Document Type
Article
Publication Date
10-2015
Publication Title
Engineering Applications of Artificial Intelligence
Abstract
Automotive simulations often prohibit the use of traditional optimization techniques because these simulations are complex and computationally expensive. These two qualities motivate the use of evolutionary algorithms and meta-modeling techniques respectively. In this work, we apply biogeography-based optimization (BBO) to optimize radial basis function (RBF)-based lookup table controls of a variable camshaft timing system for fuel economy. Also, we reduce computational search effort by finding an effective parameterization of the problem, optimizing the parameters of the BBO algorithm for the problem, and estimating the cost of a portion of the candidate solutions in BBO with design and analysis of computer experiments (DACE). We find that we can improve fuel economy by 1.7% over the original control parameters, and we find a tradeoff in population size, and an optimal value for mutation rate. Finally, we find that we can use a small number of samples to construct DACE models, and we can use these models to estimate a significant portion of the candidate solutions each generation to reduce computation effort and still obtain good BBO solutions.
Repository Citation
Thomas, George; Simon, Daniel J.; and Michelini, John, "Biogeography-Based Optimization of a Variable Camshaft Timing System" (2015). Electrical and Computer Engineering Faculty Publications. 343.
https://engagedscholarship.csuohio.edu/enece_facpub/343
Original Citation
G. Thomas, D. Simon and J. Michelini, "Biogeography-based optimization of a variable camshaft timing system," Eng Appl Artif Intell, vol. 45, pp. 376-387, 10, 2015.
DOI
10.1016/j.engappai.2015.07.015
Version
Postprint
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Volume
45
Comments
LInk to publisher version: https://doi.org/10.1016/j.engappai.2015.07.015