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Differential evolution and differential ant-stigmergy on dynamic optimisation problems

Author

Listed:
  • Janez Brest
  • Peter Korošec
  • Jurij Šilc
  • Aleš Zamuda
  • Borko Bošković
  • Mirjam Maučec

Abstract

Many real-world optimisation problems are of dynamic nature, requiring an optimisation algorithm which is able to continuously track a changing optimum over time. To achieve this, we propose two population-based algorithms for solving dynamic optimisation problems (DOPs) with continuous variables: the self-adaptive differential evolution algorithm (jDE) and the differential ant-stigmergy algorithm (DASA). The performances of the jDE and the DASA are evaluated on the set of well-known benchmark problems provided for the special session on Evolutionary Computation in Dynamic and Uncertain Environments. We analyse the results for five algorithms presented by using the non-parametric statistical test procedure. The two proposed algorithms show a consistently superior performance over other recently proposed methods. The results show that both algorithms are appropriate candidates for DOPs.

Suggested Citation

  • Janez Brest & Peter Korošec & Jurij Šilc & Aleš Zamuda & Borko Bošković & Mirjam Maučec, 2013. "Differential evolution and differential ant-stigmergy on dynamic optimisation problems," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(4), pages 663-679.
    • Handle: RePEc:taf:tsysxx:v:44:y:2013:i:4:p:663-679
    DOI: 10.1080/00207721.2011.617899
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