Please use this identifier to cite or link to this item: http://dl.pgu.ac.ir/handle/2027.42/96191
Title: Source code for paper: "A Multi-Objective Variable Fidelity Optimization Method for Genetic Algorithms"
Keywords: Genetic Algorithm;Optimization;Stiffened Panel;Variable Fidelity;Kriging
Publisher: Taylor & Francis
Description: Source code supporting a novel variable-fidelity optimization (VFO) scheme is presented for multi-objective genetic algorithms. The technique uses a low- and high-fidelity ver-sion of the objective function with a Kriging scaling model to interpolate be- tween them. The Kriging model is constructed online through a fixed updating schedule. Results for three standard genetic algorithm test cases and a two- objective stiffened panel optimization problem are presented. For the stiffened panel problem statistical analysis of four performance metrics are used to com- pare the Pareto fronts between the VFO method, full high-fidelity optimizer runs, and Pareto fronts developed by enumeration. The fixed updating ap- proach is shown to reduce the number of high-fidelity calls significantly while approximating the Pareto front in an efficient manner.
URI: https://deepblue.lib.umich.edu/handle/2027.42/96191
Other Identifiers: Engineering Optimization
Type Of Material: Software
Appears in Collections:Naval Architecture & Marine Engineering (NA&ME)

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