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

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 th...

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Vydané v:	Knowledge-based systems Ročník 89; s. 228 - 249
Hlavný autor:	Mirjalili, Seyedali
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.11.2015
Predmet:	Constrained optimization Meta-heuristic Optimization Population-based algorithm Stochastic optimization Meta-heuristic Stochastic optimization Optimization Constrained optimization Population-based algorithm
ISSN:	0950-7051, 1872-7409
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

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.
AbstractList	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.
Author	Mirjalili, Seyedali
Author_xml	– sequence: 1 givenname: Seyedali surname: Mirjalili fullname: Mirjalili, Seyedali email: seyedali.mirjalili@griffithuni.edu.au organization: School of Information and Communication Technology, Griffith University, Nathan Campus, Brisbane, QLD 4111, Australia
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Snippet	In this paper a novel nature-inspired optimization paradigm is proposed called Moth-Flame Optimization (MFO) algorithm. The main inspiration of this optimizer...
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SubjectTerms	Constrained optimization Meta-heuristic Optimization Population-based algorithm Stochastic optimization
Title	Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm
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