Consumers often form complex social networks based on a multitude of different relations and interactions. By virtue of these interactions, they influence each other's decisions in adopting products or behaviors. Therefore, it is essential for companies to identify influential consumers to target, in the hopes that influencing them will lead to a large cascade of further recommendations. Several studies, based on approximation algorithms and assume that the objective function is monotonic and submodular, have been addressed this issue of viral marketing. However, there is a complex and broad family of diffusion models in competitive environment, and the properties of monotonic and submodular may not be upheld. Therefore, in this research, we borrowed from swarm intelligence-specifically the ant colony optimization algorithm-to address the competitive influence-maximization problem. The proposed approaches were evaluated using a coauthorship data set from the arXiv e-print (http://www.arxiv.org), and the obtained experimental results demonstrated that our approaches outperform two well-known benchmark heuristics.