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Discrete multi-objective differential evolution algorithm for routing in wireless mesh network

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Abstract

The wireless mesh network (WMN) is a challenging technology that offers high quality services to the end users. With growing demand for real-time services in the wireless networks, quality-of-service-based routing offers vital challenges in WMNs. In this paper, a discrete multi-objective differential evolution (DMODE) approach for finding optimal route from a given source to a destination with multiple and competing objectives is proposed. The objective functions are maximization of packet delivery ratio and minimization of delay. For maintaining good diversity, the concepts of weight mapping crossover (WMX)-based recombination and dynamic crowding distances are implemented in the DMODE algorithm. The simulation is carried out in NS-2 and it is observed that DMODE substantially improves the packet delivery ratio and significantly minimizes the delay for various scenarios. The performance of DMODE, DEPT and NSGA-II is compared with respect to multi-objective performance measures namely as ‘spread’. The results demonstrate that DMODE generates true and well-distributed Pareto-optimal solutions for the multi-objective routing problem in a single run.

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References

  • Abbass HA (2002) The self-adaptive pareto differential evolution algorithm. In: Proceedings of congress on evolutionary computation, pp 831–836

  • Ajmal MM, Madani SA, Maqsood T, Bilal K, Nazir B, Hayat K (2013) Coordinated opportunistic routing protocol for wireless mesh networks. Comput Electr Eng 39:2442–2453

  • Akyildiz F, Wang X, Wang W (2005) Wireless mesh networks : a survey. Comput Netw 47(4):445–487

    Article  MATH  Google Scholar 

  • Alatas B, Akin E, Karci A (2008) MODENAR: multi-objective differential evolution algorithm for mining numeric association rules. Appl Soft Comput 8:646–656

    Article  Google Scholar 

  • Ali M, Siarry P, Pant M (2012) An efficient differential evolution based algorithm for solving multi-objective optimization problems. Eur J Oper Res 217:404–416

  • Alotaibi E, Mukherjee B (2012) A survey on routing algorithms for wireless Ad-Hoc and mesh networks. Comput Netw 56:940–965

  • Augusto CHP, Carvalho CB, da Silva MWR, de Rezende JF (2011) REUSE: a combined routing and link scheduling mechanism for wireless mesh networks. Comput Commun 34:2207–2216

  • Benyamina D, Hafid A, Hallam N, Gendreau M, Maureira JC (2012) A hybrid nature-inspired optimizer for wireless mesh networks design. Comput Commun 35:1231–1246

    Article  Google Scholar 

  • Borges VCM, Curado M, Monteiro E (2011) The impact of interference-aware routing metrics on video streaming in wireless mesh networks. Ad Hoc Netw 9:652–661

  • Brunoa R, Nurchis M (2010) Survey on diversity-based routing in wireless mesh networks: challenges and solutions. Comput Commun 33:269–282

  • Camelo M, Omana C, Castro H (2011) QoS routing algorithm based on multi-objective optimization for wireless mesh networks. IEEE Lat Am Trans 9(5):875–881

  • Campista MEM, Costa LHMK, Duarte OCMB (2012) A routing protocol suitable for backhaul access in wireless mesh networks. Comput Netw 56:703–718

  • Chowdhury KR, Di Felice M, Bononi L (2013) XCHARM: a routing protocol for multi-channel wireless mesh networks. Comput Commun 36:1485–1497

  • Das S, Abraham A, Konar A (2012) Adaptive clustering using improved differential evolution algorithm. IEEE Trans Syst Man Cybern Part B: Cybern 42(2):218–237

    Google Scholar 

  • Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31

  • Deb K (2001) Multi-objective optimization using evolutionary algorithms., Wiley-Interscience Series in Systems and OptimizationWiley, West Sussex

    MATH  Google Scholar 

  • Godfrey CO, Donald D (2009) Differential evolution: a handbook for global permutation based combinatorial optimization, 1st edn. Springer, Germany

  • Goldberg DE, Lingle R (1985) Alleles, loci and the traveling salesman problem. In: Grefenstette J (ed) 1st international conference on genetic algorithms, pp 154–159

  • Gujarathi AM, Babu BV (2010) Hybrid multi-objective differential evolution (H-MODE) for optimization of polyethylene terephthalate (PET) reactor. Int J Bio-Inspir Comput 2:213–231

  • Guo L, Peng Y, Wang X, Jiang D, Yu Y (2011) Performance evaluation for on-demand routing protocols based on OPNET modules in wireless mesh networks. Comput Electr Eng 37:106–114

  • Gu C, Zhang S, Xue X, Huang H (2011) Online wireless mesh network traffic classification using machine learning. J Comput Inf Syst 7:5 1524–1532

  • Jin Y, Wang W, Jiang Y, Yang M (2012) On a joint temporalspatial multi-channel assignment and routing scheme in resource-constrained wireless mesh networks. Ad Hoc Netw 401–420

  • Kai W, Gao Y, Guan J, Qin Y (2011) MDSDV: a modified DSDV routing mechanism for wireless mesh networks. J China Univ Posts Telecommun 18:34–39

  • Lin L, Gen M (2007) Bicriteria network design problem using interactive adaptive weight GA and priority-based encoding method. IEEE Trans Evol Comput (in review)

  • Li Y, Zhou L, Yang Y, Chao H-C (2011) Optimization architecture for joint multi-path routing and scheduling in wireless mesh networks. Math Comput Model 53:458–470

  • Luo B, Zheng J, Xie J, Wu J (2008) Dynamic crowding distance—new diversity maintenance strategy for MOEAs. 4th IEEE international conference natural computation, pp 580–585

  • Pal A, Nasipuri A (2011) A quality based routing protocol for wireless mesh networks. Pervasive Mob Comput 7:611–626

  • Pan QK, Wang L, Qian B (2009) A novel differential evolution algorithm for bi-criteria no-wait flow shop scheduling problem. Comput Oper Res 36(8):2498–2511

    Article  MathSciNet  MATH  Google Scholar 

  • Peng Y, Song Q, Yu Y, Wang F (2014) Fault-tolerant routing mechanism based on network coding in wireless mesh networks. J Netw Comput Appl 37: 259–272

  • Qian B, Wang L, Huang DX, Wang WL, Wang X (2009) An effective hybrid DE-based algorithm for multi-objective flow shop scheduling with limited buffers. Comput Oper Res 36(1):209–233

    Article  MathSciNet  MATH  Google Scholar 

  • Robic T, Filipic B (2005) DEMO: differential evolution for multiobjective optimization. Lecture Notes in Computer Science. Springer-Verlag, Berlin Heidelberg, pp 520–533

  • Rubio-Largo A, Vega-Rodrguez MA (2013) Applying MOEAs to solve the static routing and wavelength assignment problem in optical WDM networks. Eng Appl Artif Intell 26:1602–1619

  • Rubio-Largo A, Vega-Rodrguez MA, Gmez-Pulido JA, Snchez-Prez JM (2010) A differential evolution with pareto tournaments for solving the routing and wavelength assignment problem in WDM networks. In: Proceedings of the IEEE congress in Evolutionary Computation, Spain, pp 18–23

  • Rubio-Largo A, Vega-Rodrguez MA, Gmez-Pulido JA, Snchez-Prez JM (2013) Multiobjective metaheuristics for traffic grooming in optical networks. IEEE Trans Evol Comput 17(4):457–473

  • Storn R, Price K (1997) Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  • Tasgetiren MF, Suganthan PN, Pan Q-K (2010) An ensemble of discrete differential evolution algorithms for solving the generalized travelling salesman problem. Appl Math Comput 21:3356–3368

  • Xue F, Sanderson A, Graves R (2006) Multi-objective routing in wireless sensor networks with a differential evolution algorithm. IEEE international conference on networking, sensing and control, pp 880–885

  • Xu J, He H, Man H (2012) DCPE co-training for classification. Neuro Comput 86:75–85

  • Xu J, Yang G, Man H, He H (2013) L1 graph based on sparse coding for feature selection, vol 7951. Springer, Berlin, pp 594–601

  • Ya GY, Dong ZY, Wong KP (2008) A modified differential evolution algorithm with fitness sharing for power system planning. IEEE Trans Power Syst 23(2):514–522

  • Yetgin H, Cheung KTK, Hanzo L (2012) Multi-objective routing optimization using evolutionary algorithms. IEEE, wireless communications and networking conference, pp 1–6

  • Zhao L, Al-Dubai AY, Min G (2011) An efficient neighbourhood load routing metric for wireless mesh networks. Simul Model Pract Theory 19:1415–1426

  • Zhou A, Qu B-Y, Li H, Zhao S-Z, Suganthan PN, Zhang Q (2011) Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm Evol Comput 1:32–49

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Correspondence to R. Murugeswari.

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Communicated by V. Loia.

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Murugeswari, R., Radhakrishnan, S. Discrete multi-objective differential evolution algorithm for routing in wireless mesh network. Soft Comput 20, 3687–3698 (2016). https://doi.org/10.1007/s00500-015-1730-5

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