A novel nomad migration-inspired algorithm for global optimization

• Published in: Computers & electrical engineering Vol. 100; p. 107862
• Main Authors: Lin, Na, Fu, Luwei, Zhao, Liang, Hawbani, Ammar, Tan, Zhiyuan, Al-Dubai, Ahmed, Min, Geyong
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.05.2022; Elsevier BV
• Subjects: Accuracy; Algorithms; Function optimization; Global optimization; Global search; Nature-inspired algorithm; Nomad algorithm; Optimizer; Solution space; Nature-inspired algorithm; Global search; Nomad algorithm; Function optimization; Optimizer
• ISSN: 0045-7906; 1879-0755
• DOI: 10.1016/j.compeleceng.2022.107862

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.