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Influence of Algorithm Parameters of Bayesian Optimization, Genetic Algorithm, and Particle Swarm Optimization on Their Optimization Performance
Author(s) -
Wang ZhiLei,
Ogawa Toshio,
Adachi Yoshitaka
Publication year - 2019
Publication title -
advanced theory and simulations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.068
H-Index - 17
ISSN - 2513-0390
DOI - 10.1002/adts.201900110
Subject(s) - multi swarm optimization , meta optimization , particle swarm optimization , bayesian optimization , computer science , metaheuristic , algorithm , inverse , mathematical optimization , genetic algorithm , derivative free optimization , optimization algorithm , machine learning , mathematics , geometry
In response to modern materials research, a data‐driven properties‐to‐microstructure‐to‐processing inverse analysis is proposed for use in material design. In the present work, machine learning optimization algorithms of Bayesian optimization, genetic algorithm, and particle swarm optimization are used to perform inverse analysis with a maximum property search. The use of machine learning algorithms readily involves careful tuning of learning parameters, which is often carried out by a trial‐and‐error method requiring expert experience or general guidelines, and the choices of such parameters can play a critical role in attaining good optimization performance. Thus, the influence of various parameters on the optimization performance of the aforementioned algorithms are systematically investigated to provide a protocol for selecting adequate algorithm parameters for a given optimization problem in data‐driven material design.