Paper Info
Title
Multi-Objective Optimization Design of Constant Stress Accelerated Degradation Test Using Inverse Gaussian Process.
Abstract
A multi-objective optimization method for constant stress accelerated degradation test is proposed in order to solve the problem of different or even conflicted test configuration under different optimization objectives. The inverse Gaussian process is used as a degradation model, and the unknown parameters are solved by maximum likelihood estimation. The two optimization criteria of the maximum determinant of the information matrix and the minimum asymptotic variance of P-quantile are considered. The improved multi-objective particle swarm optimization algorithm is proposed to search test optimal configuration, and the Pareto solution set for dual-objectives is obtained. Finally, the effectiveness of the method is illustrated by a group of examples of electrical connectors. Compared with the single-objective optimization design, the proposed method is more reasonable and convenient for test configuration. The performance index of the test function indicates that the optimal algorithm we proposed has some obvious advantages over the NSGA-II in diversity and convergence of the Pareto solutions and it is significant in guiding engineering practice.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2900397
IEEE ACCESS
Keywords
Field
DocType
Accelerated degradation test,optimal design,multi-objective,maximum likelihood estimation,multi-objective particle swarm optimization
Particle swarm optimization,Convergence (routing),Mathematical optimization,Inverse Gaussian distribution,Computer science,Test functions for optimization,Multi-objective optimization,Fisher information,Delta method,Pareto principle,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Zhenyu Wu13211.36
Xiaoping Liu2134.70
Bin Guo31603143.97
Dejun Cui400.34
Li-Jie Zhang521.48