Paper Info
Title
Reliability-based MOGA design optimization using probabilistic response surface method and bayesian neural network.
Abstract
In this paper, an effective optimization approach, which integrated the probabilistic surrogate model, non-dominated sorting genetic algorithm, and reliability index method, is proposed to multi-objective reliability-based design optimization. To reduce the computational cost and improve the efficiency of the optimization process, the problem can be surrogated by probabilistic response surface method and Bayesian neural network as high fidelity metamodel with statistical modelling method. After verification through the simulation results on numerical test problem, these techniques have been applied to engineering problem in optimizing simultaneously multi-performances or objective functions subject to reliability constraints.
Year
DOI
Venue
2019
10.1145/3319619.3321901
GECCO
Keywords
Field
DocType
Multi-objective optimization, Reliability based design optimization (RBDO), Reliability index, Evolutionary algorithms, Non-dominated sorting genetic algoritlun, Probabilistic response surface method (PRSM), Bayesian neural network (BNN)
Computer science,Bayesian neural networks,Artificial intelligence,Probabilistic logic,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4503-6748-6
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Juhee Lim100.34
Jong Soo Lee274.36