Abstract

Complex networks have a large number of nodes and edges, which prevents the understanding of network structure and the discovery of valid information. Community detection is an important issue in studying network structure and network characteristics. It has received widespread attention in many fields. Most existing community detection algorithms obtain the final community structure by analyzing the relationship between each node and surrounding nodes. Starting from a portion of nodes in each community, the corresponding community for each node can be obtained through expansion operations, thereby obtaining the entire community structure. Such strategy can improve the accuracy of community detection algorithms. When solving large- scale combinatorial optimization problems, the traditional ant colony algorithm has a slow convergence rate and tends to fall into local optima. More and more scholars propose relevant optimization algorithms on the basis of classical ant colony algorithm. To overcome premature convergence, adaptively adjusted the pheromone on the path according to the existing solution, which enabled it to escape the local optimal value. To address these challenges, this research proposes an improved optimization method. This approach integrates community detection, multi-group cooperation, pheromone feedback mechanisms and Hybrid Dynamic Pheromone Updating Mechanism to improve exploration efficiency and convergence speed in large-scale TSP problems.

Authors

M. Hemalatha, N. Kamaraj
Sri Ramakrishna Mission Vidyalaya College of Arts and Science, India

Keywords

Community Network, Ant Colony Algorithm, Community Detection, Travelling Salesman Problem, Route Relation Network