Service composition is among the most important challenges that cloud providers have ever faced. Optimization of QoS attributes when composing simple atomic services to obtain a complex service can be considered to be an NP-hard problem, which could be solved properly by using Hybrid optimization algorithms. In this research, the hybridization of an improved Gravitational Attraction Search (as a local search algorithm) with an Imperialist Competitive Algorithm has led us to introduce and apply a new memetic algorithm for gaining optimal or near optimal response time and execution fees simultaneously, for cloud computing service composition. Using a roulette wheel selection algorithm to make well-advised and non-blind decisions to choose the number of countries in each empire that should be selected to apply a local search to has assisted the hybrid algorithm at achieving better solutions. Introducing a new equation to calculate the QoS eligibility of the solutions that were generated based on the normalization of the response time and execution fee has also led us to compute the results fairly and in a scientifically based manner.

In this paper, a novel heuristic search algorithm called Smart Root Search (SRS) is proposed. SRS employs intelligent foraging behavior of immature, mature and hair roots of plants to explore and exploit the problem search space simultaneously. SRS divides the search space into several subspaces. It thereupon utilizes the branching and drought operations to focus on richer areas of promising subspaces while extraneous ones are not thoroughly ignored. To achieve this, the smart reactions of the SRS model are designed to act based on analyzing the heterogeneous conditions of various sections of different search spaces. In order to evaluate the performance of the SRS, it was tested on a set of known unimodal and multimodal test functions. The results were then compared with those obtained using genetic algorithms, particle swarm optimization, differential evolution and imperialist competitive algorithms and then analyzed statistically. The results demonstrated that the SRS outperformed comparative algorithms for 92% and 82% of the investigated unimodal and multimodal test functions, respectively. Therefore, the SRS is a promising nature-inspired optimization algorithm.

Cloud computing as an effective computing approach is attracting increasingly the attention of heavy processing applicants to its capabilities. Cloud suppliers try to prevent any service-request unanswered utilizing a large number of service providers. Selecting optimal unique-services in cloud computing service composition (CCSC) is a noteworthy problem that must be addressed extremely accurate and scrupulously. Recently, considerable studies have been done for solving CCSC, however lack of widely accepted and reliable CCSC problems for fair and equitable comparison of proposing methods, is a blind spot and should be considered as a high-priority problem. In this paper, a reliable set of ten CCSC problems is introduced (CCSC_Benchmark) which is prepared to be used as a "standard test" in future works. To complete the usability of the problem set, the best solutions of the generated problems are also found and presented to provide a facility for calculating error rate of the proposed methods.