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Global migration strategy with moving colony for hierarchical distributed evolutionary algorithms

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Abstract

In the established hierarchical distributed evolutionary algorithms (HDEAs), object of global migration is individual. To obtain better solutions of concrete problems, global migration strategy with moving colony is proposed in this paper. In global migration based on the proposed strategy, migration object is subpopulation which moves between groups. Such global migration can increase the efficiency of thereafter local migration. Moreover, realizing it even needs no communication because it can be executed by regrouping subpopulations. In our experiments, the basement of parallelism is an EA for the Traveling Salesman Problem. Outcomes of HDEAs based on proposed scheme which have different global migration topology are compared with those of traditional ones on nine benchmark instances. The results show that a HDEA based on the proposed strategy having the ring global topology performs better than traditional HDEAs for high difficulty instances. However, the advantage of that having the random global topology is not so significant because of conflicting migrations arisen from this topology.

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References

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

    Article  Google Scholar 

  • Arnaldo I, Contreras I, Millán-Ruiz D, Hidalgo JI, Krasnogor N (2013) Matching island topologies to problem structure in parallel evolutionary algorithms. Soft Comput 17(7):1209–1225

    Article  Google Scholar 

  • Cai ZH, Peng JG, Gao W, Wei W, Kang LS (2005) An improved evolutionary algorithm for the traveling salesman problem. Chin J Comput 28(5):823–828

    MathSciNet  Google Scholar 

  • Cantu-Paz E (1998) A survey of parallel genetic algorithms. Calculateurs Parallles, Reseaux et Systems Repartis 10(2):141–171

  • Choi JN, Oh SK, Pedrycz W (2009) Identification of fuzzy relation models using hierarchical fair competition-based parallel genetic algorithms and information granulation. Appl Math Model 33(6):2791–2807

    Article  MATH  Google Scholar 

  • Cohoon JP, Hegde SU, Martin WN, Richards D (1987) Punctuated equilibria: a parallel genetic algorithm. In: Proceedings of the second international conference on genetic algorithms on genetic algorithms and their application, pp 148–154

  • Fernandez F, Tomassini M, Vanneschi L (2001) Studying the influence of communication topology and migration on distributed genetic programming. In: Proceedings of the annual conferences on genetic programming, pp 51–63

  • Goldberg DE, Lingle R (1985) Alleles, loci and the TSP. In: Proceedings of first international conference on genetic algorithms, pp 154–159

  • Gonzalez LF, Lee DS, Srinivas K, Wong KC (2006) Single and multi-objective UAV aerofoil optimisation via hierarchical asynchronous parallel evolutionary algorithm. Aeronaut J 110(1112):659–672

    Google Scholar 

  • Herrera F, Lozano M, Moraga C (1999) Hierarchical distributed genetic algorithms. Int J Intell Syst 14(11):1099–1121

    Article  MATH  Google Scholar 

  • Karakasis MK, Koubogiannis DG, Giannakoglou KC (2007) Hierarchical distributed metamodel-assisted evolutionary algorithms in shape optimization. Int J Numer Methods Fluids 53(3):455–469

    Google Scholar 

  • Lässig J, Sudholt D (2013) Design and analysis of migration in parallel evolutionary algorithms. Soft Comput 17(7):1121–1144

    Article  Google Scholar 

  • Lee DS, Periaux J, Onate E, Gonzalez LF, Qin N (2011) Active transonic aerofoil design optimization using robust multiobjective evolutionary algorithms. J Aircr 48(3):1084–1094

    Google Scholar 

  • Liakopoulos PIK, Kampolis IC, Giannakoglou KC (2008) Grid enabled, hierarchical distributed metamodel-assisted evolutionary algorithms for aerodynamic shape optimization. Future Gener 24(7):701–708

    Article  Google Scholar 

  • Lin S, Kernighan BW (1973) An effective heuristic algorithm for the traveling-salesman problem. Oper Res 21:498–516

    Google Scholar 

  • Power D, Ryan C, Azad RMA (2005) Promoting diversity using migration strategies in distributed genetic algorithms. In: Proceedings of IEEE congress on evolutionary computation, vol 2, pp 1831–1838

  • Reinelt G (1996) TSPLIB. University of Heidelberg, Heidelberg

    Google Scholar 

  • Skolicki Z, De Jong K (2005) The influence of migration sizes and intervals on island models. In: Proceedings of genetic and evolutionary computation conference, pp 1295–1302

  • Tao G, Michalewicz Z (1998) Evolutionary algorithms for the TSP. In: Proceedings of the 5th parallel problem solving from nature conference, pp 803–812

  • Yuan XL, Bai Y (2010) Identifying stochastic nonlinear dynamic systems using multi-objective hierarchical fair competition parallel genetic programming. J Mult Valued Log Soft Comput 16(6):643–660

    MATH  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Dr. Dunhui Xiao for his valuable suggestions.

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Authors and Affiliations

  1. School of Computer Science, China University of Geosciences, Wuhan , 430074, China

    Chengjun Li

  2. Faculty of Earth Resource, China University of Geosciences, Wuhan , 430074, China

    Guangdao Hu

Authors
  1. Chengjun Li
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  2. Guangdao Hu
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Corresponding author

Correspondence to Chengjun Li.

Additional information

Communicated by E. Munoz.

The project was supported by the Fundamental Research Founds for National University, China University of Geosciences (Wuhan) under Grant 2012199164.

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Li, C., Hu, G. Global migration strategy with moving colony for hierarchical distributed evolutionary algorithms. Soft Comput 18, 2161–2176 (2014). https://doi.org/10.1007/s00500-013-1191-7

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  • DOI: https://doi.org/10.1007/s00500-013-1191-7

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