheuristics included in comparative analysis.


Monarch butterfly optimization Swarm intelligence NP hardness Multi-objective Metaheuristics 



This research is supported by the Ministry of Education, Science and Technological Development of Republic of Serbia, Grant No. III-44006.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ivana Strumberger
    • 1
  • Eva Tuba
    • 1
  • Nebojsa Bacanin
    • 1
  • Marko Beko
    • 2
    • 3
  • Milan Tuba
    • 1
    Email author
  1. 1.Singidunum UniversityBelgradeSerbia
  2. 2.COPELABS, Universidade LusófonaLisbonPortugal
  3. 3.CTS/UNINOVALisbonPortugal

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