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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Journal ArticleDOI
Binary Whale Optimization Algorithm and Binary Moth Flame Optimization with Clustering Algorithms for Clinical Breast Cancer Diagnoses
TL;DR: A hybrid intelligence model that uses the cluster analysis algorithms with bio-inspired algorithms as feature selection for analyzing clinical breast cancer data and the experimental results positively demonstrate that the capability of the proposed bio- inspired feature selection algorithms to produce both meaningful data partitions and significant feature subsets is demonstrated.
Journal ArticleDOI
Backtracking search algorithm with specular reflection learning for global optimization
TL;DR: Experimental results confirm that specular reflection learning is a more effective technique for improving backtracking search algorithm (BSA) compared with opposition-based learning, which establishes the foundation for the applications of Specular reflection learning on other metaheuristics.
Journal ArticleDOI
Quantum-like mutation-induced dragonfly-inspired optimization approach
Caiyang Yu,Zhennao Cai,Xiaojia Ye,Mingjing Wang,Xuehua Zhao,Guoxi Liang,Huiling Chen,Chengye Li +7 more
TL;DR: The results of experimental simulations demonstrate that two introduced strategies can significantly improve the exploitative and exploratory tendencies of the original algorithm, and the convergence speed of the conventional approach has been improved to a large extent.
Journal ArticleDOI
A Moth–Flame Optimization for UPFC–RFB-Based Load Frequency Stabilization of a Realistic Power System with Various Nonlinearities
TL;DR: A nature-inspired novel moth–flame optimization (MFO) algorithm is employed as an optimization tool in the design of proportional–integral–derivative gains and the tunable parameters of UPFC and RFBs to show the superiority of MFO over other similar metaheuristic optimization techniques.
Proceedings ArticleDOI
A Bio-inspired Moth-Flame Optimization Algorithm for Arabic Handwritten Letter Recognition
TL;DR: A new approach based on Moth-flame optimization (MFO) for AHLR, called MFO-AHLR, to improve the accuracy of AHLR with a least number of features, which is the highest among the other published works.
References
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