A multi-objective genetic algorithm (MOGA) is a powerful global optimization tool, but its results are considerably affected by the crossover parameter η. Finding an appropriate η demands too much computing time because MOGA needs be run several times in order to find a good η. In this paper, a self-adaptive crossover parameter is introduced in a strategy to adopt a new η for every generation while running MOGA. This new scheme has also been adopted for a multi-generation Gaussian process optimization (MGGPO) when producing trial solutions. Compared with the existing MGGPO and MOGA, the MGGPO and MOGA with the new strategy show better performance in nonlinear optimization for the design of low-emittance storage rings.
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| http://dx.doi.org/10.1107/S1600577524004569 | DOI Listing |
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