S ervice composition with end-to-end QoS constraints have been proven to be an NP-hard problem and various evolutionary algorithms such as Artificial Bee Colony (ABC) are widely adopted to look for an approximatelyoptimal solution in the restricted time. The advantage of ABC algorithm is its simplicity (i.e., only three control parameters, and simple heuristic rules for exploiting the solution space), and our previous work has verified its effectiveness in solving the service composition problem. This paper focuses on the enhancement of traditional ABC neighborhood strategy for local search, with the objective of better optimality and faster convergence rate. The work is shown in two perspectives. Firstly, an approximate-mapping based local search strategy is proposed, where the discrete solution space of service composition problems are approximately transformed into a continuous space in which a locally optimal neighboring solution is precisely found; in this way, the superiority of traditional ABC could still hold in service composition problem. Secondly, we adopt the Von Neumann neighborhood topology, which has been proven to have better performance than other topologies, to further improve the quality of local search.Experiment results show that our Approximate-Mapping Von Neumann algorithm (AMV) is more effective than other service composition algorithms such as genetic algorithm and Threshold-Based Algorithm (TBA).

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In recent years, the research on the QoS-aware service composition problem often assume that each component service in the process to be solved is equally essential, they do not consider the impact of core component services and other component services on problem-solving, or even though their impact is considered, they are not fully considered. So this paper first proposes a diffractive method based on them. Considering the advantages of Artificial Bee Colony (ABC) such as simplicity, this paper chooses it as the basic algorithm. In addition, with the continuous development of service ecosystem, it gradually formed a variety of domain features. They have an important influence on problem-solving, but the existing research has not explored this influence in-depth. Therefore, this paper digs deep into this influence. Given the characteristics of the problem to be solved in this paper, the S-ABCPC algorithm is designed. At last, experiments have proved the effectiveness of the method proposed in this paper. The impact factors of this method have been studied.