A novel multi-objective optimization method based on an approximation model management technique

In this paper, a novel multi-objective optimization method is suggested based on an approximation model management technique. It is a sequential approximation method, in which a multi-objective optimization with approximation models subject to design variable move limits is iterated until convergenc...

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Bibliographic Details
Published in:Computer methods in applied mechanics and engineering Vol. 197; no. 33; pp. 2719 - 2731
Main Authors: Liu, G.P., Han, X., Jiang, C.
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.06.2008
Elsevier
Subjects:
Approximation model management
Computational techniques
Engineering optimization
Exact sciences and technology
Fundamental areas of phenomenology (including applications)
Mathematical methods in physics
Measurement and testing methods
Micro multi-objective genetic algorithm
Multi-objective optimization
Physics
Solid mechanics
Structural and continuum mechanics
Trust region
Approximation model management
Multi-objective optimization
Micro multi-objective genetic algorithm
Trust region
Engineering optimization
Pareto optimum
Optimization method
Multiobjective programming
Modeling
Optimization
Confidence interval
Model matching
Genetic algorithm
Sequential method
Experimental design
Response surface
System identification
ISSN:0045-7825, 1879-2138
Online Access:Get full text
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Summary:In this paper, a novel multi-objective optimization method is suggested based on an approximation model management technique. It is a sequential approximation method, in which a multi-objective optimization with approximation models subject to design variable move limits is iterated until convergence. In each iteration step, the approximation models are constructed by the response surface approximations with the samples which are obtained from the design of experiments, and a Pareto optimal set predicted by the approximations is identified through a multi-objective genetic algorithm. According to the prediction of the approximation models, a move limits updating strategy is employed to determine the design variable move limits for the next iteration. At the end of each iteration step, some uniform distributed points chosen from the predictive Pareto optimal frontier are verified by the high fidelity models and the obtained actual Pareto optimal set is stored in an external archive. The high efficiency of the present method is demonstrated by four different test functions and two engineering applications.
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ISSN:0045-7825
1879-2138
DOI:10.1016/j.cma.2007.12.014