Nature inspired meta-heuristic algorithms for solving the service composition problem in the cloud environments

Many sorts of services in the cloud environments must be composited based on the user's requests to meet the requirements. Thus, the distributed services are joined to the cloud services through service composition. Also, it is known as NP-hard problems and many researchers significantly are focused on this problem in recent years. Therefore, many different nature-inspired meta-heuristic techniques are proposed for solving this problem. The nature-inspired meta-heuristic techniques have an important role in solving the service composition problem in the cloud environments, but there is not a wide-ranging and detailed paper about reviewing and studying the important mechanisms in this field. Therefore, this study presents a comprehensive analysis of the nature-inspired meta-heuristic techniques for the service composition issue in the cloud computing. The review also contains a classification of the important techniques. These classifications include Ant Colony Optimization, Bee Colony Optimization, Genetic Algorithm, Particle Swarm Optimization, Cuckoo Optimization Algorithm, Bat Algorithm, greedy algorithm, and hybrid algorithm. An important aim of this paper is to highlight the emphasis on the optimization algorithms, and the benefits to tackle the challenges are encountered in the cloud service composition. Also, this paper presents the advantages and disadvantages of the nature-inspired meta-heuristic algorithms for solving the service composition problem in the cloud environments. Moreover, this paper aims to provide more efficient service composition algorithms in the future. Finally, the obtained results have shown that the discussed algorithms have an important effect in solving the cloud service composition problem, and this effect has been increased in recent years.