Advertisement

A Multi-objective Differential Evolution for QoS Multicast Routing

  • Wenhong Wei
  • Zhaoquan Cai
  • Yong Qin
  • Ming Tao
  • Lan Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10385)

Abstract

This paper presents a new multi-objective differential evolution algorithm (MODEMR) to solve the QoS multicast routing problem, which is a well-known NP-hard problem in mobile Ad Hoc networks. In the MODEMR, the network lifetime, cost, delay, jitter and bandwidth are considered as five objectives. Furthermore, three QoS constraints which are maximum allowed delay, maximum allowed jitter, and minimum requested bandwidth are included. In addition, we modify the crossover and mutation operators to build the shortest-path multicast tree to maximize network lifetime and bandwidth, minimize cost, delay and jitter. In order to evaluate the performance and the effectiveness of MODEMR, the experiments are conducted and compared with other algorithms for these problems. The simulation results show that our proposed method is capable of achieving faster convergence and more preferable for multicast routing in mobile Ad Hoc networks.

Keywords

Mobile Ad Hoc network Multicast routing Differential evolution Quality of service 

Notes

Acknowledgement

This work was supported by the National Nature Science Foundation of China (Nos. 61370185, 61402217), Guangdong Higher School Scientific Innovation Project (No. 2014KTSCX188), the outstanding young teacher training program of the Education Department of Guangdong Province (YQ2015158); and Guangdong Provincial Science and Technology Plan Projects (Nos. 2016A010101034, 2016A010101035). Guangdong Provincial High School of International and Hong Kong, Macao and Taiwan cooperation and innovation platform and major international cooperation projects (No. 2015KGJHZ027).

References

  1. 1.
    Storn, R., Price, K.: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 341–359 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Wei, W., Wang, J., Tao, M.: Constrained differential evolution with multiobjective sorting mutation operators for constrained optimization. Appl. Soft Comput. 33, 207–222 (2015)CrossRefGoogle Scholar
  3. 3.
    Zhou, X., Zhang, G., Hao, X., Yu, L.: A novel differential evolution algorithm using local abstract convex underestimate strategy for global optimization. Comput. Oper. Res. 75, 132–149 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Rajesh, K., Bhuvanesh, A., Kannan, S., Thangaraj, C.: Least cost generation expansion planning with solar power plant using differential evolution algorithm. Renew. Energy 85, 677–686 (2016)CrossRefGoogle Scholar
  5. 5.
    Malathy, P., Shunmugalatha, A., Marimuthu, T.: Application of differential evolution for maximizing the loadability limit of transmission system during contingency. In: Pant, M., Deep, K., Bansal, J.C., Nagar, A., Das, K. (eds.) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. AISC, vol. 437, pp. 51–64. Springer, Singapore (2016). doi: 10.1007/978-981-10-0451-3_6 CrossRefGoogle Scholar
  6. 6.
    Wei, W., Wang, J., Tao, M., Yuan, H.: Multi-objective constrained differential evolution using generalized opposition-based learning. Comput. Res. Dev. 53(6), 1410–1421 (2016)Google Scholar
  7. 7.
    Cheng, J., Yen, G.G., Zhang, G.: A grid-based adaptive multi-objective differential evolution algorithm. Inf. Sci. 367–368, 890–908 (2016)CrossRefGoogle Scholar
  8. 8.
    Liu, Y., Dong, M., Ota, K., Liu, A.: ActiveTrust: secure and trustable routing in wireless sensor networks. IEEE Trans. Inf. Forensics Secur. 11(9), 2013–2027 (2016)CrossRefGoogle Scholar
  9. 9.
    Tao, M., Lu, D., Yang, J.: An adaptive energy-aware multi-path routing strategy with load balance for wireless sensor networks. Wirel. Pers. Commun. 63(4), 823–846 (2012)CrossRefGoogle Scholar
  10. 10.
    Haghighat, A., Faez, K., Dehghan, M.: GA-based heuristic algorithms for QoS based multicast routing. Knowl. Based Syst. 16, 305–312 (2003)CrossRefGoogle Scholar
  11. 11.
    Koyama, A., Nishie, T., Arai, J., Barolli, L.: A GA-based QoS multicast routing algorithm for large-scale networks. Int. J. High Perform. Comput. Netw. 5, 381–387 (2008)CrossRefGoogle Scholar
  12. 12.
    Yen, Y., Chao, H., Chang, R., Vasilakos, A.: Flooding-limited and multi-constrained QoS multicast routing based on the genetic algorithm for MANETs. Math. Comput. Model. 53, 2238–2250 (2011)CrossRefGoogle Scholar
  13. 13.
    Karthikeyan, P., Baskar, S.: Genetic algorithm with ensemble of immigrant strategies for multicast routing in ad hoc networks. Soft. Comput. 19, 489–498 (2015)CrossRefGoogle Scholar
  14. 14.
    Sun, J., Fang, W., Wu, X., Xie, Z., Xu, W.: QoS multicast routing using a quantum-behaved particle swarm optimization algorithm. Eng. Appl. Artif. Intell. 24, 123–131 (2011)CrossRefGoogle Scholar
  15. 15.
    Bitam, S., Mellouk, A.: Bee life-based multi constraints multicast routing optimization for vehicular ad hoc networks. J. Netw. Comput. Appl. 36, 981–991 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Wenhong Wei
    • 1
  • Zhaoquan Cai
    • 2
  • Yong Qin
    • 1
  • Ming Tao
    • 1
  • Lan Li
    • 3
  1. 1.School of ComputerDongguan University of TechnologyDongguanChina
  2. 2.Huizhou UniversityHuizhouChina
  3. 3.School of SoftwareNanchang UniversityNanchangChina

Personalised recommendations