Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm

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874 Citations (Scopus)

Abstract

In this paper a novel nature-inspired optimization paradigm is proposed called Moth-Flame Optimization (MFO) algorithm. The main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a useless/deadly spiral path around artificial lights. This paper mathematically models this behaviour to perform optimization. The MFO algorithm is compared with other well-known nature-inspired algorithms on 29 benchmark and 7 real engineering problems. The statistical results on the benchmark functions show that this algorithm is able to provide very promising and competitive results. Additionally, the results of the real problems demonstrate the merits of this algorithm in solving challenging problems with constrained and unknown search spaces. The paper also considers the application of the proposed algorithm in the field of marine propeller design to further investigate its effectiveness in practice. Note that the source codes of the MFO algorithm are publicly available at http://www.alimirjalili.com/MFO.html.

Original languageEnglish
Pages (from-to)228-249
Number of pages22
JournalKnowledge-Based Systems
Volume89
DOIs
Publication statusPublished - 1 Nov 2015
Externally publishedYes

Keywords

  • Constrained optimization
  • Meta-heuristic
  • Optimization
  • Population-based algorithm
  • Stochastic optimization

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