<![CDATA[Ranking functionally similar cloud services by their quality of service (QoS) is essential for selecting the best option, yet current ranking methods generally handle only one QoS objective at a time and give up robustness once several conflicting criteria are in play. We propose a multi-objective ranking framework built on grey wolf optimization (GWO) to close this gap, pairing similarity computation via the Kendall Rank Correlation Coefficient (KRCC) with an optimization-driven prioritization step. Testing the resulting MOGWO-based method against several baseline algorithms on a cloud service dataset shows clear gains in both ranking accuracy and efficiency. The framework offers a scalable way to support decision-making in complex cloud environments and a clear structure for prioritizing services across multiple QoS dimensions at once.]]>
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