Service composition and optimal selection (SCOS) concerns the building of optimal composite service by integrating existing services with the aim of performing complex task. Due to a plethora of affordable cloud services providing similar functionalities while differing in quality of service (QoS), how to determine suitable candidates to orchestrate the best composite service, also known as QoS-aware SCOS problem, becomes more complicated. A number of evolutionary optimizers have been developed to resolve SCOS. Unfortunately, a large majority of these optimizers carry out the optimization by aggregating many diverse QoS attributes into a single objective or simply considering two or three representative QoS attributes. SCOS, particularly, from the perspective of many-objective optimization, has not received an appropriate attention. As more factors come into play, SCOS is strictly a many-objective problem. This study explores the scalability of recently state-of-the-art evolutionary many-objective optimization (EMaO) algorithms in addressing SCOS. Comparative results reveal that these EMaO algorithms, never before applied to many-objective SCOS, exhibit distinct search abilities with respect to the objective space dimensionality and problem scale. Based on the empirical observation, useful suggestions and insights for choosing suitable EMaO algorithms pertaining to different SCOS problems are given.

服务组合和最佳选择（SCOS）涉及通过集成现有服务以执行复杂任务的目的来构建最佳组合服务。由于提供了相似功能但服务质量（QoS）不同的大量可负担的云服务，如何确定合适的候选者来编排最佳组合服务（也称为QoS感知SCOS问题）变得更加复杂。已经开发了许多进化优化器来解决SCOS。不幸的是，这些优化器中的大多数通过将许多不同的QoS属性聚合到一个目标中或简单地考虑两个或三个代表性的QoS属性来执行优化。特别是从多目标优化的角度来看，SCOS尚未得到适当的重视。随着更多因素的出现，SCOS严格来说是一个多目标问题。这项研究探索了解决SCOS方面最新的最新进化多目标优化（EMaO）算法的可扩展性。比较结果表明，这些EMaO算法从未应用于多目标SCOS，它们在目标空间维数和问题规模方面表现出独特的搜索能力。基于经验观察，给出了针对不同SCOS问题选择合适的EMaO算法的有益建议和见解。从来没有应用于多目标SCOS，在目标空间维数和问题规模方面表现出独特的搜索能力。基于经验观察，给出了针对不同SCOS问题选择合适的EMaO算法的有益建议和见解。从来没有应用于多目标SCOS，在目标空间维数和问题规模方面表现出独特的搜索能力。基于经验观察，给出了针对不同SCOS问题选择合适的EMaO算法的有益建议和见解。