Trust Region Based Mode Pursuing Sampling Method for Global Optimization of High Dimensional Design Problems

被引：49

作者：

Cheng, George H. ^{[1
]}

Younis, Adel ^{[1
]}

Hajikolaei, Kambiz Haji ^{[1
]}

Wang, G. Gary ^{[1
]}

机构：

[1] Simon Fraser Univ, Sch Mechatron Syst Engn, PDOL, Surrey, BC V3T0A3, Canada

来源：

JOURNAL OF MECHANICAL DESIGN | 2015年 / 137卷 / 02期

基金：

加拿大自然科学与工程研究理事会;

关键词：

APPROXIMATION;

D O I：

10.1115/1.4029219

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Mode pursuing sampling (MPS) was developed as a global optimization algorithm for design optimization problems involving expensive black box functions. MPS has been found to be effective and efficient for design problems of low dimensionality, i.e., the number of design variables is less than 10. This work integrates the concept of trust regions into the MPS framework to create a new algorithm, trust region based mode pursuing sampling (TRMPS2), with the aim of dramatically improving performance and efficiency for high dimensional problems. TRMPS2 is benchmarked against genetic algorithm (GA), dividing rectangles (DIRECT), efficient global optimization (EGO), and MPS using a suite of standard test problems and an engineering design problem. The results show that TRMPS2 performs better on average than GA, DIRECT, EGO, and MPS for high dimensional, expensive, and black box (HEB) problems.

引用

页数：9

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