A cloud service composition method using a trust-based clustering algorithm and honeybee mating optimization algorithm

One of the most critical issues in using service-oriented technologies is the combination of services, which has become an important challenge in the present. There are some significant challenges in the service composition, most notable is the quality of service (QoS), which is more challenging due to changing circumstances in dynamic service environments. Also, trust value in the case of selection of more reliable services is another challenge in the service composition. Due to NP-hard complexity of service composition, many metaheuristic algorithms have been used so far. Therefore, in this paper, the honeybee mating optimization algorithm as one of the powerful metaheuristic algorithms is used for achieving the desired goals. To improve the QoS, inspirations from the mating stages of the honeybee, the interactions between honeybees and queen bee mating and the selection of the new queen from the relevant optimization algorithm have been used. To address the trust challenge, a trust-based clustering algorithm has also been used. The simulation results using C# language have shown that the proposed method in small scale problem acts better than particle swarm optimization algorithm, genetic algorithm, and discrete gbest-guided artificial bee colony algorithm. With the clustering and reduction of the search space, the response time is improved; also, more trusted services are selected. The results of the simulation on a large-scale problem have indicated that the proposed method is exhibited worse performance than the average results of previous works in computation time.