High dimensional Bayesian Optimization Algorithm for Complex System in Time Series
At present, high-dimensional global optimization problems with time-series models have received much attention from engineering fields. Since it was proposed, Bayesian optimization has quickly become a popular and promising approach for solving global optimization problems. However, the standard Bayesian optimization algorithm is insufficient to solving the global optimal solution when the model is high-dimensional. Hence, this paper presents a novel high dimensional Bayesian optimization algorithm by considering dimension reduction and different dimension fill-in strategies. Most existing literature about Bayesian optimization algorithms did not discuss the sampling strategies to optimize the acquisition function. This study proposed a new sampling method based on both the multi-armed bandit and random search methods while optimizing the acquisition function. Besides, based on the time-dependent or dimension-dependent characteristics of the model, the proposed algorithm can reduce the dimension evenly. Then, five different dimension fill-in strategies were discussed and compared in this study. Finally, to increase the final accuracy of the optimal solution, the proposed algorithm adds a local search based on a series of Adam-based steps at the final stage. Our computational experiments demonstrated that the proposed Bayesian optimization algorithm could achieve reasonable solutions with excellent performances for high dimensional global optimization problems with a time-series optimal control model.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian OptimizationDimensionality Reductionglobal-optimizationTime SeriesTime Series AnalysisVocal Bursts Intensity PredictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Vanilla Bayesian Optimization Performs Great in High Dimensions
High-dimensional problems have long been considered the Achilles' heel of Bayesian optimization algorithms. Spurred by the curse of dimensionality, a large collection of algorithms aim to make it more performant in this …
Bayesian OptimizationHigh Dimensional Bayesian Optimization via Supervised Dimension Reduction
Bayesian optimization (BO) has been broadly applied to computational expensive problems, but it is still challenging to extend BO to high dimensions. Existing works are usually under strict assumption of an additive or a…
Bayesian OptimizationDimensionality ReductionVocal Bursts Intensity PredictionExpected Coordinate Improvement for High-Dimensional Bayesian Optimization
Bayesian optimization (BO) algorithm is very popular for solving low-dimensional expensive optimization problems. Extending Bayesian optimization to high dimension is a meaningful but challenging task. One of the major c…
Bayesian OptimizationAn Adaptive Dropout Approach for High-Dimensional Bayesian Optimization
Bayesian optimization (BO) is a widely used algorithm for solving expensive black-box optimization problems. However, its performance decreases significantly on high-dimensional problems due to the inherent high-dimensio…
Bayesian OptimizationHigh dimensional Bayesian Optimization via Condensing-Expansion Projection
In high-dimensional settings, Bayesian optimization (BO) can be expensive and infeasible. The random embedding Bayesian optimization algorithm is commonly used to address high-dimensional BO challenges. However, this met…
Bayesian Optimization