A New Interval Multi-Objective Optimization Method for Uncertain Problems with Dependent Interval Variables

被引：2
|
作者：
Liu, Guiping [1 ]
Luo, Rui [1 ]
Liu, Sheng [1 ]
机构：
[1] Hunan Univ, Coll Mech & Vehicle Engn, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Hunan, Peoples R China
来源：
INTERNATIONAL JOURNAL OF COMPUTATIONAL METHODS | 2020年 / 17卷 / 10期
基金：
中国国家自然科学基金;
关键词：
Multi-objective optimization; interval model; uncertainty; variable dependency; crashworthiness; LINEAR-PROGRAMMING PROBLEMS; RELIABILITY-ANALYSIS; OBJECTIVE FUNCTIONS; CONVEX MODEL; DESIGN; NUMBER;
D O I：
10.1142/S0219876220500073
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
In this paper, a new interval multi-objective optimization (MOO) method integrating with the multidimensional parallelepiped (MP) interval model has been proposed to handle the uncertain problems with dependent interval variables. The MP interval model is integrated to depict the uncertain domain of the problem, where the uncertainties are described by marginal intervals and the degree of the dependencies among the interval variables is described by correlation coefficients. Then an efficient multi-objective iterative algorithm combining the micro multi-objective genetic algorithm (MOGA) with an approximate optimization method is formulated. Three numerical examples are presented to demonstrate the efficiency of the proposed approach.
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收藏
页数：21
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