High dimensional Bayesian Optimization Algorithm for Complex System in Time Series
复杂时间序列系统的高维贝叶斯优化算法AI 解读 ↓
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复杂时间序列系统的高维贝叶斯优化算法
- 研究问题
- 时间序列最优控制模型中的高维全局优化难以由标准贝叶斯优化处理，如何结合降维、采样与局部搜索改善求解表现？
- 方法与思路
- 作者结合降维、多臂老虎机与随机搜索式采样，比较五种维度补全策略，并在最后加入基于 Adam 步骤的局部搜索。
- 材料陈述的贡献
- 摘要称，算法利用时间或维度依赖特征降低优化维数，计算实验显示其能在高维时间序列最优控制问题中取得较好的可行解。
- 阅读关注点
- 核对降维与补全规则、采集函数的采样策略、局部搜索的作用，以及实验中的维数、计算预算和基准算法是否可比。
- 阅读边界
- 所据为 arXiv 预印本，同行评审及期刊接收未核实；摘要未给出全局最优保证、具体误差或计算成本，不能据此确认普遍优势。
原文摘要
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.