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Multi-Objective Multi-Generation Gaussian Process Optimizer
release_rzanel4p2ve4zjywflnqwtxl64
by
Xiaobiao Huang,
Minghao Song,
Zhe Zhang,
Lin, Liu (Ed.),
John Byrd,
Regis Neuenschwander (Ed.),
Renan Picoreti,
Volker RW Schaa
Published
by JACoW Publishing, Geneva, Switzerland.
2021 Volume IPAC2021, Brazil
Abstract
We present a multi-objective evolutionary optimization algorithm that uses Gaussian process (GP) regression-based models to select trial solutions in a multi-generation iterative procedure. In each generation, a surrogate model is constructed for each objective function with the sample data. The models are used to evaluate solutions and to select the ones with a high potential before they are evaluated on the actual system. Since the trial solutions selected by the GP models tend to have better performance than other methods that only rely on random operations, the new algorithm has much higher efficiency in exploring the parameter space. Simulations with multiple test cases show that the new algorithm has a substantially higher convergence speed and stability than NSGA-II, MOPSO, and some other recent preselection-assisted algorithms.
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