Journal ArticleDOI
Moth-flame optimization algorithm
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TLDR
The MFO algorithm is compared with other well-known nature-inspired algorithms on 29 benchmark and 7 real engineering problems and the statistical results show that this algorithm is able to provide very promising and competitive results.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.read more
Citations
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Optimal operation of microgrid with multi-energy complementary based on moth flame optimization algorithm
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A hybridization of grey wolf optimizer and differential evolution for solving nonlinear systems
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Proceedings ArticleDOI
Multilevel thresholding for satellite image segmentation with moth-flame based optimization
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Selection scheme sensitivity for a hybrid Salp Swarm Algorithm: analysis and applications
TL;DR: A hybrid version of the Salp Swarm Algorithm and the hill climbing technique using various selection schemes to solve engineering design problems and produced results that were at least comparable and in many cases superior to SSA and similar algorithms in the literature.
References
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