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Parallel multi-objective metaheuristics for smart communications in vehicular networks

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Abstract

This article analyzes the use of two parallel multi-objective soft computing algorithms to automatically search for high-quality settings of the Ad hoc On Demand Vector routing protocol for vehicular networks. These methods are based on an evolutionary algorithm and on a swarm intelligence approach. The experimental analysis demonstrates that the configurations computed by our optimization algorithms outperform other state-of-the-art optimized ones. In turn, the computational efficiency achieved by all the parallel versions is greater than 87 %. Therefore, the line of work presented in this article represents an efficient framework to improve vehicular communications.

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References

  • Alba E, Tomassini M (2002) Parallelism and evolutionary algorithms. Evolut Comput IEEE Trans 6(5):443–462

    Article  Google Scholar 

  • Alba E, Dorronsoro B, Luna F, Bouvry P (2005) A cellular multi-objective genetic algorithm for optimal broadcasting strategy in metropolitan MANETs. In: Proceedings of the 19th IEEE international symposium on parallel and distributed processing symposium, pp 1–8

  • Cheng H, Yang S (2010) Genetic algorithms with immigrant schemes for dynamic multicast problems in mobile ad hoc networks. EAAI 23:806–819

    Google Scholar 

  • Coello C, Lamont G, Van Veldhuizen D (2007) Evolutionary algorithms for solving multi-objective problems, vol 5. Springer, New York

    MATH  Google Scholar 

  • Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley-Interscience Series in Systems and Optimization, Wiley, New York

    MATH  Google Scholar 

  • Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. Evolut Comput IEEE Trans 6(2):182–197

    Article  Google Scholar 

  • Durillo JJ, Nebro AJ (2011) jMetal: a Java framework for multi-objective optimization. Adv Eng Softw 42:760–771

    Article  Google Scholar 

  • Durillo J, Nebro A, Luna F, Alba E (2008) A study of master-slave approaches to parallelize NSGA-II. In: IEEE international symposium on parallel and distributed processing, 2008. IPDPS 2008, pp 1–8

  • García-Nieto J, Alba E (2010) Automatic parameter tuning with metaheuristics of the AODV routing protocol for vehicular ad-hoc networks. In: EvoApplications (2), LNCS, vol 6025. Springer, pp 21–30

  • García-Nieto J, Toutouh J, Alba E (2010) Automatic tuning of communication protocols for vehicular ad hoc networks using metaheuristics. EAAI 23(5):795–805

    Google Scholar 

  • Huang CJ, Chuang YT, Hu KW (2009) Using particle swam optimization for QoS in ad-hoc multicast. Eng Appl Artif Intell 22(8):1188–1193

    Article  Google Scholar 

  • Krajzewicz D, Bonert M, Wagner P (2006) The open source traffic simulation package SUMO. In: RoboCup’06, 2016, Bremen, Germany, pp 1–10

  • Lee KC, Lee U, Gerla M (eds) (2009) Survey of routing protocols in vehicular ad hoc networks, chap 8. IGI Global, pp 149–170

  • Luna F, Nebro A, Alba E (2006) Parallel evolutionary multiobjective optimization. In: Nedjah N, Mourelle LM, Alba E (eds) Parallel evolutionary computations, studies in computational intelligence, vol 22. Springer, Berlin, Heidelberg, pp 33–56

    Chapter  Google Scholar 

  • Mezmaz M, Melab N, Kessaci Y et al (2011) A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. J Parallel Distrib Comput 71(11):1497–1508

    Article  Google Scholar 

  • Nebro A, Durillo J, Garcia-Nieto J, Coello C, Luna F, Alba E (2009) SMPSO: a new PSO-based metaheuristic for multi-objective optimization. In: IEEE symposium on computational intelligence in miulti-criteria decision-making, pp 66–73

  • Ns-2 (2014) The Network Simulator NS-2. http://www.isi.edu/nsnam. Accessed June 2014

  • Patil KV, Dhage MR (2013) The enhanced optimized routing protocol for vehicular ad hoc network. Int J Adv Res Comput Commun Eng 2(10):4013–4017

    Google Scholar 

  • Perkins C, Royer E, Das S (2003) Ad hoc on demand distance vector (AODV) routing (RFC 3561). Technical report, IETF MANET Working Group. http://tools.ietf.org/html/rfc3561. Accessed Aug 2003

  • Ruiz P, Dorronsoro B, Valentini G, Pinel F, Bouvry P (2011) Optimisation of the enhanced distance based broadcasting protocol for manets. J Supercomput 62:1213–1240

  • Said S, Nakamura M (2014) Master-slave asynchronous evolutionary hybrid algorithm and its application in vanets routing optimization. In: 3rd international conference on advanced applied informatics (IIAIAAI), 2014, pp 960–965

  • Segura C, Cervantes A, Nebro AI et al (2009) Optimizing the DFCN broadcast protocol with a parallel cooperative strategy of multi-objective evolutionary algorithms. In: Ehrgott M (ed) Evolutionary multi-criterion optimization, LNCS, vol 5467. Springer, Berlin, Heidelberg, pp 305–319

  • Sheskin DJ (2007) Handbook of parametric and nonparametric statistical procedures. Chapman & Hall/CRC, New York

    MATH  Google Scholar 

  • Toutouh J, Alba E (2012a) Multi-objective OLSR optimization for VANETs. In: IEEE 8th international conference on wireless and mobile computing, networking and communications (WiMob), pp 571–578

  • Toutouh J, Alba E (2012b) Parallel swarm intelligence for VANETs optimization. In: 2012 seventh international conference on P2P, parallel, grid, cloud and internet computing (3PGCIC). IEEE, pp 285–290

  • Toutouh J, Garcia-Nieto J, Alba E (2012a) Intelligent OLSR routing protocol optimization for VANETs. Vehic Technol IEEE Trans 61(4):1884–1894

    Article  Google Scholar 

  • Toutouh J, Nesmachnow S, Alba E (2012b) Fast energy-aware OLSR routing in VANETs by means of a parallel evolutionary algorithm. Cluster Comput 16(3):435–450

    Article  Google Scholar 

  • Zukarnain Z, Al-Kharasani N, Subramaniam S, Hanapi Z (2014) Optimal configuration for urban VANETs routing using particle swarm optimization. In: Proceeding of the international conference on artificial intelligence and computer science 2014, pp 1–6

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Acknowledgments

J. Toutouh is supported by Grant AP2010-3108 of the Spanish Ministry of Education. This research has been partially funded by project UMA/FEDER FC14-TIC36, and the Spanish MINECO project TIN2014-57341-R (http://moveon.lcc.uma.es). University of Malaga, International Campus of Excellence Andalucía Tech.

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  1. Dept. of Lenguajes y Ciencias de la Computación, University of Malaga, 29071, Málaga, Spain

    Jamal Toutouh & Enrique Alba

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  1. Jamal Toutouh
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  2. Enrique Alba
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Correspondence to Jamal Toutouh.

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The authors declare that they have no conflict of interest.

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Communicated by V. Loia.

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Toutouh, J., Alba, E. Parallel multi-objective metaheuristics for smart communications in vehicular networks. Soft Comput 21, 1949–1961 (2017). https://doi.org/10.1007/s00500-015-1891-2

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