Contextual multi-objective bayesian optimization = 상황적 다중목적 베이지언 최적화

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In this paper, we describe how to optimize multiple objective functions in uncontrollable environment changes. We use Bayesian optimization to optimize for a changing environment in complex systems where the form of the objective function is unknown. The difference from the previous study is that the controlled input values and the given environmental values are selected in a continuous range rather than in a set. To do this, we define a virtual Pareto set using the predictive distribution of the Gaussian process and present a CMOBO algorithm. The proposed algorithm describes how optimization is performed when wind direction is changed for the wind farm data generated by the FLORIS simulator.
Advisors
Park, Jinkyooresearcher박진규researcher
Description
한국과학기술원 :산업및시스템공학과,
Publisher
한국과학기술원
Issue Date
2019
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 산업및시스템공학과, 2019.2,[iv, 30 p. :]

Keywords

Gaussian process▼abayesian optimization▼apareto set; 가우시안 과정▼a베이지언 최적화▼a파레토 집합

URI
http://hdl.handle.net/10203/266250
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=843203&flag=dissertation
Appears in Collection
IE-Theses_Master(석사논문)
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