An improved fruit fly optimization algorithm for QoS aware cloud service composition

Abstract

An improved fruit fly optimization algorithm based on discrete immune optimization is proposed for quality of service (QoS) aware cloud service composition. The selection and composition of cloud services based on QoS criteria is formulated as NP hard optimization problem. We determined pareto optimal service set which is nondominated solution set as input to the improved fruit fly optimization algorithm. A mathematical model is derived to enhance local search capabilities and also improves the fitness value of composite service sequence. The fruit fly optimization (FOA) performs the evolutionary search process and enhances the convergence speed with good fitness value. The experimental results show that the improved FOA outperforms the genetic algorithm (GA), particle swarm optimization (PSO) and hybrid particle swarm optimization (HPSO) in terms of fitness value, execution time and error rate.