动态面向服务的黑盒子优化计算平台设计与实现

Design and realization of dynamic service-oriented optimization computing platform of BlackBox

针对工程优化算法中黑盒子的高计算代价问题,提出一个基于River技术的动态面向服务优化计算平台(R-DSOCP)用于分布式并行计算黑盒子。首先分析优化算法中黑盒子的执行模式,遵循动态面向服务架构并围绕着River的服务发布和查找功能设计所需的核心服务并利用它们组建R-DSOCP;然后设计了一个基于蚁群优化的黑盒子调度问题(BSP)算法,调度服务利用该算法不仅能够快速为黑盒子选取最佳计算服务而且能够均衡平台负载;最后,实验结果表明在分离优化算法执行和黑盒子计算后,黑盒子在平台中得到了有效的并行计算,平均计算效率相比单计算节点提高近n倍,其中n为并行因子。因此借助高性能计算(HPC)技术,R-DSOCP在工程优化领域能够为提高优化算法速度并降低计算成本提供一个可行方案。

Aiming at the problem of high computational cost of the BlackBox of the engineering optimizations, a River- based Dynamic Service-oriented Optimization Computing Platform （R-DSOCP） was proposed to calculate the BlackBox in a distributed and parallel way. Firstly, the running pattern of BlackBox in the optimization algorithms was analyzed. Conforming to the dynamic service-oriented architecture and surrounding the functions of service release and lookup of River, the kernel services required for building R-DSOCP were designed. Secondly, an ACO-based BlackBox Schedule Problem （BSP） algorithm was devised. Depending on it, the scheduling service could not only choose the best computing services for BlackBox quickly but also balance the load of R-DSOCP. At Last, the experimental results show that the BlackBox can be parallel performed on the platform effectively after separating the BlackBox＇ s computation from the execution of the optimization algorithm. Comparing with a single computing machine, the average computing efficiency is advanced nearly n times, n is the parallel factor. Thus, with the help of High Performance Computing （HPC） technology, R-DSOCP can offer a feasible scheme for accelerating the optimization algorithm and reducing the computational expenses in the field of engineering optimization.