- Tytuł:
-
Zastosowanie algorytmów rojowych do optymalizacji parametrów w modelach układów regulacji
Application of swarm intelligence algorithms to optimization of control system models - Autorzy:
- Tomera, M.
- Powiązania:
- https://bibliotekanauki.pl/articles/269153.pdf
- Data publikacji:
- 2015
- Wydawca:
- Politechnika Gdańska. Wydział Elektrotechniki i Automatyki
- Tematy:
-
algorytmy rojowe
optymalizacja parametrów
algorytm mrówkowy
algorytm sztucznej kolonii pszczół
algorytm optymalizacji rojem cząstek
swarm intelligence
swarm based optimization
ant colony optimization
Artificial Bee Colony
particle swarm optimization (PSO) - Opis:
-
W pracy przedstawione zostały algorytmy rojowe, takie jak: algorytm mrówkowy, zmodyfikowany algorytm mrówkowy, algorytm sztucznej kolonii pszczół oraz algorytm optymalizacji rojem cząstek. Dla tych algorytmów przygotowane zostało oprogramowanie w Matlabie, pozwalające na optymalizację parametrów poszukiwanych modeli matematycznych, wyznaczanych na podstawie przeprowadzonych testów identyfikacyjnych lub na optymalizację parametrów regulatorów zastosowanych w modelach matematycznych układów sterowania.
The paper presents the swarm intelligence algorithms, such as: ant colony algorithm (ACO), the modified ant colony algorithm (MACO), the artificial bee colony algorithm (ABC) and the particle swarm optimization algorithm (PSO). Ant colony optimization (ACO) based upon the observation of the behavior of ant colonies looking for food in the surrounding anthill. Feeding ants it is based on finding the shortest path transitions between a food source and the anthill. In the process of foraging ants on their paths crossing from the nest to a food source and back, they leave a pheromone trail. The work presents also the modified ant colony algorithm (MACO). This algorithm is based on searching the solution space surrounded by the best solution obtained in the previous iteration. If you find a local minimum, the proposed algorithm uses pheromone to find a new solution space, while retaining the position information current local minimum. The artificial bee colony algorithm is one of the well-known swarm intelligence algorithms. In the past decade there has been created several different algorithms based on the observation of the behavior of cooperative bees. Among them, the most frequently analyzed and used is bee algorithm proposed in 2005 by Dervis Karaboga and was be used in the proposed paper. The particle swarm optimization algorithm (PSO) is based on adjusting the change speed of the moving particles to a speed of particles movement in the neighborhood. Particle optimization algorithm is one of the computational techniques derived on the basis of swarm behavior such as flocks of birds and schools of fish, which is the basis for the functioning of the exchange of information to enable them to cooperate. It was noticed that the animals in the herd tend to maintain the optimum distance from their neighbors, by appropriate adjustment of their speed. This method allows the synchronous and collision-free motion, often accompanied by sudden changes of direction and due to the rearrangement of the optimal formation. For these algorithms has been prepared the software in Matlab, allowing to optimization of the mathematical models designated on the basis of the carried out identification tests and control parameters used in the mathematical model of the control system. - Źródło:
-
Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej; 2015, 46; 97-102
1425-5766
2353-1290 - Pojawia się w:
- Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej
- Dostawca treści:
- Biblioteka Nauki
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