DOI：10.3969/j.issn.1672-3872.2025.03.037
作 者：王 震（长安大学工程机械学院，陕西 西安 710064）
摘 要：【目的】开发一种创新的双尺度拓扑优化策略，应用于结构设计与优化研究。【方法】结合混合元胞自动机（HCA）与双向渐进法（BESO），推出HCA-BESO拓扑优化方法，其核心创新点在于采用HCA的邻域规则替代传统基于距离的加权处理机制，简化了灵敏度过滤规则，并引入了多样的邻域组合形式。【结果】通过引入HCA，该方法在双尺度优化方面显著提升了性能，减少了迭代次数，相比传统BESO算法，能够创建出刚度更高的结构。【结论】HCA-BESO方法代表了双尺度拓扑优化研究的一个重大进步，为未来的结构设计与优化研究开辟了新的路径。
关键词：双尺度优化；双向渐进法（BESO）；混合元胞自动机（HCA）；灵敏度过滤
A New Method of Dual-Scale Topology Optimization Based on Fusion of Neighborhood Rules
Author: Wang Zhen (School of Construction Machinery, Chang’an University, Xi’an 710064, China)
Abstract: [Objective] To develop an innovative two-scale topology optimization strategy for structural design and optimization. [Method] The HCA-BESO topology optimization method was proposed by combining the bi-directional asymptotic method (BESO) and hybrid cellular automata (HCA). The core of the hca-beso topology optimization method was to replace the traditional weighted processing mechanism based on distance with the neighborhood rules of HCA, simplify the sensitivity filtering rules, and introduce a variety of neighborhood combinations. [Result] By introducing HCA, the method can significantly improve the performance in two-scale optimization and reduce the number of iterations. Compared with the traditional BESO algorithm, it can create a structure with higher stiffness. [Conclusion] The HCA-BESO method represents a significant progress in the research of two-scale topology optimization, and opens up a new path for the future research of structural design and optimization.
Keywords: dual-scale optimization; Bi-directional Evolutionary Structural Optimization (BESO); Hybrid Cellular Automata (HCA); sensitivity filtering
引文信息：[1]王震.基于融合邻域规则的双尺度拓扑优化新方法[J].南方农机，2025，56(3)：147-149+159.
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