桁架结构多目标优化的微分演化算法

Differential evolution algorithm for truss structure multi-objective optimization

为了解决带有约束的桁架结构的多目标优化问题,本文采用了一种基于微分演化的多目标优化(DEMO)方法。DEMO方法采用多目标优化进化算法中Pareto和拥挤距离排序机制,并保留了DE算法的优点。为了验证DEMO算法的可行性和有效性,对经典桁架进行尺寸优化,并与其他优化方法进行了比较,数值结果表明DEMO算法性能比其他算法要好,其所得的解具有更好的多样性、均匀性和收敛性。

In order to solve the multi-objective optimization of truss structures with constrains,a new approach to multi-objective optimization based on differential evolution（DEMO） was adopted in this paper.DEMO adopted the mechanisms of Pareto based ranking and crowding distance sorting which used by evolutionary algorithms for multi-objective optimization,and preserved the advantages of differential evolution（DE）.Classical truss sizing optimization problems are solved to demonstrate the feasibility and effectiveness of the DEMO algorithm,and the results are compared with other optimization methods.The results indicate that the DEMO provides better performance in the diversity,the uniformity and the convergence of the obtained solution than other methods.