A Multi-heuristic Cooperative Ant Colony System for Optimizing Elimination Ordering of Bayesian Networks

Xuchu Dong, D. Ouyang, Yuxin Ye, Haihong Yu, Yonggang Zhang
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Abstract

To solve the problem of searching for an optimal elimination ordering of Bayesian networks, a novel effective heuristic, MinSum Weight, and an ACS approach incorporated with multi-heuristic mechanism are proposed. The ACS approach named MHC-ACS utilizes a set of heuristics to direct the ants moving in the search space. The cooperation of multiple heuristics helps ants explore more regions. Moreover, the most appropriate heuristic will be identified and be reinforced with the evolution of the whole system. Experiments demonstrate that MHC-ACS has a better performance than other swarm intelligence methods.
基于多启发式协同蚁群系统的贝叶斯网络消去排序优化
为了解决贝叶斯网络的最优消去排序问题,提出了一种新的有效启发式方法——最小和权值法,以及一种结合多启发式机制的ACS方法。名为MHC-ACS的ACS方法利用一组启发式方法来指导蚂蚁在搜索空间中的移动。多种启发式的合作有助于蚂蚁探索更多的区域。此外,最合适的启发式将被确定并随着整个系统的发展而得到加强。实验表明,MHC-ACS算法比其他群体智能算法具有更好的性能。
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