DDAPSO: Hybrid Discrete Dragonfly Algorithm and Particle Swarm Algorithm to Service Selection and Composition for the Internet of Things Applications

Abstract

Recently, the Internet of Things (IoT) has quickly risen as one of the most essential technologies of this century. IoT allows users to connect to a vast network of smart devices, services, and data. An important and challenging research problem in the Internet of Things applications is how to select an appropriate service selection (SS). In the SS problem, users can combine several services from diverse sources (things or devices) to satisfy their needs. On the other hand, the SS problem is known for its complexity and is categorized as an NP-hard problem; such problems are typically solved utilizing heuristics like bio-inspired algorithms. In this research a new bio-inspired algorithm called DDAPSO is created to solve the SS problem where a new strategy is proposed to maintain a balance between the exploration and exploitation abilities. This hybrid algorithm is the result of coupling a Discrete Dragonfly Algorithm (DDA) with the particle swarm optimization algorithm (PSO). The suggested algorithm was properly tested using a variety of scenarios with different numbers of services and with different numbers of concrete services per each service set or task. The proposed algorithm is compared with the main recent well-known algorithms, i.e. GA, PSO, DDA, ABC and MVO for service selection. In a large-scale setting, the results clearly show that the DDAPSO algorithm outperforms other services selection algorithms reported in the literature in terms of selection optimally as well as execution time.