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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 7/2020

18.12.2019 | Original Article

Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm

verfasst von: Ali Wagdy Mohamed, Anas A. Hadi, Ali Khater Mohamed

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 7/2020

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Abstract

This paper proposes a novel nature-inspired algorithm called Gaining Sharing Knowledge based Algorithm (GSK) for solving optimization problems over continuous space. The GSK algorithm mimics the process of gaining and sharing knowledge during the human life span. It is based on two vital stages, junior gaining and sharing phase and senior gaining and sharing phase. The present work mathematically models these two phases to achieve the process of optimization. In order to verify and analyze the performance of GSK, numerical experiments on a set of 30 test problems from the CEC2017 benchmark for 10, 30, 50 and 100 dimensions. Besides, the GSK algorithm has been applied to solve the set of real world optimization problems proposed for the IEEE-CEC2011 evolutionary algorithm competition. A comparison with 10 state-of-the-art and recent metaheuristic algorithms are executed. Experimental results indicate that in terms of robustness, convergence and quality of the solution obtained, GSK is significantly better than, or at least comparable to state-of-the-art approaches with outstanding performance in solving optimization problems especially with high dimensions.
Vorheriger Artikel An online PLA algorithm with maximum error bound for generating optimal mixed-segments
Nächster Artikel Homo-ELM: fully homomorphic extreme learning machine

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Metadaten
Titel
Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm
verfasst von
Ali Wagdy Mohamed
Anas A. Hadi
Ali Khater Mohamed
Publikationsdatum
18.12.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 7/2020
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-019-01053-x

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