Despite all the progress in Web service selection, the need for an approach with a better optimality and performance still remains. This paper presents a genetic algorithm by adopting the Pareto principle that is called GAP2WSS for selecting a Web service for each task of a composite Web service from a pool of candidate Web services. In contrast to the existing approaches, all global QoS constraints, interservice constraints, and transactional constraints are considered simultaneously. At first, all candidate Web services are scored and ranked per each task using the proposed mechanism. Then, the top 20 percent of the candidate Web services of each task are considered as the candidate Web services of the corresponding task to reduce the problem search space. Finally, the Web service selection problem is solved by focusing only on these 20 percent candidate Web services of each task using a genetic algorithm. Empirical studies demonstrate this approach leads to a higher efficiency and efficacy as compared with the case that all the candidate Web services are considered in solving the problem.
翻译：尽管在选择网络服务方面已取得了所有进展,但仍然需要一种更优化和业绩的方法。本文件通过采用称为“GAP2WSSS”的Pareto原则,从候选网络服务库中为综合网络服务的每一项任务选择一种网络服务,提出了一种遗传算法。与现有的方法不同,所有全球的QOS限制、服务间限制和交易限制都同时考虑。首先,所有候选网络服务都使用拟议机制按每项任务评分和排位。然后,每项任务中前20%的候选网络服务被视为相应任务的候选网络服务,以减少问题搜索空间。最后,网络服务选择问题通过只注重利用基因算法处理每个任务的20%候选网络服务来解决。经验性研究表明,与所有候选网络服务都考虑解决问题的情况相比,这一方法的效率和效力更高。
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