%0 Journal Article
%T 模拟退火法在协同优化中的应用<br>An application of simulated annealing to collaborative optimization
%A 饶太春
%A 兰林强
%A 罗伟林
%J 福州大学学报(自然科学版)
%D 2018
%R 10.7631/issn.1000-2243.17298
%X 为解决传统标准协同优化算法经常无法收敛和容易陷入局部最优等缺陷，提出一种基于模拟退火法的改进协同优化算法. 该算法在继承标准协同优化算法并行自治优点的前提下，首先系统级由全局模拟退火算法和梯度算法相结合的组合优化策略代替单优化算法，其次根据优化具体情况引入动态松弛因子，以此来保证优化的全局性和精度. 通过两个典型的MDO测试算例对改进的协同优化算法进行验证，优化结果表明，改进的协同优化算法具有更好的精度、收敛速度和稳定性.<br>In order to deal with the shortcoming of traditional collaborative optimization，for example the results are always not converging and local optimal solution；an improved collaborative optimization based on simulated annealing is presented. Under the premise of the advantages of the parallel and autonomy of the standard collaborative optimization algorithm，the ASA-MMFD-CO replaces the single optimization algorithm by the hybrid optimization strategy based on the global algorithm ASA and the gradient algorithm MMFD in the system level. Meanwhile，dynamic relaxation factor is used in the optimization. The global optimization and accuracy can be guaranteed，and the shortcoming of traditional collaborative optimization can be resolved. Two typical MDO examples are adopted to test the improved CO. The results show that it has better accuracy，convergence rate and stability
%K 协同优化 模拟退火 组合优化 动态松弛&lt
%K br&gt
%K collaborate optimization simulated annealing hybrid optimization dynamic relaxation
%U http://xbzrb.fzu.edu.cn/ch/reader/view_abstract.aspx?file_no=201803014&flag=1