Hybridized moth search algorithm for constrained optimization problems

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

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

14 Citations (Scopus)


In this paper, hybridized moth search algorithm adjusted for tackling constrained optimization problems, is presented. The moth search algorithm is new optimization method. The application of this approach for classic constrained functions was not published in any scientific paper before. By analyzing basic moth search, we noticed some deficiencies. To address these deficiencies, we hybridized the moth search algorithm with artificial bee colony metaheuristics. In order to evaluate robustness, convergence speed and solutions' quality of the hybridized moth search algorithm, we conducted tests on set of 13 classic constrained benchmarks. We performed comparative analysis with the original moth search, and with other algorithms, that proved to be robust and efficient optimization methods. According to the results obtained during experiments, and comparative analysis with other approaches, we came to the conclusion that the hybridized moth search algorithm has potential in dealing with constrained functions optimization.

Original languageEnglish
Title of host publicationProceedings - 2018 International Young Engineers Forum, YEF-ECE 2018
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)9781538615034
Publication statusPublished - 29 May 2018
Event2nd International Young Engineers Forum, YEF-ECE 2018 - Caparica, Portugal
Duration: 4 May 20184 May 2018


Conference2nd International Young Engineers Forum, YEF-ECE 2018


  • constraints handling techniques
  • metaheuristic optimization
  • moth search algorithm
  • swarm intelligence


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