Multi-constraint QoS routing using a customized lightweight evolutionary strategy

Abstract

The ever-increasing transmitting real-time multimedia applications such as VoIP and video conference through the Internet require developing routings methods which guarantee quality of service (QoS) according to the needs of these applications. For these types of applications, a fundamental issue is how to find a feasible path that satisfies multiple constraints. This problem which is known as multi-constraint QoS routing is an NP-complete one, and many research has been devoted to solving it. However, there are still many gaps especially in terms of complexity and speed of the algorithms that must be bridged in order for these methods to be practical. In this regard, in this paper, a novel multi-constraint QoS routing algorithm based on evolutionary strategies is proposed which is lightweight and finds feasible solutions in a very short time. The main reason behind these features is due to the proposed inventive gene decoding mechanism that makes the algorithm needless of any complex evolutionary operators and validation phases. Moreover, a cost function is developed for evaluating the fitness of the potential solutions, which is so flexible for using for as many constraints as required. Therefore, the algorithm is able to search the solution space, no matter how big they are, efficiently and quickly. Simulation results on different network topologies and packet traffics show that our method outperforms competitor algorithms in terms of run time and success ratio, and it is more reliable in different network and traffic scenarios.

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References

  1. Abdullah N, Al-wesabi OA, Baklizi M, Kadhum MM (2017) Intelligent routing algorithm using genetic algorithm (IRAGA). In: International conference of reliable information and communication technology. Springer, pp 255–263

  2. Ahn CW, Ramakrishna RS (2002) A genetic algorithm for shortest path routing problem and the sizing of populations. IEEE Trans Evolut Comput 6:566–579

    Article  Google Scholar 

  3. Alejandro RR, Chin KW, Soh S, Raad R (2015) On the performance of online and offline green path establishment techniques. EURASIP J Wirel Commun Netw 1:1–17

    Google Scholar 

  4. Bäck T (1995) Evolution strategies: an alternative evolutionary algorithm. In: European conference on artificial evolution. Springer, Berlin, Heidelberg, pp 1–20

  5. Back T, Rudolph G, Schwefel H (1993) Evolutionary programming and evolution strategies: similarities and differences. In: Proceedings of the second annual conference on evolutionary programming, pp 11–22

  6. Barolli L, Koyama A, Sawada H, Suganuma T, Shiratori N (2002) A new QoS routing approach for multimedia applications based on genetic algorithms. In: Proceedings of the international IEEE conference on cyber worlds, pp 289–295

  7. Barolli L, Koyama A, Suganuma T, Shiratori N (2003) A genetic algorithm based QoS routing method for multimedia communications over high-speed networks. IPSJ J 44(2):544–552

    Google Scholar 

  8. Barolli L, Koyama A, Matsumoto K, Apduhan BO (2004) A GA-based Multi-purpose optimization algorithm for QoS routing. In: Proceedings of the international IEEE conference on advanced information networking and applications, pp 23–28

  9. Benlai L, Yu J (2015) One multi-constraint QoS routing algorithm CGEA based on ant colony system. In: Information science and control engineering (ICISCE), 2nd international conference on IEEE, pp 848–851

  10. Chitra C, Subbaraj P (2012) A non-dominated sorting genetic algorithm solution for shortest path routing problem in computer networks. Expert Syst Appl J 39:1518–1525

    Article  Google Scholar 

  11. Chun B, Zhao B, Kubiatowicz J (2005) Impact of neighbor selection on performance and resilience of structured P2P networks. In: International workshop on peer-to-peer systems, pp 264–274

  12. Deng W, Zhao HM, Yang XH, Xiong JX, Sun M, Li B (2017a) Study on an improved adaptive PSO algorithm for solving multi-objective gate assignment. Appl Soft Comput 59:288–302

    Article  Google Scholar 

  13. Deng W, Zhao HM, Zou L, Li GY, Yang XH, Wu DQ (2017b) A novel collaborative optimization algorithm in solving complex optimization problems. Soft Comput 21(15):4387–4398

    Article  Google Scholar 

  14. Feng G, Curry E, Intizar Ali M, Bhiri S, Mileo A (2014) Qos-aware complex event service composition and optimization using genetic algorithms. In: Service-oriented computing. Springer, pp 386–393

  15. Gu B, Sheng VS (2016) A robust regularization path algorithm for \(\nu \)-support vector classification. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2016.2527796

    Google Scholar 

  16. Gu B, Sun X, Sheng V (2016) Structural minimax probability machine. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2016.2544779

    Google Scholar 

  17. Jain S, Sharma JD (2012) Tree structured encoding based multi-objective multicast routing algorithm. Int J Phys Sci 7:1622–1632

    Google Scholar 

  18. Karthikeyan P, Baskar S (2015) Genetic algorithm with ensemble of immigrant strategies for multicast routing in Ad hoc networks. Soft Comput 19:489–498

    Article  Google Scholar 

  19. Karthikeyan P, Baskar S, Alphones A (2013) Improved genetic algorithm using different genetic operator combinations (GOCs) for multicast routing in ad hoc networks. Soft Comput 17:1563–1572

    Article  Google Scholar 

  20. Kong Y, Zhang MJ, Ye DY (2016) A belief propagation-based method for task allocation in open and dynamic cloud environments. Knowl Based Syst 115:123–132

    Article  Google Scholar 

  21. Kormza T, Krunz M (2001) Multi-constrained optimal path selection. In: Proceedings of the twentieth annual joint conference of the IEEE computer and communications societies, pp 834–843

  22. Leela R, Thanulekshmi N, Selvakumar S (2011) Multi-constraint QoS unicast routing using genetic algorithm (MURUGA). Appl Soft Comput J 11:1753–1761

    Article  Google Scholar 

  23. Li P, Guo S, Yu S, Vasilakos AV (2012) CodePipe: an opportunistic feeding and routing protocol for reliable multicast with pipelined network coding. In: Proceedings of the IEEE conference on INFOCOM, pp 100–108

  24. Liu L, Peng Y, Xu W (2015) To converge more quickly and effectively—mean field annealing based optimal path selection in WMN. Inf Sci J 294:216–226

    MathSciNet  Article  MATH  Google Scholar 

  25. Liu Q, Cai WD, Shen J, Fu ZJ, Liu XD, Linge N (2016) A speculative approach to spatial–temporal efficiency with multi-objective optimization in a heterogeneous cloud environment. Secur Commun Netw 9(17):4002–4012

    Article  Google Scholar 

  26. Martín V, Skorin-Kapov L, Ebrahimi T (2014) Quality of service versus quality of experience. Quality of experience. Springer International Publishing, Berlin, pp 85–96

    Google Scholar 

  27. Munetomo M, Takai Y, Sato Y (1998) An adaptive routing algorithm with load balancing by a genetic algorithm. IPSJ J 39(2):219–227

    Google Scholar 

  28. Ni M (2011) A simple and fast algorithm for restricted shortest path problem. In: Proceedings of the international IEEE conference on computational sciences and optimization. pp 99–102

  29. Rong H, Ma T, Tang M, Cao J (2017) A novel subgraph K\(^{+}\)-isomorphism method in social network based on graph similarity detection. Soft Comput. https://doi.org/10.1007/s00500-017-2513-y

    Google Scholar 

  30. Roy A, Das SK (2004) A QoS-based mobile multicast routing protocol using multi-objective genetic algorithm. Wirel Netw J 10:271–286

    Article  Google Scholar 

  31. Rudolph G (1994) Convergence of non-elitist strategies. In: Proceedings of the first IEEE conference on evolutionary computation, pp 63–66

  32. Salimans T, Ho J, Chen X, Sutskever I (2017) Evolution strategies as a scalable alternative to reinforcement learning. arXiv preprint arXiv:1703.03864

  33. Ting L, Zhu J (2013) A genetic algorithm for finding a path subject to two constraints. Appl Soft Comput J 13:891–898

    Article  Google Scholar 

  34. Vasilakos A, Ricudis C, Anagnostakis K, Pedryca W, Pitsillides A (1998) Evolutionary-fuzzy prediction for strategic QoS routing in broadband networks. In: Proceddings of the IEEE world congress on computational intelligence and fuzzy systems, pp 1488–1493

  35. Voigt H, Mfihlenbein H, Cvetkovid D (1995) Fuzzy recombination for the breeder genetic algorithm. In: Proceedings of the 6th international conference, pp 104–111

  36. Waxman BM (1998) Routing of multipoint connection. IEEE J Sel Areas Commun 6:1617–1622

    Article  Google Scholar 

  37. Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 6:80–83

    Article  Google Scholar 

  38. Xue Y, Jiang JM, Zhao BP, Ma TH (2017) A self-adaptive artificial bee colony algorithm based on global best for global optimization. Soft Comput. https://doi.org/10.1007/s00500-017-2547-1

    Google Scholar 

  39. Yena YS, Chao HC, Changd RS, Vasilakos A (2011) Flooding-limited and multi-constrained QoS multicast routing based on the genetic algorithm for MANETs. Math Comput Model J 53:2238–2250

    Article  Google Scholar 

  40. Yin PY, Chang RI, Chao CC, Chu YT (2014) Niched ant colony optimization with colony guides for QoS multicast routing. J Netw Comput Appl 40:61–72

    Article  Google Scholar 

  41. Yuan C, Xia Z, Sun X (2017) Coverless image steganography based on SIFT and BOF. J Internet Technol 18(2):209–216

    Google Scholar 

  42. Zaheeruddin, Lobiyal DK, Prasad S (2017) Ant based Pareto optimal solution for QoS aware energy efficient multicast in wireless networks. Appl Soft Comput 55:72–81. https://doi.org/10.1016/j.asoc.2017.01.029

  43. Zeng Y, Xiang K, Li D, Vasilakos A (2013) Directional routing and scheduling for green vehicular delay tolerant networks. Wirel Netw 19(2):161–173

    Article  Google Scholar 

  44. Zhang Y, Huang H, Lin Z, Hao Z, Hu G (2016) Running-time analysis of evolutionary programming based on Lebesgue measure of searching space. Neural Comput Appl, pp 1–10

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Correspondence to Hadi Soltanizadeh.

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Communicated by V. Loia.

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Torkzadeh, S., Soltanizadeh, H. & Orouji, A.A. Multi-constraint QoS routing using a customized lightweight evolutionary strategy. Soft Comput 23, 693–706 (2019). https://doi.org/10.1007/s00500-018-3018-z

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Keywords

  • Evolutionary strategy
  • Genetic algorithm
  • Multi-constraint
  • QoS routing
  • Computer networks