Nomadic People Optimizer for IoT Combinatorial Testing Problem

Nowadays, smart cities depends on the integration between the systems via internet connections which are known as the Internet of Everything (IoE). The integrated systems raise several concerns involving the potential presence of critical integration defects. Therefore, there is a need for an interaction testing approach. In this paper, we present a Nomadic People Optimizer as a search engine for the test list generation for interaction testing. The proposed test list generation strategy is called Nomadic People Strategy (NPS). The results show the NPS is also capable of outperforming the existing strategies such as Genetic Algorithm (GA), Ant Colony Algorithm (ACA), Coco Search Algorithm (CS), Harmony Search Algorithm (HS), Jaya Algorithm (JA), Firefly Algorithm (FA) and Melody Algorithm (MA).