Service Composition in Internet of Vehicle Environment Using multi-Objective Harris Hawk Optimization Algorithm

The development of the Internet of Vehicles (IoV) has led to the increasing complexity of users' demands for IoV services and the growing number of services with the same function in the service pool. How to match the optimal service instances among the massive services with the users' demands and specific scenarios to form a composite service quickly is the key issue of service composition and optimal selection (SCOS). To solve the problem, this paper designs an emergency handling service when the road accident occurs according to the vehicle network deployed in smart cities, proposes a corresponding service composition model, and describes the service composition problem as a multi-objective multi-constraint optimization problem by weighing the quality of service (QoS) attributes such as time, availability, reliability, throughput, success rate, robustness, security, and cost as well as time, space constraints. In addition, this paper proposes the enhanced multi-objective Harris hawk optimization (EMOHHO) to solve the optimization problem, which improves the convergence and diversity by improving the energy iteration curve, single iteration coordinates of the original multi-objective Harris hawk optimization (MOHHO), and initializes the coordinate when the multiple iteration coordinates of the hawk are the same to avoid the algorithm falling into local optimum. Simulation results show that the EMOHHO can achieve better convergence and diversity in a shorter search time compared to other multi-objective optimization algorithms.