基于贪婪算法的影响网络行动方案优选

针对影响网络中行动方案的优选问题,分析了现有方法存在的不足,提出了一种基于贪婪算法的行动方案优选方法。该方法采用自顶向下的搜索方式,通过分析行动组合对期望效果的整体影响来选择较优的行动方案。并进行实例验证。结果表明,与灵敏度分析法和穷举搜索法相比,该方法能够在较短时间内找到较优的可行行动方案集合,可有效支持行动方案的优选。

A greedy algorithm based approach for course of actions determination in influence nets

The purpose of creating influence nets models is to determine the best or close-to-best course of actions in uncertain situations. In order to overcome the limitation of existing approaches, this paper presents a greedy algorithm based approach for this problem. This approach is a hill-climbing search technique, allowing the user to observe the combined impact of actions on the desired effect in contrast to the sensitivity analysis that allows the user to evaluate individual impacts only. The example demonstrates that the approach serach the feasible solutions in shorter time and supports best course of actions determination for influence nets.