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Improved genetic algorithm using different genetic operator combinations (GOCs) for multicast routing in ad hoc networks

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Abstract

In this paper, a Modified Topology Crossover (MTC), Energy-II and Energy-III mutations and Genetic Operator Combinations (GOCs) for integer coded Genetic Algorithm (GA) with sequence and topological representations are proposed to improve the efficiency of the GA for multicast routing in ad hoc networks. Combined lifetime improvement and time delay minimization are considered as objectives. To study the effect of genetic operators on the performance of multicast routing optimization problem, crossover methods such as sequence and topology crossover, topology crossover and mutation methods such as node mutation, energy mutation, inverse mutation and insert mutation are considered. Penalty parameter-less constraint handling scheme is used for handling the number of broken links which are identified during reproduction. The simulations are conducted on different size graphs generated using Waxman’s graph generator. Three case studies namely Case-1: Performance comparison of various crossover methods with node mutation, Case-2: Performance comparison of various mutation methods with the proposed MTC and Case-3: Performance comparisons of four GOCs are investigated. The above three cases are experimented with nonparametric statistical tests such as Friedman, Aligned Friedman and Quade. From these tests, it is proved that GOCs perform better for both large scale and small scale networks. These results also endorse that the proposed GOCs can be used to improve the GA for solving multicast routing problems more effectively.

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References

  • Acampora G, Loia V, Vitiello A (2011a) Exploiting timed automata based fuzzy controllers for voltage regulation in smart grids. In: IEEE international conference on fuzzy systems, Taipei

  • Acampora G, Cadenas JM, Loia V, Muñoz Ballester E (2011b) A multi-agent memetic system for human-based knowledge selection. IEEE Trans Syst Man Cybern Part A 41(5):946–960

  • Acampora G, Cadenas JM, Loia V, Muñoz Ballester E (2011c) Achieving memetic adaptability by means of agent-based machine learning. IEEE Trans Ind Inf 7(4):557–569

  • Acampora G, Loia V, Salerno S, Vitiello A (2012) A hybrid evolutionary approach for solving the ontology alignment problem. Int J Intell Syst 27(3):189–216

    Google Scholar 

  • Asokan R, Natarajan AM, Venkatesh C (2008) Ant based dynamic source routing protocol to support multiple quality of service (QoS) metrics in mobile ad hoc networks. Int J Comp Sci Sec 2:48–56

    Google Scholar 

  • Baumann R, Heimlicher S, Strasser M, Weibel A (2007) A survey on routing metrics. TIK Report, Computer Engineering and Networks Laboratory, ETH-Zentrum

  • Cao Q, Zhou J, Li C, Huang R (2010) A genetic algorithm based on extended sequence and topology encoding for the multicast protocol in two-tiered WSN. Exp Sys Appl 37:1684–1695

    Article  Google Scholar 

  • Caro GD, Ducatelle F, Gambardella LM (2004) AntHocNet: An adaptive nature-inspired algorithm for routing in mobile ad hoc networks. Technical Report, Dalle Molle Institute for Artificial Intelligence, Galleria 2, 6928 Manno

  • Chao H-C, Yen Y-S, Chan Y-K, Park JH (2008) A genetic algorithm for energy-efficient based multicast routing on MANETs. Comp Comm 31:2632–2641

    Article  Google Scholar 

  • Chao H-C, Yen Y-S, Chang R-S, Vasilakos A (2011) Flooding-limited and multi-constrained QoS multicast routing based on the genetic algorithm for MANETs. Math Comp Mode 53:2238–2250

    Article  Google Scholar 

  • Deb K (2000) An efficient constraint-handling method for genetic algorithms. Comp Meth in Appl Mech and Eng 186:311–338

    Article  MATH  Google Scholar 

  • Derrac J, Garcia S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evol Comp 1:3–18

    Article  Google Scholar 

  • Eiben AE, Smith JE (2010) Introduction to evolutionary computing genetic algorithms. Springer, Berlin

    Google Scholar 

  • Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison Wesley, Boston

    MATH  Google Scholar 

  • Haghighat AT, Faez K, Dehghan M (2003) GA-based heuristic algorithms for QoS based multicast routing. Know Bas Sys 16:305–312

    Article  Google Scholar 

  • Huang Y-M, Chiang T-C, Liu C-H (2007) A near-optimal multicast scheme for mobile ad hoc networks using a hybrid genetic algorithm. Exp Sys with Appl 33:734–742

    Article  Google Scholar 

  • Huang C-J, Chuang Y-T, Hu K-W (2009) Using particle swam optimization for QoS in ad hoc multicast. Engg Appl of Arti Intel 22:1188–1193

    Article  Google Scholar 

  • Iacca G, Neri F, Mininno E, Ong YS, Lim MH (2012) Ockham's Razor in memetic computing: three stage optimal memetic exploration. Inf Sci 188:17–43

    Google Scholar 

  • Jain S, Sharma JD (2011) QoS constraints multicast routing for residual bandwidth optimization using evolutionary algorithm. Int J Comp Theo and Eng 3:211–216

    Article  Google Scholar 

  • Kong J (2005) Building underwater ad-hoc networks and sensor networks for large scale real-time aquatic applications. Int conf Milit Comm, Atlantic City

    Google Scholar 

  • Koyama A, Nishie T, Arai J, Barolli L (2008) A GA-based QoS multicast routing algorithm for large-scale networks. Int J High Perf Comp Net 5:381–387

    Google Scholar 

  • Le MN, Ong YS, Jin Y, Sendhoff B (2009) Lamarckian memetic algorithms: local optimum and connectivity structure analysis. Memetic Comput 1(3):175–190

    Google Scholar 

  • Loia V, Vaccaro A (2011) A decentralized architecture for voltage regulation in Smart Grids. IEEE Int Symp Ind Electron (ISIE), Poland

  • Oliveira C, Pardalos P (2005) A survey of combinatorial optimization problems in multicast routing. Comp and Oper Rese 32:1953–1981

    Article  MATH  Google Scholar 

  • Pinto D, Barán B (2005) Solving multi objective multicast routing problem with a new ant colony optimization approach. Inl IFIP/ACM Latin America Network Conf, Colombia

  • Salama HF, Reeves DS, Viniotis Y (1997) Evaluation of multicast routing algorithms for real-time communication on high-speed networks. IEEE J on Sel Ar in Comm 15:332–345

    Article  Google Scholar 

  • Sateesh Kumar P, Ramachandram S (2008) Genetic zone routing protocol. Theo and Appl Info Tech 4:789–794

    Google Scholar 

  • Sesay S, Yang Z, He J (2004) A survey on mobile ad hoc wireless network. InfoTech 3:168–175

    Google Scholar 

  • SivaRamMurthy C, Manoj BS (2004) Ad hoc wireless networks—architectures and protocols. Pearson Education, India

    Google Scholar 

  • 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. Progr Nat Sci 18:331–336

    Article  Google Scholar 

  • Wang B, Gupta SKS (2003) On maximizing lifetime of multicast trees in wireless ad hoc networks. Intl Conf Para Proc, Kaohsiung

  • Wang B, Hou J (2000) A survey on multicast routing and its QoS extensions: problems, algorithms, and protocols. IEEE Trans Net 14:22–36

    Google Scholar 

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Correspondence to P. Karthikeyan.

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Communicated by V. Loia.

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Karthikeyan, P., Baskar, S. & Alphones, A. Improved genetic algorithm using different genetic operator combinations (GOCs) for multicast routing in ad hoc networks. Soft Comput 17, 1563–1572 (2013). https://doi.org/10.1007/s00500-012-0976-4

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