基于支持向量回归机和粒子群算法的改进协同优化方法  

Improved Collaborative Optimization Based on Support Vector Regression and Particle Swarm Optimization

作　　者：杨希祥[1] 杨慧欣[1] 江振宇[1] 张为华 

机构地区：[1]国防科学技术大学航天与材料工程学院,湖南长沙410073

出　　处：《湖南大学学报（自然科学版）》2011年第3期34-39,共6页Journal of Hunan University:Natural Sciences

基　　金：国家'863计划'资助项目(2007AAX02503);中国博士后科学基金资助项目(200801491)

摘　　要：研究基于支持向量回归机和粒子群算法的改进协同优化方法.阐述了协同优化方法和支持向量回归机方法基本原理,为有效解决系统级优化协调困难问题,改善收敛性能,提高收敛速度,采用支持向量回归机构造系统级约束条件的近似模型,引入粒子群算法求解系统级和学科级优化问题.仿真计算结果表明,设计的协同优化方法可有效求解多学科设计优化问题,和基本协同优化方法相比,求解精度高,优化迭代次数少,稳定性好.可为多学科设计优化研究提供理论参考.Improved collaborative optimization based on support vector regression and particle swarm optimization algorithm was researched.The basic principle of collaborative optimization and support vector regression was represented,and in order to resolve the difficulty in system-level coordination,improve convergence performance and efficiency,approximate models of constraint conditions in system-level were constructed using support vector regression,and particle swarm optimization algorithm was introduced to the system-level optimization and disciplinary-level optimization.Simulation results show that the improved collaborative optimization can effectively resolve multidisciplinary design optimization problems,and compared to standard collaborative optimization,optimization accuracy is higher,system-level iterative operation is less,and the stability is better.All those can provide theoretical reference for the research of multidisciplinary design optimization.

关 键 词：协同优化 支持向量回归机 粒子群算法 

分 类 号：V412.1[航空宇航科学与技术—航空宇航推进理论与工程]