Multi-population adapative inflationary differential evolution

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In this paper, a multi-population version of Adaptive Inflationary Differential Evolution, which automatically adapts the crossover probability and the differential weight of the Differential Evolution, is presented. The multi-population algorithm exploits the use of different populations, and the local minima found by each population, to assess the distance between minima; a probabilistic kernel based approach is then used to automatically adapt the dimension of a bubble in which the population is re-initialized after converging to a local minimum. The algorithm is tested on two real case functions and on two difficult academic functions.
Original languageEnglish
Publication statusPublished - 13 Sep 2014
EventBio-inspired Optimization Methods and their Applications, BIOMA 14 - Ljubljana, Slovenia
Duration: 13 Sep 201413 Sep 2014


ConferenceBio-inspired Optimization Methods and their Applications, BIOMA 14


  • adaptive algorithms
  • differential evolution
  • global optimization

Research Output

  • 1 Article
  • 1 Conference Contribution

Adaptive multi-population inflationary differential evolution

Di Carlo, M., Vasile, M. & Minisci, E., 15 Jul 2019, In : Soft Computing. 31 p.

Research output: Contribution to journalArticle

Open Access
  • 1 Citation (Scopus)
    10 Downloads (Pure)

    Aerodynamic design optimization of wind turbine airfoils under aleatory and epistemic uncertainty

    Caboni, M., Minisci, E. & Riccardi, A., 1 Jun 2018, In : Journal of Physics: Conference Series . 1037, 4, 10 p., 042011.

    Research output: Contribution to journalConference Contribution

    Open Access
  • 3 Citations (Scopus)
    23 Downloads (Pure)

    Cite this

    Di Carlo, M., Vasile, M., & Minisci, E. (2014). Multi-population adapative inflationary differential evolution. 41-54. Paper presented at Bio-inspired Optimization Methods and their Applications, BIOMA 14, Ljubljana, Slovenia.