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Browsing by Subject "NSGA-II"

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  • thumbnail.journal.alt
    Item type:Thesis, Access status: Restricted ,
    Agentowo-ewolucyjny system optymalizacji wielokryterialnej
    (Data obrony: 2020-07-16) Kasprzyk, Michał
    Wydział Informatyki, Elektroniki i Telekomunikacji
  • thumbnail.journal.alt
    Item type:Article, Access status: Open Access ,
    An application of the NSGA-II algorithm in Pareto joint inversion of 2D magnetic and gravity data
    (Wydawnictwa AGH, 2021) Miernik, Katarzyna; Węglińska, Elżbieta; Danek, Tomasz; Leśniak, Andrzej
    Joint inversion is a widely used geophysical method that allows model parameters to be obtained from the observed data. Pareto inversion results are a set of solutions that include the Pareto front, which consists of non-dominated solutions. All solutions from the Pareto front are considered the most feasible models from which a particular one can be chosen as the final solution. In this paper, it is shown that models represented by points on the Pareto front do not reflect the shape of the real model. In this contribution, a collective approach is proposed to interpret the geometry of models retrieved in inversion. Instead of choosing single solutions from the Pareto front, all obtained solutions were combined in one »heat map«, which is a plot representing the frequency of points belonging to all returned objects from the solution set. The conducted experiment showed that this approach limits the problem of equivalence and is a promising way of representing the geometry of the model that was retrieved in the inversion process.
  • thumbnail.journal.alt
    Item type:Article, Access status: Open Access ,
    Very fast non-dominated sorting
    (2014) Smutnicki, Czesław; Rudy, Jarosław; Żelazny, Dominik
    A new and very efficient parallel algorithm for the Fast Non-dominated Sorting of Pareto fronts is proposed. By decreasing its computational complexity, the application of the proposed method allows us to increase the speedup of the best up to now Fast and Elitist Multi-Objective Genetic Algorithm (NSGA-II) more than two orders of magnitude. Formal proofs of time complexities of basic as well as improved versions of the procedure are presented. The provided experimental results fully confirm theoretical findings.