The multi-objective quality of service (QoS)-driven web service composition problem (MOQWSCP) aims to find the best combinations of atomic web services (i.e. composite service) to answer high quality of the optimized QoS criteria in a way that maximize benefit QoS parameters such as availability and reliability and minimize the negative ones like price and response time, where the users’ requirements should be satisfied. Due to the dynamic environments in which the elementary services are invoked, some services’ QoS parameters are often ambiguous and uncertain, so, it is inappropriate to express them by fixed values. Hence, The QoS parameters are represented by trapezoidal fuzzy numbers. Thus, we formulate MOQWSCP as a fuzzy multi-objective optimization problem (FMOQWSCP). A fuzzy discrete multi-objective artificial bee colony (FDMOABC) approach is provided to solve the formulated FMOQWSCP, for which we have integrated a new fuzzy ranking method to cope with solutions sorting and a new fuzzy distance measure that is used to control and keep the diversity of FDMOABC’s solutions. Furthermore, a fuzzy multi-criteria decision-making method (FMCDMM) is provided to determine the best composite service among the Pareto-optimal solutions generated by FDMOABC. Finally, two kinds of comparisons are performed to validate the performance and the effectiveness of FDMOABC and FMCDMM methods. In the former, the combined FDMOABC and FMCDMM methods is compared against the fuzzy single objective optimization approaches TGA and EFPA, whereas in the later, a multi-objective optimization comparison is performed among FDMOABC and the fuzzy-extended versions of NSGA-II and SPEA2 algorithms.

由多目标服务质量（QoS）驱动的Web服务组合问题（MOQWSCP）的目的是找到原子Web服务（即复合服务）的最佳组合，以最大程度地提高收益QoS的方式满足优化QoS标准的高质量诸如可用性和可靠性之类的参数，并最小化诸如价格和响应时间之类的负面参数，从而满足用户的要求。由于调用基本服务的动态环境，一些服务的QoS参数通常是模棱两可和不确定的，因此不宜用固定值来表示它们。因此，QoS参数由梯形模糊数表示。因此，我们将MOQWSCP公式化为模糊多目标优化问题（FMOQWSCP）。提供了一种模糊离散多目标人工蜂群（FDMOABC）方法来解决公式化的FMOQWSCP，为此，我们集成了一种新的模糊排序方法以应对解决方案排序，并集成了一种新的模糊距离度量，该度量用于控制和保持分类。 FDMOABC解决方案的多样性。此外，提供了一种模糊的多准则决策方法（FMCDMM）来确定由FDMOABC生成的帕累托最优解中的最佳组合服务。最后，进行了两种比较，以验证FDMOABC和FMCDMM方法的性能和有效性。前者将FDMOABC和FMCDMM的组合方法与模糊单目标优化方法TGA和EFPA进行了比较，而在后者中，