An improved routing optimization algorithm based on travelling salesman problem for social networks

Abstract

A social network is a social structure, which is organized by the relationships or interactionsbetween individuals or groups. Humans link the physical network with social network, and theservices in the social world are based on data and analysis, which directly influence decision makingin the physical network. In this paper, we focus on a routing optimization algorithm, which solvesa well-known and popular problem. Ant colony algorithm is proposed to solve this problemeffectively, but random selection strategy of the traditional algorithm causes evolution speed tobe slow. Meanwhile, positive feedback and distributed computing model make the algorithm quicklyconverge. Therefore, how to improve convergence speed and search ability of algorithm is the focusof the current research. The paper proposes the improved scheme. Considering the difficulty aboutsearching for next better city, new parameters are introduced to improve probability of selection,and delay convergence speed of algorithm. To avoid the shortest path being submerged, and improvesensitive speed of finding the shortest path, it updates pheromone regulation formula. The resultsshow that the improved algorithm can effectively improve convergence speed and search ability forachieving higher accuracy and optimal results.