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Surrogate-assisted evolutionary algorithms (SAEAs) have become a popular method to solve data-driven optimization problems (DOPs), which are common in industry. However, with the development of the Internet of Things, data are collected, processed, and stored in a distributed manner, leading a new optimization paradigm for SAEAs. To make SAEAs adapt to these distributed DOPs, this paper employs the edge computing paradigm to develop a platform that provides technical support for SAEAs with distributed structures, named ECdo. Specifically, the platform utilizes KubeEdge, an open-source edge computing framework, to mount the cluster and combines microservice interface design with the containerization strategy to offer a flexible deployment approach for distributed SAEAs. In addition, an efficient and stable internal communication mechanism is designed for the interaction between distributed …