Biogeography-based optimization of a variable camshaft timing system

Automotive system optimization problems are difficult to solve with traditional optimization techniques because the optimization problems are complex, and the simulations are computationally expensive. These two characteristics motivate the use of evolutionary algorithms and meta-modeling techniques respectively. In this work, we apply biogeography-based optimization (BBO) to radial basis function (RBF)-based lookup table controls of a variable camshaft timing system for fuel economy optimization. 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% compared to the original control parameters, and we find effective, problem-specific values for BBO population size and 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 BBO candidate solutions each generation to reduce computation effort and still obtain good BBO solutions.