In the realm of constrained datasets, the efficacy of optimization algorithms wields a significant influence over the convergence and optimization prowess of machine learning models in the pursuit of optimal material solutions. This, in turn, directly impacts the efficiency of discovering novel materials and refining their designs. Professor Yanjing Su's team, hailing from University of Science and Technology Beijing and Beijing Advanced Innovation Center for Materials Genome Engineering, has propounded a universally applicable algorithm for active learning in the domain of material triple-objective optimization. Termed as the "equiprobability distribution with maximum confidence interval" (ED-MCI), this algorithm extends its reach towards collaborative optimization designs encompassing a multitude of interrelated property objectives. The ED-MCI algorithm boasts remarkable optimization efficiency, enabling the fabrication of comprehensive, exceptional materials through a relatively modest number of iterative experimental cycles. In this paper, taking superalloy design as an example, a machine learning model is trained with multiple small data sets. Via iterative feedback between model-driven recommendations and experimental validation with ED-MCI, a harmonized optimization of the volume fraction, dimensions, and morphology of the  ′ phase in CoNiAlCr-base superalloys is achieved. The ED-MCI algorithm, unburdened by the limitations of material categories during mathematical derivation, introduces a novel universal approach to multi-objective collaborative optimization in the domain of materials.