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Open AccessArticle

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

by Naixue Xiong 1,2, Wenliang Wu 1 and Chunxue Wu 1,*
1
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Military Road, No. 516, Shanghai 200093, China
2
Department of Mathematics and Computer Science, Northeastern State University Address: 611 N, Grand Ave, Tahlequah, OK 74464, USA
*
Author to whom correspondence should be addressed.
Sustainability 2017, 9(6), 985; https://doi.org/10.3390/su9060985
Received: 5 April 2017 / Revised: 24 May 2017 / Accepted: 6 June 2017 / Published: 8 June 2017
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 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. View Full-Text
Keywords: ant colony algorithm; positive feedback; pheromone; convergence speed ant colony algorithm; positive feedback; pheromone; convergence speed
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Xiong, N.; Wu, W.; Wu, C. An Improved Routing Optimization Algorithm Based on Travelling Salesman Problem for Social Networks. Sustainability 2017, 9, 985.

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