The different communications of users in social networks play a key role in effect to each other. The effect is important when they can achieve their goals through different communications. Studying the effect of specific users on other users has been modeled on the influence maximization problem on social networks. To solve this problem, different algorithms have been proposed that each of which has attempted to improve the influence spread and running time than other algorithms. Due to the lack of a review of the meta-heuristic algorithms for the influence maximization problem so far, in this paper, we first perform a comprehensive categorize of the presented algorithms for this problem. Then according to the efficient results and significant progress of the meta-heuristic algorithms over the last few years, we describe the comparison, advantages, and disadvantages of these algorithms.

用户在社交网络中的不同通信对彼此的影响起着关键作用。当他们可以通过不同的沟通实现他们的目标时，效果很重要。研究特定用户对其他用户的影响已被建模为社交网络上的影响最大化问题。为了解决这个问题，已经提出了不同的算法，每个算法都试图比其他算法改善影响传播和运行时间。由于到目前为止缺乏对影响最大化问题的元启发式算法的回顾，在本文中，我们首先对该问题的现有算法进行全面分类。然后根据过去几年元启发式算法的有效结果和显着进展，我们描述了比较，