A Discrete Adaptive Lion Optimization Algorithm for QoS-Driven IoT Service Composition with Global Constraints

Abstract

In an Internet of Things (IoT) environment, multiple objects usually interact with one another to meet a complex user’s request. This involves the composition of several atomic IoT services. Given a large number of functionally equivalent services with different Quality of Service (QoS) values, the service composition problem remains one of the main challenges in IoT environments. This paper presents a Discrete Adaptive Lion Optimization Algorithm (DALOA) to select IoT services in a composition process while considering global user QoS constraints. DALOA is based on the Lion Optimization Algorithm (LOA) and developed by combining several LOA operators, such as roaming, mating, and migration. First, DALOA divides the initial population into two sub-populations: pride and nomad, and each sub-population has its search strategies. Second, the roaming nomad process follows a random searching mode (strong exploration) to avoid being trapped in local optima. Third, the roaming pride searching mode represents strong local research, ensuring more efficient exploitation. Four, mating (mating pride, mating nomad) allows for information sharing between members of the same population. Finally, the migration operator is used to ensure population diversity by allowing information sharing between the pride and the nomad. The simulation results show that DALOA obtains the best compositional optimality and finds the near-optimal composition of the IoT services in a reasonable execution time compared to other approaches. Indeed, the combination of the previous operators provides a good trade-off between exploration and exploitation.