Modified and Hybridized Monarch Butterfly Algorithms for Multi-Objective Optimization

Ivana Strumberger, Eva Tuba, Nebojsa Bacanin, Marko Beko, Milan Tuba

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationHybrid Intelligent Systems - 18th International Conference on Hybrid Intelligent Systems HIS 2018
EditorsNiketa Gandhi, Ana Maria Madureira, Ajith Abraham, Maria Leonilde Varela
Place of PublicationCham
Number of pages10
ISBN (Electronic)978-3-030-14347-3
ISBN (Print)978-3-030-14346-6
Publication statusPublished - 2020
Event18th International Conference on Hybrid Intelligent Systems, HIS 2018 - Porto, Portugal
Duration: 13 Dec 201815 Dec 2018

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


Conference18th International Conference on Hybrid Intelligent Systems, HIS 2018


  • Metaheuristics
  • Monarch butterfly optimization
  • Multi-objective
  • NP hardness
  • Swarm intelligence


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