一种基于网格的分布计算协同机制

通过对网格计算中资源协同调度机制的研究,根据该机制的资源协同分配需求,提出一个以Globus Toolkit为平台的基于网格计算的分布协同计算模型。在基于经济模型的作业调度机制的竞价机制中,给出一个较为完善的价格函数模型以及相应的作业预测完成时间模型,利用设计的价格函数模型实现资源提供的主观能动性,使得资源的调配更加合理、快速。同时,在分布式计算模型中还给出一个基于备份的作业调度容错机制。

多飞行器协同轨迹优化设计

针对多飞行器协同轨迹约束多、耦合强的复杂多目标优化与决策问题,对多飞行器协同轨迹优化进行了较为系统的研究。首先,对多飞行器协同轨迹优化进行了数学描述和对多飞行器在未知环境下的航迹规划进行了数学建模;其次,提出了多飞行器协同任务规划系统优化设计的数值算法,其主要包括多飞行器协同任务分配算法与飞行器最优航迹规划;最后,基于以上的研究,对3种典型不同情况下的多飞行器协同轨迹优化进行了飞行数值仿真与分析。该算法具有以下特性:全局一体优化、采用最优的多维策略、实时在线性、高精度、能够考虑各种随机干扰的作用等。

Gaussian process assisted coevolutionary estimation of distribution algorithm for computationally expensive problems

In order to reduce the computation of complex problems, a new surrogate-assisted estimation of distribution algorithm with Gaussian process was proposed. Coevolution was used in dual populations which evolved in parallel. The search space was projected into multiple subspaces and searched by sub-populations. Also, the whole space was exploited by the other population which exchanges information with the sub-populations. In order to make the evolutionary course efficient, multivariate Gaussian model and Gaussian mixture model were used in both populations separately to estimate the distribution of individuals and reproduce new generations. For the surrogate model, Gaussian process was combined with the algorithm which predicted variance of the predictions. The results on six benchmark functions show that the new algorithm performs better than other surrogate-model based algorithms and the computation complexity is only 10% of the original estimation of distribution algorithm.