基于进化元胞自动机的结构拓扑优化

实现元胞自动机算法自组织演化机制的关键是建立适合问题的局部规则。传统的方法是根据人们的经验或 其他算法得到的结果来建立局部规则,被称为局部间接规则。为了解决局部间接规则存在的局限性,计算量大等 缺点,提出用进化建立元胞自动机局部直接规则的方法。通过建立结构优化的多目标优化模型,用遗传算法寻求 最优的演化规则,得到适应相应问题的解。由仿真结果可见用遗传算法建立的元胞自动机局部直接规则对复杂系 统的自组织问题是很有效的。

Structural topology optimization using evolutionary cellular automata

The key technique of cellular automata operation is to establish transition rules suitable to corresponding problem. Traditionally, the indirect transition rules are established by experience or according to the results obtained through other arithmetic. To overcome the shortcomings of localization and long operation time caused by adopting indirect transition rules, genetic algorithms based direct transition rules are discussed. The direct local transition rules established by genetic algorithms are used in the thin-plate topological layout minimum weight optimization. Multi-objective optimization model is created and optimum transition rules are obtained by the evolution of genetic algorithms. From the simulation results, it is observed that the local direct rules obtained by evolution are effective in the self-organization process of the complex problems.