混合遗传算法进行离心叶轮优化设计

Shape Opetimization of Centrifugal Impeller by a Hybrid Genetic Algorithm

利用自适应交叉遗传算法和生物生长算法的特点 ,提出一种新的优化方法———混合生物生长自适应交叉遗传算法。该方法既充分利用了遗传算法全局寻优和生物生长法快速收敛的优点 ,又弥补了遗传算法收敛速度过慢和生物生长法过分依赖结构初始形状的不足。为了验证优化方法的正确性和合理性 ,对某三维离心叶轮进行优化设计 ,结果表明 ,混合算法较单纯采用遗传算法收敛速度快 。

Utilizing the characteristics of the adaptive cross operator genetic algorithm and biological growth method, this paper presented a hybrid approcach, adaptive cross operator genetic algorithm with biological growth strategy, for the shape optimization of structures with complicated geometry. The new method embeded the biological growth algorithm into the adaptive cross operator genetic algorithm, and theretore took advantages of both approaches in terms of global optimization and computational efficiency. As an example of application, the new method had been used for the shape optimization of an impeller with rather complicated geometry. The results show that better optimized structure in terms of weight can be achieved and the computational efficiency can also be significantly improved.