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Browsing by Subject "discrete optimization"

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  • thumbnail.journal.alt
    Item type:Article, Access status: Open Access ,
    Algorytm selekcji klonalnej w marszrutowaniu pojazdów
    (Wydawnictwa AGH, 2009) Dąbrowski, Jacek
    Clonal Selection (CS) algorithms are discrete optimization algorithms that belong to the class of Artificial Immune Systems. In this work we present an application of CS principles to solving the NP-hard Capacitated Vehicle Routing Problem. We present details of the algorithm and some results of computer experiments aimed at assesing the parameters of the algorithm, as well as comparing it with a Simulated Annealing algorithm for CVRP.
  • thumbnail.journal.alt
    Item type:Article, Access status: Open Access ,
    Algorytmy heurystyczne w trójwymiarowym zagadnieniu pakowania
    (Wydawnictwo AGH, 2010) Chmiel, Wojciech; Kadłuczka, Piotr; Wala, Konrad; Jędrusik, Stanisław
    In this paper we examine the problem of optimal packing of a three-dimensional container with rectangular boxes such that the volume of the packed boxes is maximized. We investigate fast constructive procedures and an approximation algorithm based on simulated annealing. In all developed algorithms solutions are represented in a form of four sequences. Extensive computational results involving various test instances up to 400 boxes, are presented.
  • thumbnail.journal.alt
    Item type:Article, Access status: Open Access ,
    Emergence of population structure in socio-cognitively inspired ant colony optimization
    (Wydawnictwa AGH, 2018) Byrski, Aleksander; Świderska, Ewelina; Łasisz, Jakub; Kisiel-Dorohinicki, Marek; Lenaerts, Tom; Samson, Dana; Indurkhya, Bipin
    A metaheuristic proposed by us recently, Ant Colony Optimization (ACO) hybridized with socio-cognitive inspirations, turned out to generate interesting results compared to classic ACO. Even though it does not always find better solutions to the considered problems, it usually finds sub-optimal solutions usually. Moreover, instead of a trial-and-error approach to configure the parameters of the ant species in the population, in our approach, the actual structure of the population emerges from predefined species-to-species ant migration strategies. Experimental results of our approach are compared against classic ACO and selected socio-cognitive versions of this algorithm.