基于多蚁群同步优化的多真值发现算法

为提高在多真值场景下真值发现的准确性，提出一种多蚁群同步优化的多真值发现算法（multi-ant co-lonies synchronization optimization based multi-truth discovery algorithm，MAC-SO-MTD）。以最大化各数据源提供的观测值集合与该对象真值集合之间相似度的加权和为目标，将多真值发现问题建模为求解子集问题，在此基础上设计蚁群算法进行求解：根据对象个数设置相应的蚁群，构造子集问题的有向图，利用路径概率转移公式进行同步搜索真值；将信息素更新分为本次迭代最优更新和本次迭代不更新，提高了算法的收敛速度。最后，通过算法复杂度分析和在真实数据集上的实验验证了该算法的优越性。

Multi-ant colonies synchronization optimization based multi-truth discovery algorithm

In order to improve the accuracy of truth discovery in multi-truth scene, this paper proposed a multi-ant colonies synchronization optimization based multi-truth discovery(MAC-SO-MTD) algorithm. It modeled the multi-truth discovery problem as the subset problem, which goal was maximizing the weighted sum of similarity between the set of observations provided by each data source and the set of true values of the object. On this basis, then it designed ant colony algorithm to solve the problem. It set ant colonies according to the number of objects. Based on the subset problem''s structure graph, this paper used routes'' probability transition equations to search for truths synchronically. After one cycle, the best route of this cycle updating and no updating were two instances of updating pheromone, which improved the convergence speed. Finally, the analysis of algorithm complexity and contrast experiment on the real data set validate the superiority of the algorithm.