结合差分进化( DE) 与协方差矩阵自适应进化策略( CMAES) ，提出了自适应子群体米母算法( SaS-MA) ; 为发挥SaS-MA 的全局勘探和局部开发能力，设计了一个由删除机制和建立机制组成的子群体自适应策略; 通过一个数值实验，将SaS-MA 与DE、L-CMAES、PSO 和IP-CMAES 等算法进行了对比; 最后对一个10 层2 跨的混凝土框架进行了位移性能优化，并将SaS-MA 的优化结果与例题原始文献、目前广泛应用且性能可靠的DE、CMA、PSO 和GA 等算法的优化结果进行了对比． 数值实验结果表明: 子群体自适应策略通过动态删除和建立子群体，提高了算法性能; SaS-MA 在统计学意义上优于其他4 种算法，是一个有效、可靠的全局优化算法． 位移性能优化算例结果表明: SaS-MA 在混凝土框架位移优化问题上更具有效性; 相比于其他4 种算法，同等条件下SaS-MA 能够得到最好的解，具备良好的寻优能力和收敛性．

In this paper,a new memetic algorithm named SaS-MA ( Self-Adaptive Subpopulations-based MemeticAlgorithm) is proposed by combining the differential evolution ( DE) with the covariance matrix adaptation evolutionstrategy ( CMAES) .In order to improve the synergy of the global exploration and local exploitation abilities ofSaS-MA,a self-adaptive subpopulation strategy consisting of the deletion mechanism and the addition mechanism isdesigned.Then,a numerical experiment is implemented to compare SaS-MA with four algorithms,namely,DE,LCMAES,PSO and IP-CMAES.Finally,a ten-story two-bay concrete frame is adopted to carry out the displacementperformance optimization,and the results obtained by using SaS-MA are compared with those obtained from theoriginal paper and through four algorithms,namely,DE,CMA,PSO and GA,which are reliable and widely usedat present.Numerical results indicate that the proposed strategy improves the performance of SaS-MA by means ofdynamic deletion and by constructing subpopulations,and that,SaS-MA is efficient and reliable,and it statisticallyoutperforms the former four existing algorithms.The optimization results show that SaS-MA is more efficient in termsof the concrete frame displacement optimization,and that,as compared with the other four algorithms under thesame conditions,SaS-MA is capable of achieving the best solution,and it possesses excellent optimization abilityand preferable convergence.