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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Vydáno v:	The Artificial intelligence review Ročník 56; číslo 4; s. 2811 - 2869
Hlavní autoři:	Sahoo, Saroj Kumar, Saha, Apu Kumar, Nama, Sukanta, Masdari, Mohammad
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Dordrecht Springer Netherlands 01.04.2023 Springer Nature B.V
Témata:	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
On-line přístup:	Získat plný text
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