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A Novel Nomad Migration-Inspired Algorithm for Global Optimization

Lin, Na; Fu, Luwei; Zhao, Liang; Hawbani, Ammar; Tan, Zhiyuan; Al-Dubai, Ahmed; Min, Geyong


Na Lin

Luwei Fu

Liang Zhao

Ammar Hawbani

Geyong Min


Nature-inspired computing (NIC) has been widely studied for many optimization scenarios. However, miscellaneous solution space of real-world problem causes it is challenging to guarantee the global optimum. Besides, cumbersome structure and complex parameters setting-up make the existed algorithms hard for most users who are not specializing in NIC, to understand and use. To alleviate these limitations, this paper devises a succinct and efficient optimization algorithm called Nomad Algorithm (NA). It is inspired by the migratory behaviour of nomadic tribes on the prairie. Extensive experiments are implemented with respects to accuracy, rate, stability, and cost of optimization. Mathematical proof is given to guarantee the global convergence, and the nonparametric tests are conducted to confirm the significance of experiment results. The statistical results of optimization accuracy denote NA outperforms its rivals for most cases (23/28) by orders of magnitude significantly. It is considered as a promising optimizer with excellent efficiency and adaptability.

Journal Article Type Article
Acceptance Date Feb 24, 2022
Online Publication Date Mar 17, 2022
Publication Date 2022-05
Deposit Date Mar 10, 2022
Publicly Available Date Mar 18, 2023
Journal Computers and Electrical Engineering
Print ISSN 0045-7906
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 100
Article Number 107862
Keywords Nomad algorithm; Nature-inspired algorithm; Optimizer; Function optimization; Global search
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