Study on QoS routing algorithm of CPS heterogeneous network based on ant colony algorithm and genetic algorithm

CPS is the development trend of the next network. Because of heterogeneity of CPS network, it has become a serious problem to guarantee the network's quality of service (QoS). Genetic algorithm and ant colony algorithm are effective methods to solve the routing problem. Genetic algorithm has the ability of global search, however, it doesn't utilize the feedback information of the system, which results in low solution rate; Ant colony algorithm is capable of global convergence, but it is lack of initial pheromones leading to slow algorithm. This paper combines the two algorithms to solve CPS network routing problems and it is turned out to be valid through simulation.

[1]  S. P. Todd,et al.  Research on what? , 1984, Journal of rehabilitation research and development.

[2]  Luca Maria Gambardella,et al.  AntHocNet: an adaptive nature-inspired algorithm for routing in mobile ad hoc networks , 2005, Eur. Trans. Telecommun..

[3]  Antonio Alfredo Ferreira Loureiro,et al.  GPS/Ant-Like Routing in Ad Hoc Networks , 2001, Telecommun. Syst..

[4]  Dario Pompili,et al.  Communication and Coordination in Wireless Sensor and Actor Networks , 2007, IEEE Transactions on Mobile Computing.

[5]  Sasikanth Avancha,et al.  Security for Sensor Networks , 2004 .

[6]  Kang G. Shin,et al.  Evolution of the Internet QoS and support for soft real-time applications , 2003, Proc. IEEE.

[7]  Charles E. Perkins,et al.  Scalability study of the ad hoc on‐demand distance vector routing protocol , 2003, Int. J. Netw. Manag..

[8]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[9]  Amitava Mukherjee,et al.  Pervasive Computing: A Paradigm for the 21st Century , 2003, Computer.

[10]  Kwang Mong Sim,et al.  Ant colony optimization for routing and load-balancing: survey and new directions , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[11]  Luca Maria Gambardella,et al.  Using Ant Agents to Combine Reactive and Proactive Strategies for Routing in Mobile Ad-hoc Networks , 2005, Int. J. Comput. Intell. Appl..

[12]  Léon J. M. Rothkrantz,et al.  Ant-Based Load Balancing in Telecommunications Networks , 1996, Adapt. Behav..

[13]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[14]  Carl E. Landwehr,et al.  Basic concepts and taxonomy of dependable and secure computing , 2004, IEEE Transactions on Dependable and Secure Computing.

[15]  Zhou Ren,et al.  Application of improved ant colony algorithm in fault-section location of complex distribution network , 2011, 2011 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT).

[16]  Parameswaran Ramanathan,et al.  Packet-dispersion techniques and a capacity-estimation methodology , 2004, IEEE/ACM Transactions on Networking.

[17]  Peng Pei-fu,et al.  Optimal PID Control of Self-Adapted Ant Colony Algorithm Based on Genetic Gene , 2006, CSSE 2008.

[18]  Marco Dorigo,et al.  AntNet: Distributed Stigmergetic Control for Communications Networks , 1998, J. Artif. Intell. Res..

[19]  Yang Jian-feng Function optimization problem based on genetic algorithm and ant algorithm , 2007 .

[20]  Duan Haibin Research on the A.S.Convergence Properties of Basic Ant Colony Algorithm , 2006 .

[21]  Sheng-Tzong Cheng,et al.  Genetic Optimal Deployment in Wireless Sensor Networks , 2005 .

[22]  Pei-Chann Chang,et al.  A hybrid genetic algorithm with dominance properties for single machine scheduling with dependent penalties , 2009 .

[23]  Parameswaran Ramanathan,et al.  Packet Dispersion Techniques and Capacity Estimation , 2004 .

[24]  Xiu Ju Liu Application of Improved Ant Colony Algorithm in QoS Routing Optimization , 2010 .