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    Wyświetlanie 1-3 z 3
    Tytuł:
    A new auto adaptive fuzzy hybrid particle swarm optimization and genetic algorithm
    Autorzy:
    Dziwiński, Piotr
    Bartczuk, Łukasz
    Paszkowski, Józef
    Powiązania:
    https://bibliotekanauki.pl/articles/1837533.pdf
    Data publikacji:
    2020
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    hybrid methods
    Particle Swarm Optimization
    Genetic Algorithm
    fuzzy systems
    multimodal function
    Opis:
    The social learning mechanism used in the Particle Swarm Optimization algorithm allows this method to converge quickly. However, it can lead to catching the swarm in the local optimum. The solution to this issue may be the use of genetic operators whose random nature allows them to leave this point. The degree of use of these operators can be controlled using a neuro-fuzzy system. Previous studies have shown that the form of fuzzy rules should be adapted to the fitness landscape of the problem. This may suggest that in the case of complex optimization problems, the use of different systems at different stages of the algorithm will allow to achieve better results. In this paper, we introduce an auto adaptation mechanism that allows to change the form of fuzzy rules when solving the optimization problem. The proposed mechanism has been tested on benchmark functions widely adapted in the literature. The results verify the effectiveness and efficiency of this solution.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 2; 95-111
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Modelling Microcystis Cell Density in a Mediterranean Shallow Lake of Northeast Algeria (Oubeira Lake), Using Evolutionary and Classic Programming
    Autorzy:
    Arif, Salah
    Djellal, Adel
    Djebbari, Nawel
    Belhaoues, Saber
    Touati, Hassen
    Guellati, Fatma Zohra
    Bensouilah, Mourad
    Powiązania:
    https://bibliotekanauki.pl/articles/2174666.pdf
    Data publikacji:
    2023
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    microcystis cell density
    Multiple Linear Regression
    Support Vector Machine
    Particle Swarm Optimization
    Genetic Algorithm
    Bird Swarm Algorithm
    Opis:
    Caused by excess levels of nutrients and increased temperatures, freshwater cyanobacterial blooms have become a serious global issue. However, with the development of artificial intelligence and extreme learning machine methods, the forecasting of cyanobacteria blooms has become more feasible. We explored the use of multiple techniques, including both statistical [Multiple Regression Model (MLR) and Support Vector Machine (SVM)] and evolutionary [Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Bird Swarm Algorithm (BSA)], to approximate models for the prediction of Microcystis density. The data set was collected from Oubeira Lake, a natural shallow Mediterranean lake in the northeast of Algeria. From the correlation analysis of ten water variables monitored, six potential factors including temperature, ammonium, nitrate, and ortho-phosphate were selected. The performance indices showed; MLR and PSO provided the best results. PSO gave the best fitness but all techniques performed well. BSA had better fitness but was very slow across generations. PSO was faster than the other techniques and at generation 20 it passed BSA. GA passed BSA a little further, at generation 50. The major contributions of our work not only focus on the modelling process itself, but also take into consideration the main factors affecting Microcystis blooms, by incorporating them in all applied models.
    Źródło:
    Geomatics and Environmental Engineering; 2023, 17, 2; 31--68
    1898-1135
    Pojawia się w:
    Geomatics and Environmental Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Dynamics of Stochastic vs. Greedy Heuristics in Traveling Salesman Problem
    Autorzy:
    Białogłowski, M.
    Staniaszek, M.
    Laskowski, W.
    Grudniak, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/91276.pdf
    Data publikacji:
    2018
    Wydawca:
    Warszawska Wyższa Szkoła Informatyki
    Tematy:
    traveling salesman problem
    Nearest Neighbor
    Monte Carlo
    Simulated Annealing
    Genetic Algorithm
    particle swarm optimization (PSO)
    Opis:
    We studied the relative performance of stochastic heuristics in order to establish the relations between the fundamental elements of their mechanisms. The insights on their dynamics, abstracted from the implementation details, may contribute to the development of an efficient framework for design of new probabilistic methods. For that, we applied four general optimization heuristics with varying number of hyperparameters to traveling salesman problem. A problem-specific greedy approach (Nearest Neighbor) served as a reference for the results of: Monte Carlo, Simulated Annealing, Genetic Algorithm, and Particle Swarm Optimization. The more robust heuristics – with higher configuration potential, i.e. with more hyperparameters – outperformed the smart ones, being surpassed only by the method specifically designed for the task.
    Źródło:
    Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki; 2018, 12, 19; 7-24
    1896-396X
    2082-8349
    Pojawia się w:
    Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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