Biogeography-based optimization of a variable camshaft timing system. (October 2015)

• Record Type: Journal Article
• Authors: Thomas, George; Simon, Dan; Michelini, John
• Is Part Of: Engineering applications of artificial intelligence. Volume 45(2015:Sep.)
• Page Start: 376
• Page End: 387
• Publication Date: 2015-10
• Subjects: Computationally expensive optimization -- Biogeography-based optimization -- Automotive system optimization -- Design and analysis of computer experiments; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2015.07.015
• Languages: English
• ISSNs: 0952-1976

Abstract

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.