A novel nomad migration-inspired algorithm for global optimization. (May 2022)

• Record Type: Journal Article
• Authors: Lin, Na; Fu, Luwei; Zhao, Liang; Hawbani, Ammar; Tan, Zhiyuan; Al-Dubai, Ahmed; Min, Geyong
• Is Part Of: Computers & electrical engineering. Volume 100(2022)
• Publication Date: 2022-05
• Subjects: Nomad algorithm -- Nature-inspired algorithm -- Optimizer -- Function optimization -- Global search; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.107862
• Languages: English
• ISSNs: 0045-7906

Abstract

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 behavior 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.