A Novel Nomad Migration-Inspired Algorithm for Global Optimization

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

Abstract

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.

Citation

Lin, N., Fu, L., Zhao, L., Hawbani, A., Tan, Z., Al-Dubai, A., & Min, G. (2022). A Novel Nomad Migration-Inspired Algorithm for Global Optimization. Computers and Electrical Engineering, 100, Article 107862. https://doi.org/10.1016/j.compeleceng.2022.107862

• 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
• DOI: https://doi.org/10.1016/j.compeleceng.2022.107862
• Keywords: Nomad algorithm; Nature-inspired algorithm; Optimizer; Function optimization; Global search
• Public URL: http://researchrepository.napier.ac.uk/Output/2852912