An improved moth flame optimization algorithm based on modified dynamic opposite learning strategy

Moth flame optimization (MFO) algorithm is a relatively new nature-inspired optimization algorithm based on the moth’s movement towards the moon. Premature convergence and convergence to local optima are the main demerits of the algorithm. To avoid these drawbacks, a modified dynamic opposite learni...

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Veröffentlicht in:	The Artificial intelligence review Jg. 56; H. 4; S. 2811 - 2869
Hauptverfasser:	Sahoo, Saroj Kumar, Saha, Apu Kumar, Nama, Sukanta, Masdari, Mohammad
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.04.2023 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Computer Science Convergence Design engineering Learning Learning strategies Machine learning Measurement Measures Optimization Optimization algorithms Performance enhancement Rank tests Robustness Robustness (mathematics) Statistical tests Tests World problems Benchmark functions Dynamic opposite learning strategy Swarm intelligence Moth flame optimization algorithm Simple quadratic interpolation
ISSN:	0269-2821, 1573-7462
Online-Zugang:	Volltext
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