Paper Info
Title
High-Fidelity models in global optimization
Abstract
This work presents a Simulation Based Design environment based on a Global Optimization (GO) algorithm for the solution of optimum design problems. The procedure, illustrated in the framework of a multiobjective ship design optimization problem, make use of high-fidelity, CPU time expensive computational models, including a free surface capturing RANSE solver. The use of GO prevents the optimizer to be trapped into local minima. The optimization is composed by global and local phases. In the global stage of the search, a few computationally expensive simulations are needed for creating surrogate models (metamodels) of the objective functions. Tentative design, created to explore the design variable space are evaluated with these inexpensive analytical approximations. The more promising designs are clustered, then locally minimized and eventually verified with high-fidelity simulations. New exact values are used to improve the metamodels and repeated cycles of the algorithm are performed. A Decision Maker strategy is finally adopted to select the more promising design.
Year
DOI
Venue
2003
10.1007/11425076_9
COCOS
Keywords
Field
DocType
design variable space,expensive computational model,multiobjective ship design optimization,optimum design problem,tentative design,global optimization,high-fidelity simulation,global stage,high-fidelity model,computationally expensive simulation,local minimum,promising design,decision maker,local minima,computer model,design optimization,objective function,free surface
Probabilistic-based design optimization,Mathematical optimization,Global optimization,Biology,CPU time,Multi-objective optimization,Solver,Engineering optimization,Optimization problem,Multidisciplinary design optimization
Conference
Volume
ISSN
ISBN
3478
0302-9743
3-540-26003-X
Citations 
PageRank 
References 
1
0.48
4
Authors
2
Name
Order
Citations
PageRank
Daniele Peri12410.14
Emilio F. Campana2263.02