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The surface deformation strategy for the volute parameterization utilizes a set of control points that adapt the volute surface circumferentially to the flow using transformation operations. A fully automated loop was used to generate design of experiments (DoE) cases, from which data mining was employed for design-rule extraction. Surrogate-based multi-objective optimization was performed on a Gaussian process interpolation model at a single operating point. An improvement of 1.32 %, and 67.85 % in isentropic efficiency, and static pressure rise coefficient were attained with a reduction in total pressure loss coefficient of 10.21 %. Data mining results showed that the distortion index was strongly influenced by the throat area, exit diffuser cone variation, and the tongue radius. The reduction in area to radius ratio distribution and progressive change in volute cross-sectional profile downstream of the tongue was found to be the most sensitive to static pressure rise and total pressure loss. 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