Improved Nelder-Mead algorithm in high dimensions with adaptive parameters based on Chebyshev spacing points

In spite of being one of the most popular optimization methods, Nelder-Mead's simplex search algorithm with the default choice of parameters performs poorly on high-dimensional problems. The work presented here concerns such values of the Nelder-Mead algorithm's parameters that help improv...

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Vydané v:Engineering optimization Ročník 52; číslo 10; s. 1814 - 1828
Hlavný autor: Mehta, V.K.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Abingdon Taylor & Francis 02.10.2020
Taylor & Francis Ltd
Predmet:
Adaptive algorithms
adaptive parameters
Algorithms
Chebyshev approximation
Chebyshev spacing points
convergence
high-dimensional problems
Nelder-Mead algorithm
Optimization
Parameters
Search algorithms
ISSN:0305-215X, 1029-0273
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Shrnutí:In spite of being one of the most popular optimization methods, Nelder-Mead's simplex search algorithm with the default choice of parameters performs poorly on high-dimensional problems. The work presented here concerns such values of the Nelder-Mead algorithm's parameters that help improve the convergence and success rate of the algorithm in high dimensions. In this work, a novel way of assigning parameters to the Nelder-Mead simplex search algorithm is proposed. The proposed scheme is based on Chebyshev spacing points and adapts itself to the dimension of the problem. The numerical experiments conducted for this study show that the proposed scheme is better not just in comparison with the original Nelder-Mead algorithm but it outperforms the other existing adaptive schemes as well.
Bibliografia:ObjectType-Article-1
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ISSN:0305-215X
1029-0273
DOI:10.1080/0305215X.2019.1688315