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

Neural computing & applications, 2020-08, Vol.32 (16), p.12427-12452 [Peer Reviewed Journal]

Springer-Verlag London Ltd., part of Springer Nature 2020 ;Springer-Verlag London Ltd., part of Springer Nature 2020. ;ISSN: 0941-0643 ;EISSN: 1433-3058 ;DOI: 10.1007/s00521-019-04696-7

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Title:
A new hybrid algorithm to solve bound-constrained nonlinear optimization problems
Author: Duary, Avijit ; Rahman, Md Sadikur ; Shaikh, Ali Akbar ; Niaki, Seyed Taghi Akhavan ; Bhunia, Asoke Kumar
Subjects: Artificial Intelligence ; Computational Biology/Bioinformatics ; Computational Science and Engineering ; Computer Science ; Computer simulation ; Continuity (mathematics) ; Crossovers ; Data Mining and Knowledge Discovery ; Genetic algorithms ; Image Processing and Computer Vision ; Mutation ; Operators (mathematics) ; Optimization ; Original Article ; Performance indices ; Probability and Statistics in Computer Science
Is Part Of: Neural computing & applications, 2020-08, Vol.32 (16), p.12427-12452
Description: 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.
Publisher: London: Springer London
Language: English
Identifier: ISSN: 0941-0643
EISSN: 1433-3058
DOI: 10.1007/s00521-019-04696-7
Source: ProQuest Central

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

Springer-Verlag London Ltd., part of Springer Nature 2020 ;Springer-Verlag London Ltd., part of Springer Nature 2020. ;ISSN: 0941-0643 ;EISSN: 1433-3058 ;DOI: 10.1007/s00521-019-04696-7

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