混合生物生长自适应搜索遗传算法在形状优化中的应用

利用自适应搜索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搜索遗传算法。该算法即可充分利用前两种算法的优点,又可弥补二者的不足。为了验证该算法的合理性和正确性,对经典算例三杆桁架结构进行了优化,并将新算法进一步应用于具有复杂结构的三维离心叶轮优化设计中,结果表明混合算法较遗传算法收敛速度快,且可得到形状优化最优解。

Shape Optimization by an Adaptive Search Genetic Algorithm with Biological Growth Strategy

Utilizing the characteristics of the adaptive search genetic algorithm and biological growth algorithm, this paper presents a hybrid approach, adaptive search genetic algorithm with biological growth strategy, for the shape optimization of structures with complicated geometry. The new algorithm embeds the biological growth algorithm into the adaptive search genetic algorithm, and therefore takes advantages of both approaches in terms of global optimization and computational efficiency. As an example of application, the new algorithm has been used for the shape optimization of three link truss and an impeller with rather complicated geometry. Being subject to the same set of geometric and stress constraints, the results show that better optimized structure in terms of weight can be achieved and the computational efficiency can also be significantly improved by the hybrid algorithm in comparison with the algorithms proposed previously.