An Improved Routing Optimization Algorithm Based on Travelling Salesman Problem for Social Networks

Naixue Xiong, Wenliang Wu, Chunxue Wu
2017 Sustainability  
A social network is a social structure, which is organized by the relationships or interactions between individuals or groups. Humans link the physical network with social network, and the services in the social world are based on data and analysis, which directly influence decision making in the physical network. In this paper, we focus on a routing optimization algorithm, which solves a well-known and popular problem. Ant colony algorithm is proposed to solve this problem effectively, but
more » ... ffectively, but random selection strategy of the traditional algorithm causes evolution speed to be slow. Meanwhile, positive feedback and distributed computing model make the algorithm quickly converge. Therefore, how to improve convergence speed and search ability of algorithm is the focus of the current research. The paper proposes the improved scheme. Considering the difficulty about searching 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 improve sensitive speed of finding the shortest path, it updates pheromone regulation formula. The results show that the improved algorithm can effectively improve convergence speed and search ability for achieving higher accuracy and optimal results. Sustainability 2017, 9, 985 2 of 15 a new ant colony intelligent behavior optimization algorithm that was first proposed by Italian scholar Dorigo [6] at the beginning of the 1990s. Ant colony algorithm [7] , an optimization algorithm to find the optimal path, is used to solve the combinatorial optimization problem, which is based on the cooperative behavior of ant foraging. It has also been widely used in many fields, such as travelling sales, distribution scheduling and dynamic routing [8] . The algorithm simulates the foraging process of ants, where ants secrete pheromones during the food search process to record their path, and other ants perceive the density of pheromones to choose a shorter path to find food. The more ants on the path, the more pheromones are secreted, and the path will be chosen by more ants. On the contrary, the fewer ants on the path, the less pheromone are secreted, the fewer ants will choose the path, so most of the ants will choose the path of pheromone concentration to find food. This shows that the algorithm has good distributed collaboration and robustness, which is why it is widely used in logistics and distribution, network optimization and path optimization problem, is one of the algorithms to solve combinatorial optimization problems [9] [10] [11] . The remainder of this paper is organized as follows. In Section 2, we introduce the related works. The research of ant colony algorithm is described in detail in Section 3. Then, the improvement of algorithm is introduced in Section 4. Next, in Section 5, experiments of our method and the compared methods on the two parameters demonstrate the effectiveness and improved performance of the improved method, and application scenario analysis of Applied Improved Ant Colony Algorithm. After that, we interpret our results in this section. Finally, we conclude this paper and discuss the future works in Section 6.
doi:10.3390/su9060985 fatcat:7sn5jply5zgalb3vojb7kb7dka