Properties of the Island-Based and Single Population Differential Evolution Algorithms Applied to Discrete-Continuous Scheduling

  • Piotr Jędrzejowicz
  • Aleksander SkakovskiEmail author
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 56)


In this paper we have studied by experiment the properties of two models of the DE search: a model based on a single population, and a model based on multiple populations, known as the island model (IBDEA). We consider two versions of the island model: with migration of individuals between islands and without migration. We investigated how the effectiveness of models depends on such parameters as the size of a single population, and in the case of the island model, also the number of islands and migration rate between them. The general conclusion is that both models can be equally effective when used with proper parameter settings, which have been determined by the experiment. In addition, conditions for higher effectiveness of the IBDEA were discussed. The discrete-continuous scheduling with continuous resource discretisation was used as the test problem.


Island model Differential evolution Population size Number of islands Migration rate Effectiveness 


  1. 1.
    Alba, E., Troya, J.: Analysis of synchronous and asynchronous parallel distributed genetic algorithms with structured and panmictic Islands. In: Rolim. J. et al., (eds.) In: Proceedings of the 10th Symposium on Parallel and Distributed Processing, pp. 248–256. San Juan, Puerto Rico, USA, 12–16 April 1999Google Scholar
  2. 2.
    Bartusch, M., Rolf, H.M., Radermacher, F.J.: Scheduling project networks with resource constraints and time windows. Ann. Oper. Res. 16, 201–240 (1988)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Belding, T.C.: The distributed genetic algorithm revisited. In: Eshelman, L.J. (ed.) Proceedings of the Sixth International Conference on Genetic Algorithms, pp. 114–121. Morgan Kaufmann, San Francisco, CA (1995)Google Scholar
  4. 4.
    Cantu-Paz, E.: Migration policies, selection pressure, and parallel evolutionary algorithms. J. Heuristics 7(4), 31–334 (2001)CrossRefzbMATHGoogle Scholar
  5. 5.
    Cantu-Paz, E., Goldberg, D.E.: Are multiple runs of genetic algorithms better than one? In: Proceedings of the Genetic and Evolutionary Computation Conference (2003)Google Scholar
  6. 6.
    Damak, N., Jarboui, B., Siarry, P., Loukil, T.: Differential evolution for solving multi-mode resource-constrained project scheduling problems. Comput. Oper. Res. 36(9), 2653–2659 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Jędrzejowicz, P., Skakovski, A.: Structure vs. efficiency of the cross-entropy based population learning algorithm for discrete-continuous scheduling with continuous resource discretisation. In: Czarnowski, I., Jędrzejowicz, P., Kacprzyk, J. (eds.) Studies in Computational Intelligence. Agent-Based Optimization, vol. 456, pp. 77–102. Springer (2013)Google Scholar
  8. 8.
    Jędrzejowicz, P., Skakovski, A.: Population learning with differential evolution for the discrete-continuous scheduling with continuous resource discretisation. In: IEEE International Conference on Cybernetics (CYBCONF), pp. 92–97. Lausanne Switzerland, 13–15 June 2013Google Scholar
  9. 9.
    Jędrzejowicz, P., Skakovski, A.: Island-based differential evolution algorithm for the discrete-continuous scheduling with continuous resource discretisation. Procedia Comput. Sci. 35, 111–117 (2014)CrossRefGoogle Scholar
  10. 10.
    Józefowska, J., Węglarz, J.: On a methodology for discrete-continuous scheduling. Europ J Oper Res. 107–2, 338–353 (1998)CrossRefzbMATHGoogle Scholar
  11. 12.
    Józefowska, J., Różycki, R., Waligóra, G., Węglarz, J.: Local search metaheuristics for some discrete-continuous scheduling problems. Europ J Oper Res 107–2, 354–370 (1998)CrossRefzbMATHGoogle Scholar
  12. 11.
    Józefowska, J., Mika, M., Różycki, R., Waligóra, G., Węglarz, J.: Solving discrete-continuous scheduling problems by Tabu Search. In: 4th Metaheuristics International Conference MIC’2001, Porto, Portugal, pp. 667–671, 16–20 July 2001Google Scholar
  13. 13.
    Krink, T., Mayoh, B.H., Michalewicz, Z.: A PACHWORK model for evolutionary algorithms with structured and variable size populations. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, vol. 2, pp. 1321–1328. Orlando, Florida, USA, Morgan Kaufmann, (1999)Google Scholar
  14. 14.
    Muhlenbein, H.: Evolution in time and space: the parallel genetic algorithm. In: Rawlins, G. (ed.) FOGA-1. pp. 316–337. Morgan Kaufman (1991)Google Scholar
  15. 15.
    Pandey, H.M., Chaudharyb, A., Mehrotra, D.: A comparative review of approaches to prevent premature convergence in GA. Appl. Soft Comput. 24, 1047–1077 (2014)CrossRefGoogle Scholar
  16. 16.
    Różycki, R.: Zastosowanie algorytmu genetycznego do rozwiązywania dyskretno-ciągłych problemów szeregowania. PhD diss, Poznań University of Technology, Poland (2000)Google Scholar
  17. 17.
    Sekaj, I.: Robust parallel genetic algorithms with re-initialisation. In: Proceedings of Parallel Problem Solving from Nature—PPSN VIII. 8th International Conference, LNCS, vol. 3242, pp. 411–419. Springer, Birmingham, UK, 18–22 Sept 2004Google Scholar
  18. 18.
    Skolicki, Z.: An analysis of Island models in evolutionary computation. In: Proceedings of GECCO’05, pp. 386–389. Washington, DC, USA, 25–29 June 2005Google Scholar
  19. 19.
    Skolicki, Z., Kenneth, D.J.: The influence of migration sizes and intervals on Island models. In: Proceedings of GECCO’05, pp. 1295–1302. Washington, DC, USA, 25–29 June 2005Google Scholar
  20. 20.
    Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global Opt. 11, 341–359 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 22.
    Whitley, D., Starkweather, T.: GENITOR II: a distributed genetic algorithm. J. Exper. Theor. Artif. Intel. 2, 33–47 (1990)CrossRefGoogle Scholar
  22. 21.
    Whitley, D., Rana, S., Heckendorn, R.B.: The island model genetic algorithm: on separability, population size and convergence. J. Comp. and Infor. Tech. 7–1, 33–47 (1999)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Chair of Information SystemsGdynia Maritime UniversityGdyniaPoland
  2. 2.Department of NavigationGdynia Maritime UniversityGdyniaPoland

Personalised recommendations