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Neural Computing and Applications

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30-10-2017 | Original Article

QoS multicast routing for wireless mesh network based on a modified binary bat algorithm

Authors: Yassine Meraihi, Dalila Acheli, Amar Ramdane-Cherif

Published in: Neural Computing and Applications | Issue 7/2019

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Abstract

The quality of service multicast routing problem is a very important research issue for transmission in wireless mesh networks. It is known to be NP-complete problem, so many heuristic algorithms have been employed for solving the multicast routing problem. This paper proposes a modified binary bat algorithm applied to solve the QoS multicast routing problem for wireless mesh network which satisfies the requirements of multiple QoS constraints such as delay, delay jitter, bandwidth and packet loss rate to get low-cost multicasting tree. The binary bat algorithm has been modified by introducing the inertia weight w in the velocity update equation, and then the chaotic map, uniform distribution and gaussian distribution are used for choosing the right value of w. The aim of these modifications is to improve the effectiveness and robustness of the binary bat algorithm. The simulation results reveal the successfulness, effectiveness and efficiency of the proposed algorithms compared with other algorithms such as genetic algorithm, particle swarm optimization, quantum-behaved particle swarm optimization algorithm, bacteria foraging-particle swarm optimization, bi-velocity discrete particle swarm optimization and binary bat algorithm.

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Wang Z, Crowcroft J (1996) Quality-of-service routing for supporting multimedia applications. IEEE J Sel Areas Commun 14(7):1228–1234CrossRef

Hwang RH, Do WY, Yang SC (2000) Multicast routing based on genetic algorithms. J Inf Sci Eng 16(6):885–901

Haghighat AT, Faez K, Dehghan M, Mowlaei A, Ghahremani Y (2003) GA-based heuristic algorithms for QoS based multicast routing. Knowl-Based Syst 16(5):305–312CrossRef

Sun B, Pi S, Gui C, Zeng Y, Yan B, Wang W, Qin Q (2008) Multiple constraints QoS multicast routing optimization algorithm in MANET based on GA. Prog Nat Sci 18(3):331–336CrossRef

Yen YS, Chao HC, Chang RS, Vasilakos A (2011) Flooding-limited and multi-constrained QoS multicast routing based on the genetic algorithm for MANETs. Math Comput Modell 53(11):2238–2250CrossRef

Tseng SY, Lin CC, Huang YM (2008) Ant colony-based algorithm for constructing broadcasting tree with degree and delay constraints. Expert Syst Appl 35(3):1473–1481CrossRef

Wang H, Xu H, Yi S, Shi Z (2011) A tree-growth based ant colony algorithm for QoS multicast routing problem. Expert Syst Appl 38(9):11787–11795CrossRef

Ghaboosi N, Haghighat AT (2007) Tabu search based algorithms for bandwidth-delay-constrained least-cost multicast routing. Telecommun Syst 34(3–4):147–166CrossRef

Wang H, Meng X, Zhang M, Li Y (2010) Tabu search algorithm for RP selection in PIM-SM multicast routing. Comput Commun 33(1):35–42CrossRef

10.

Liu J, Sun J, Xu W-B (2006) QoS multicast routing based on particle swarm optimization. In: Corchado E, Yin H, Botti V, Fyfe C (eds) IDEAL 2006. LNCS, vol 4224, Springer, Heidelberg, pp 936–943

11.

Wang H, Meng X, Li S, Xu H (2010) A tree-based particle swarm optimization for multicast routing. Comput Netw 54(15):2775–2786MATHCrossRef

12.

Sun J, Fang W, Wu X, Xie Z, Xu W (2011) QoS multicast routing using a quantum-behaved particle swarm optimization algorithm. Eng Appl Artif Intell 24(1):123–131CrossRef

13.

Pradhan R, Kabat M-R, Sahoo S-P (2013) A bacteria foraging-particle swarm optimization algorithm for QoS multicast routing. In: Panigrahi BK et al (eds) SEMCCO 2013. LNCS. vol 8297, Springer, Berlin, pp 590-600

14.

Shen M, Zhan ZH, Chen WN, Gong YJ, Zhang J, Li Y (2014) Bi-velocity discrete particle swarm optimization and its application to multicast routing problem in communication networks. IEEE Trans Ind Electr 61(12):7141–7151CrossRef

15.

Abdel-Kader RF (2011) An improved discrete PSO with GA operators for efficient QoS-multicast routing. Int J Hybrid Inf Technol 4(2):23–38

16.

Yang X-S (2010). A new metaheuristic bat-inspired algorithm. In: Gonzalez JR et al (eds) Nature inspired cooperative strategies for optimization (NICSO 2010). SCI vol 284, Springer, Berlin, pp. 65–74

17.

Nakamura RY, Pereira LA, Costa KA, Rodrigues D, Papa JP, Yang XS (2012). BBA: a binary bat algorithm for feature selection. In: 2012 25th SIBGRAPI conference on graphics, patterns and images, IEEE, pp 291–297

18.

Yilmaz S, Kucuksille EU (2013) Improved bat algorithm (IBA) on continuous optimization problems. Lect Notes Softw Eng 1(3):279CrossRef

19.

Gandomi AH, Yang XS, Alavi AH, Talatahari S (2013) Bat algorithm for constrained optimization tasks. Neural Comput Appl 22(6):1239–1255CrossRef

20.

Tamiru AL, Hashim FM (2013) Application of bat algorithm and fuzzy systems to model exergy changes in a gas turbine. In: Artificial intelligence, evolutionary computing and metaheuristics, Springer, Berlin Heidelberg, pp 685–719

21.

Cai X, Wang L, Kang Q, Wu Q (2014) Bat algorithm with Gaussian walk. Int J Bio-Inspir Comput 6(3):166–174CrossRef

22.

Sabba S, Chikhi S (2014) A discrete binary version of bat algorithm for multidimensional knapsack problem. Int J Bio-Inspir Comput 6(2):140–152CrossRef

23.

Abdel-Raouf O, Abdel-Baset M, El-Henawy I (2014) An improved chaotic bat algorithm for solving integer programming problems. Int J Mod Educ Comput Sci 6(8):18CrossRef

24.

Yilmaz S, Kucuksille EU (2015) A new modification approach on bat algorithm for solving optimization problems. Appl Soft Comput 28:259–275CrossRef

25.

Byksaat S (2015) Bat algorithm application for the single row facility layout problem. In: Recent advances in swarm intelligence and evolutionary computation, Springer International Publishing, Berlin, pp 101–120

26.

Fister I, Rauter S, Yang XS, Ljubi K (2015) Planning the sports training sessions with the bat algorithm. Neurocomputing 149:993–1002CrossRef

27.

Saji Y, Riffi ME (2016) A novel discrete bat algorithm for solving the travelling salesman problem. Neural Comput Appl 27(7):1853–1866CrossRef

28.

Oshaba AS, Ali ES, Elazim SA (2017) PI controller design for MPPT of photovoltaic system supplying SRM via BAT search algorithm. Neural Comput Appl 28(4):651–667CrossRef

29.

Zhao D, He Y (2015) Chaotic binary bat algorithm for analog test point selection. Analog Integr Circ Sig Process 84(2):201–214CrossRef

30.

Mirjalili S, Mirjalili SM, Yang X-S (2014) Binary bat algorithm. Neural Comput Appl 25(3–4):663–681CrossRef

31.

Mirjalili S, Lewis A (2013) S-shaped versus V-shaped transfer functions for binary particle swarm optimization. Swarm Evolut Comput 9:1–14CrossRef

32.

Saremi S, Mirjalili S, Lewis A (2014) How important is a transfer function in discrete heuristic algorithms. Neural Comput Appl 26(3):625–640CrossRef

33.

Rashedi E, Nezamabadi-Pour H, Saryazdi S (2010) BGSA: binary gravitational search algorithm. Nat Comput 9(3):727–745MathSciNetMATHCrossRef

34.

Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: IEEE international conference on computational cybernetics and simulation, pp 4104–4108

35.

Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceedings of the congress on evolutionary computation (CEC), pp 1945–1950

36.

Caponetto R, Fortuna L, Fazzino S, Xibilia MG (2003) Chaotic sequences to improve the performance of evolutionary algorithms. IEEE Trans Evolut Comput 7(3):289–304CrossRef

37.

Chuang LY, Yang CH, Li JC (2011) Chaotic maps based on binary particle swarm optimization for feature selection. Appl Soft Comput 11(1):239–248CrossRef

38.

Heidari AA, Abbaspour RA, Jordehi AR (2015) An efficient chaotic water cycle algorithm for optimization tasks. Neural Comput Appl 1–29

39.

Saremi S, Mirjalili S, Lewis A (2014) Biogeography-based optimisation with chaos. Neural Comput Appl 25(5):1077–1097CrossRef

40.

Lei X, Du M, Xu J, Tan Y (2014) Chaotic Fruit Fly Optimization Algorithm. In : Tan, Y. et al. (eds) ICSI 2014. LNCS vol 8794, Springer Switzerland, pp 74–85

41.

Kanso A, Smaoui N (2009) Logistic chaotic maps for binary numbers generations. Chaos, Solitons Fractals 40(5):2557–2568MATHCrossRef

42.

dos Santos Coelho L, Sauer JG, Rudek M (2009) Differential evolution optimization combined with chaotic sequences for image contrast enhancement. Chaos, Solitons Fractals 42(1):522–529CrossRef

43.

Waxman BM (1988) Routing of multipoint connections. IEEE J Sel Areas Commun 6(9):1617–1622CrossRef

Title: QoS multicast routing for wireless mesh network based on a modified binary bat algorithm
Authors: Yassine Meraihi
Dalila Acheli
Amar Ramdane-Cherif
Publication date: 30-10-2017
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 7/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-017-3252-9

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