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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 16/2020

02.01.2020 | Original Article

A new hybrid algorithm to solve bound-constrained nonlinear optimization problems

verfasst von: Avijit Duary, Md Sadikur Rahman, Ali Akbar Shaikh, Seyed Taghi Akhavan Niaki, Asoke Kumar Bhunia

Erschienen in: Neural Computing and Applications | Ausgabe 16/2020

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Abstract

The goal of this work is to propose a hybrid algorithm called real-coded self-organizing migrating genetic algorithm by combining real-coded genetic algorithm (RCGA) and self-organizing migrating algorithm (SOMA) for solving bound-constrained nonlinear optimization problems having multimodal continuous functions. In RCGA, exponential ranking selection, whole-arithmetic crossover and non-uniform mutation operations have been used as different operators where as in SOMA, a modification has been done. The performance of the proposed hybrid algorithm has been tested by solving a set of benchmark optimization problems taken from the existing literature. Then, the simulated results have been compared numerically and graphically with existing algorithms. In the graphical comparison, a modified performance index has been proposed. Finally, the proposed algorithm has been applied to solve two real-life problems.
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Metadaten
Titel
A new hybrid algorithm to solve bound-constrained nonlinear optimization problems
verfasst von
Avijit Duary
Md Sadikur Rahman
Ali Akbar Shaikh
Seyed Taghi Akhavan Niaki
Asoke Kumar Bhunia
Publikationsdatum
02.01.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 16/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04696-7

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