Moths–Flame Optimization Algorithm

Modestus O. Okwu, Lagouge K. Tartibu

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Citation (Scopus)


Moth–flame optimization (MFO) algorithm is a simple and easy to implement, nature-inspired, meta-heuristic algorithm. This chapter covers the introduction to moth flame optimization algorithm, steps for generating moths randomly within the neighbourhood or solution space and solved numerical example are provided. The fitness value for each moth is calculated and the best position obtained is tagged by flame. The updating process takes place after which the process is repeated until a point where criteria for termination is attained. The MFO model was implemented in MATLAB, to illustrate the approach, 30 search agents were considered (n = 30) and the maximum number of iterations were set to 100. The best solution obtained by MFO [1.7999 0.19996] and the best optimal value of the objective function found by MFO is 84. MFO algorithm can be used in diverse areas of operation for scheduling, estimation, simulation, and control.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages9
Publication statusPublished - 2021

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

ASJC Scopus subject areas

  • Artificial Intelligence


Dive into the research topics of 'Moths–Flame Optimization Algorithm'. Together they form a unique fingerprint.

Cite this