Academic Correspondence on Gradient-Free Topology Optimization and the DSA Paradigm: A Paradigm Shift from Continuous Calculus to Discrete Topology

This paper, framed as a scholarly correspondence, provides an in-depth analysis of four cutting-edge paradigms in gradient-free topology optimization: generative AI reparameterization, bionic intelligence search, parallel computation with projection transformations, and Topological Data Analysis (TDA). In response to the inherent limitations of these paradigms—such as black-box physical constraints, late-stage convergence traps, excessive computational costs, and combinatorial explosion—this paper introduces the "Daopological Structural Annealing (DSA)" paradigm. By abandoning the calculus assumption and utilizing discrete grids, topological operators, and energy annealing mechanisms, DSA offers a lightweight, white-box, and highly cost-effective "minimalist physicalism" pathway for high-dimensional black-box optimization.