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...

Full description

Saved in:
Bibliographic Details
Published in:Knowledge-based systems Vol. 89; pp. 228 - 249
Main Author: Mirjalili, Seyedali
Format: Journal Article
Language:English
Published: Elsevier B.V 01.11.2015
Subjects:
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
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2015.07.006