Multi-objective integrated optimization based on evolutionary strategy with a dynamic weighting schedule  

基于动态加权计划和进化算法的多目标集成优化设计(英文)

作　　者：傅武军[1] 朱昌明[1] 叶庆泰[1] 

机构地区：[1]上海交通大学机械与动力工程学院,上海200030

出　　处：《Journal of Southeast University(English Edition)》2006年第2期204-207,共4页东南大学学报（英文版）

摘　　要：The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system performance and control cost are defined by H2 or H∞ norms. During this optimization process, the weights are varying with the increasing generation instead of fixed values. The proposed strategy together with the linear matrix inequality （LMI） or the Riccati controller design method can find a series of uniformly distributed nondominated solutions in a single run. Therefore, this method can greatly reduce the computation intensity of the integrated optimization problem compared with the weight-based single objective genetic algorithm. Active automotive suspension is adopted as an example to illustrate the effectiveness of the proposed method.为了求解结构和控制器多目标集成优化设计问题,获得用H2或H∞范数定义的最优系统性能和控制代价的所有非劣解,采用了基于动态加权计划的进化算法.该算法采用的权值随着进化代数的变化而变化,而不是固定值,通过结合线性矩阵不等式(LMI)或Riccati控制器设计方法,一次运行就能够得到均匀分布的非劣解.与加权的单目标遗传算法相比,该方法可以大大减少求解集成优化设计问题的计算强度.通过汽车悬架的集成设计表明了该方法的有效性.

关 键 词：integrated design multi-objective optimization evolutionary strategy dynamic weighting schedule suspension system 

分 类 号：U463.33[机械工程—车辆工程]