本文应用遗传算法，采用基于“链码”的编码方式和数学形态学的变异算子，设计了一种连续体结构拓扑优化的新方法。
A novel algorithm for topology optimization of continuum structures has been designed on the basis of "chain-code" encoding and mathematical morphologic operators.
利用用户认知的不确定性设计定向变异算子。首先，采用主成分分析法辨识用户认知的不确定性；
A directional mutation operator is designed by using the user's uncertain cognitive. The main ingredient analysis is used to identify the user's uncertain cognitive.
通过设计粗粒度并行遗传算法和交叉、变异等算子，提高了算法的计算效率和性能。
The computing efficiency and the performance of the algorithm are improved by a designed parallel algorithm, the crossover and the mutation operators.
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