融入武装部队的鲸鱼优化算法用于社区发现

针对于鲸鱼优化算法（WOA）多样性不足、两搜索阶段信息交流效率低、不平衡的问题,这里借用武装部队协同作战机理,提出一种新的WOA用于社区发现。为解决包围捕食阶段多样性不足问题,引入“邻居潜力”学习模型,提高WOA的全局搜索能力和学习广度;为解决两捕食阶段信息交流效率低问题,提出鲸鱼指挥官领导的气泡网捕食,确保搜索信息有效利用;为解决两种捕食机制不平衡的问题,采用改进的学习自动机引导鲸鱼种群向有希望区域移动。同时,考虑到复杂网络社区发现是离散问题,提出了一种基于拓扑特性的新编码离散演化规则。最后,通过真实数据集测试并与其他算法比较,结果表明,所提算法相较于对比算法具有更优的寻优能力,验证了算法的有效性。

Whale optimization algorithm incorporating armed forces collaboration for community discovery

Aiming at the problems of insufficient diversity of whale optimization algorithm(WOA), inefficient and unbalanced information exchange between the two search phases, this paper proposed an improved WOA based on the armed forces collaborative warfare mechanism for community discovery. In order to solve the problem of insufficient diversity in the encircling predation stage, this paper developed a "neighbor potential" learning model to improve the global search capability and learn breadth of WOA; to solve the problem of inefficient information exchange during the two-predation phase, this paper proposed bubble net predation based on whale commanders, which could ensure effective utilization of search information; to address the imbalance between the two predation mechanisms, this paper proposed an improved learning automaton, which could guide whale populations toward promising areas. Meanwhile, because community discovery in complex networks is a discrete problem, this paper proposed a new coded discrete evolution rule based on topological properties. finally, this paper tested the proposed algorithms on real data sets and compared them with other algorithms, and simulation experiments show that the proposed algorithm has better optimization ability than the comparison algorithm, verifying the effectiveness of the improved strategy.