文献类型：期刊文献

中文题名：Kriging模型并行加点策略的多目标优化方法

英文题名：A Multi-objective Optimization Method using Kriging Model and Parallel Point Adding Strategy

作者：韩鼎[1];郑建荣[1];周骏彦[2]

机构：[1]华东理工大学机械与动力工程学院,上海200237;[2]太平洋机电(集团)有限公司,上海200050

年份：2016

卷号：35

期号：11

起止页码：1715

中文期刊名：机械科学与技术

外文期刊名：Mechanical Science and Technology for Aerospace Engineering

收录：CSTPCD;;Scopus;北大核心:【北大核心2014】；CSCD:【CSCD2015_2016】；

基金：国家科技支撑计划项目(2011BAF08B03);上海市引进技术的吸收与创新年度计划项目(产-30-方向-28)

语种：中文

中文关键词：多目标优化;Kriging模型;自适应并行加点;Pareto最优解

外文关键词：multi-objective optimization; Kriging model; parallel point adaptively adding; Pareto optimal solution

摘要：针对复杂机械装备多学科多目标优化设计成本高、周期长等问题,提出一种近似模型与并行加点策略相结合的多目标优化方法。基于Kriging模型,将添加更新样本点定义为同时考虑Pareto最优解和预测误差的动态多目标优化问题,应用改进NSGA-II优化算法和极大极小距离准则,确定最优的并行更新样本点,在提高Kriging模型精度的同时实现多目标优化。测试函数验证和实例结果表明,该方法可有效提高复杂系统多目标优化效率,同时获得收敛性和分散性俱佳的Pareto最优解。
Aiming at reducing computational cost and time for expensive multi-objective optimization problem of complex mechanical systems, a multi-objective optimization method based on approximate model with a parallel update point adaptively adding strategy is put forward. In this approach, based on a Kriging model, the point adding strategy is formulated as a dynamic multi-objective optimization problem which considers both Pareto optimal solutions and model uncertainty. By applying an improved NSGA-II and a maximum distance criterion, multiple update points can be identified for further parallel evaluation. The proposed approach is tested on six numerical functions and one engineering example. The results show that, this method not only is able to obtain Pareto optimal solutions of good convergence and diversity, but also possess the advantage in optimization efficiency.