Surrogate-model based optimization algorithms can be applied to solve expensive black-box function optimization problem. With the introduction of ensemble model, surrogate-model based algorithms can be automatically adjusted to adapt to various specific problems with different parameter spaces and no need for manual design of surrogate model. However, introduction of ensemble model significantly increases the computational load of surrogate-model based algorithms for training and updating of ensemble model. In this article, parallel computing technology is utilized to speed up the weight updating related computation for the ensemble surrogate model built by Dempster-Shafer theory, and a novel parallel sampling mechanism based on stochastic response surface method is developed to implement asynchronous parameter optimization, based on witch an asynchronous parallel global optimization algorithm is proposed. Furthermore, the parallel algorithm proposed is applied to quantitative trading strategy tuning in financial market and shows both feasibility and effectiveness in actual application. Experiments demonstrates that, the algorithms can achieve high speedup ratio and scalability with no degradation of optimization performance.

基于代理模型的优化算法可用于解决昂贵的黑盒函数优化问题。随着集成模型的引入，可以自动调整基于代理模型的算法，以适应具有不同参数空间的各种特定问题，而无需人工设计代理模型。然而，集成模型的引入极大地增加了用于训练和更新集成模型的基于代理模型的算法的计算负荷。本文利用并行计算技术加快了基于Dempster-Shafer理论构建的整体代理模型权重更新的相关计算，并开发了一种基于随机响应面法的新型并行采样机制来实现异步参数优化，在此基础上，提出了一种异步并行全局优化算法。此外，将所提出的并行算法应用于金融市场中的定量交易策略调整，并在实际应用中显示了可行性和有效性。实验表明，该算法可以实现较高的加速比和可扩展性，并且不会降低优化性能。