摘要： 为提高离散桁架优化问题的计算效率，提出一种改进的离散差分进化算法。基于种群多样性自适应地选择变异策略以平衡探索和收敛能力，根据个体差异度和种群多样性缩减种群规模以减少计算量，在进行结构分析前舍弃较大的实验个体规避无用计算，并引入精英选择技术解决选择阶段目标个体和实验个体数量不等的问题，在此基础上，给出一种将数值之间的距离转化为概率的离散化方法，处理离散变量问题。实验结果表明，与IGA、DE等算法相比，该算法在保证最优解质量的同时，能够大幅减少结构分析次数。

关键词: 离散差分进化算法, 自适应变异策略, 自适应种群规模, 结构分析次数, 桁架优化

Abstract: In order to improve the calculation efficiency of the discrete truss optimization problem,this paper proposes an improved discrete differential evolution algorithm.The mutation strategies is adaptively selected based on population diversity to balance exploration and convergence capabilities,and the population size is adaptively reduced based on individual differences and population diversity to reduce calculations.Heavier trial individuals are discarded before structural analysis to avoid useless calculations,and the elite selection technique is introduced to solve the problem of unequal numbers of target individuals and trial individuals in the selection phase.On this basis,a discretization method that converts the distance between the values into a probability is proposed to solve the problem of discrete variables. Experimental results show that compared with algorithms such as IGA and DE,this algorithm can greatly reduce the structural analysis times while ensuring the optimal solution quality.

Key words: discrete differential evolution algorithm, adaptive mutation strategy, adaptive population size, number of structural analysis, truss optimization

中图分类号: 

• TP391