A Biomimetic Moving-Mesh Topology Optimization Method

Topology optimization has experienced rapid development over the past two decades and has been widely applied in fields such as aircraft structures, civil engineering, and transportation equipment. Common topology optimization methods, such as density-based methods and level set methods, focus on global variable optimization. These global optimization approaches often consume substantial computational resources and are not suitable for parallel optimization. In contrast, structures in nature evolve from a combination of numerous local optimization problems, where each cell unit adjusts on the basis of its perception of the surrounding environment, leading to the formation of biological structures. This paper proposes a novel heuristic topology optimization method, the biomimetic moving-mesh (BMM) method, inspired by biological cell growth and evolution. The BMM method uses the positions of mesh nodes as variables to simulate cellular expansion and contraction, thereby creating a new optimization approach. Compared with traditional topology optimization methods, the BMM method offers smoother meshes and is more suitable for handling large-scale parallel optimization problems.