Abstract
In this paper, an Ensemble of Immigrant Strategies with Genetic Algorithm (EISGA) which optimizes the combined objectives of network lifetime and delay is proposed for solving multicast routing problem. Immigrant strategies are the specific replacement operators designed for dynamic optimization problems and are naturally suited for multicast routing in ad hoc networks. The proposed system ensembles random immigrant with random replacement, random immigrant with worst replacement, elitism-based immigrant and hybrid immigrant strategies. The sequence and topological coding with genetic operators such as modified topology crossover, energy mutation and node mutation are employed in EISGA. The performance of four variants of genetic algorithms formed from these immigrant strategies is evaluated in two different network topologies, with different range of immigrant probability values. Results show that fixing of probability values for various immigrant strategies is very difficult. The proposed EISGA, with equal probability and adaptive probability, is evaluated on four different networks with 10, 20, 30 and 40 nodes on two kinds of topologies. The performance of the proposed EISGA with adaptive probability is assessed in various Learning Period (LP) to determine the suitable LP and is compared with other existing algorithms using non-parametric statistical tests with average ranking. These results endorse that the proposed EISGA improves the performance of GA in solving multicast routing problems effectively.
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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
Acampora G, Loia V, Vitiello A (2011) Exploiting Timed Automata based Fuzzy Controllers for voltage regulation in Smart Grids. In: IEEE International Conference on Fuzzy Systems, Taipei, Taiwan
Acampora G, Cadenas JM, Loia V, Muñoz Ballester E (2011) 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 (2011) Achieving memetic adaptability by means of agent-based machine learning. IEEE Trans Ind Inform 7(4):557–569
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 Comput Sci Sec 2:48–56
Baumann R, Heimlicher S, Strasser M, Weibel A (2007) A survey on routing metrics. TIK Report, Computer Engineering and Networks Laboratory, ETH-Zentrum, Switzerland
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
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, Switzerland
Chao HC, Yen YS, Chan YK, Park JH (2008) A genetic algorithm for energy-efficient based multicast routing on MANETs. Comput Commum 31:2632–2641
Cheng H, Yang S (2010) Genetic algorithms with immigrants schemes for dynamic multicast problems in mobile ad hoc networks. Eng Appl Artif Intell 23:806–819
Deb K (2000) An efficient constraint-handling method for genetic algorithms. Comput Methods Appl Mech Eng 186:311–338
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 Evol Comp 1:3–18
Eiben AE, Smith JE (2010) Introduction to evolutionary computing genetic algorithms. Springer, Berlin
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison Wesley, Boston
Haghighat AT, Faez K, Dehghan M (2003) GA-based heuristic algorithms for QoS based multicast routing. Knowl Based Syst 16:305–312
Huang YM, Chiang TC, Liu CH (2007) A near-optimal multicast scheme for mobile ad hoc networks using a hybrid genetic algorithm. Exp Syst Appl 33:734–742
Huang CJ, Chuang YT, Hu KW (2009) Using particle swam optimization for QoS in ad hoc multicast. Eng Appl Artif Intel 22:1188– 1193
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
Jain S, Sharma JD (2011) QoS constraints multicast routing for residual bandwidth optimization using evolutionary algorithm. Int J Comput Theor Eng 3:211–216
Karthikeyan P, Baskar S, Alphones A (2012) Improved genetic algorithm using different genetic operator combinations (GOCs) for multicast routing in ad hoc networks. Soft Comput 17:1563–1572
Kong J (2005) Building underwater ad-hoc networks and sensor networks for large scale real-time aquatic applications. In: International conference on Military Communications, Atlantic City, NJ
Koyama A, Nishie T, Arai J, Barolli L (2008) A GA-based QoS multicast routing algorithm for large-scale networks. Int J High Perform Comput Net 5:381–387
Le MN, Ong YS, Jin Y, Sendhoff B (2009) Lamarckian memetic algorithms: local optimum and connectivity structure analysis. Memet Comput 1(3):175–190
Loia V, Vaccaro A (2011) A decentralized architecture for voltage regulation in Smart Grids. in: IEEE international symposium on industrial electronics (ISIE), Poland
Oliveira C, Pardalos P (2005) A survey of combinatorial optimization problems in multicast routing. Comput Oper Res 32:1953–1981
Pilski M, Seredynski F (2008) Genetic algorithm based metaheuristic for energy efficient routing in ad hoc networks. Intel Info Syst XVI: 89–98
Pinto D, Barán B (2005) Solving multi objective multicast routing problem with a new ant colony optimization approach. In: IFIP/ACM Latin America Network Conference, Colombia
Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417
Salama HF, Reeves DS, Viniotis Y (1997) Evaluation of multicast routing algorithms for real-time communication on high-speed networks. IEEE J Sel Areas Commun 15:332–345
Sateesh Kumar P, Ramachandram S (2008) Genetic zone routing protocol. Theor Appl Inf Tech 4:789–794
Sesay S, Yang Z, He J (2004) A survey on mobile ad hoc wireless network. InfoTech 3:168–175
SivaRamMurthy C, Manoj BS (2004) Ad hoc wireless networks—architectures and protocols. Pearson Education, India
Suganthan PN, Mallipeddi R, Pan QK, Tasgetiren MF (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11(2):1679–1696
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 18:331–336
Tinos R, Yang S (2007) A self-organizing random immigrants genetic algorithm for dynamic optimization problems. Genet Program Evol Mach 8(3):255–286
Wang B, Hou J (2000) A survey on multicast routing and its QoS extensions: problems, algorithms, and protocols. IEEE Trans Netw 14:22–36
Wang B, Gupta SKS (2003) On maximizing lifetime of multicast trees in wireless ad hoc networks. In: International Conference on Para Proc, Kaohsiung, Taiwan
Yang S, Tinos R (2007) A hybrid immigrants scheme for genetic algorithms in dynamic environments. Int J Autom Comput 4(3):243–254
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 Model 53:2238–2250
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Karthikeyan, P., Baskar, S. Genetic algorithm with ensemble of immigrant strategies for multicast routing in Ad hoc networks. Soft Comput 19, 489–498 (2015). https://doi.org/10.1007/s00500-014-1269-x
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DOI: https://doi.org/10.1007/s00500-014-1269-x