Ant colony optimization with multi-agent evolution for detecting functional modules in protein-protein interaction networks

Functional module identification in a Protein-Protein Interaction (PPI) network is one of the most important and challenging tasks in computational biology. For detecting functional modules, it is difficult to solve the problem directly and always results in a low accuracy and a large discard rate. In this paper, we present a novel algorithm of ant colony optimization with multi-agent evolution for detecting functional modules. The proposed ACO-MAE algorithm enhances the performance of ant colony optimization (ACO) by incorporating multi-agent evolution (MAE). First, the ant colony optimization for solving Traveling Salesman Problems (TSP) is conducted to construct primary clustering results. Then, the multi-agent evolutionary process is performed to move out of local optima. From simulation results, it is shown that the proposed ACO-MAE algorithm has superior performance when compared to other existing algorithms.