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Wyszukujesz frazę "minimum search" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Global minimum search using DMC algorithm with continuous weights
Autorzy:
Kazimirski, J. K.
Powiązania:
https://bibliotekanauki.pl/articles/121973.pdf
Data publikacji:
2011
Wydawca:
Uniwersytet Humanistyczno-Przyrodniczy im. Jana Długosza w Częstochowie. Wydawnictwo Uczelniane
Tematy:
DMC algorithm
diffusion Monte Carlo
continuous objective function
minimum search
algorytm DMC
metoda Monte Carlo
funkcja ciągłego celu
wyszukiwanie minimalne
Opis:
In this study we presented an algorithm for an unconstrained optimization of a continuous objective function, inspired by the Diffusion Monte Carlo method using a weight-based implementation. In this algorithm a cloud of replicas explores the solution space. Replicas are moved and evaluated after each step. Each replica carries an additional parameter (weight) which reflects the quality of its local solution. This parameter is updated after each step. Most inefficient replicas, i.e. replicas with the lowest weights, are occasionally replaced with their highest weight counterparts. In our study we present the basic implementation of the algorithm and compare its performance with other approaches, including the previously used implementation of DMC algorithm with a fluctuating population.
Źródło:
Scientific Issues of Jan Długosz University in Częstochowa. Mathematics; 2011, 16; 117-122
2450-9302
Pojawia się w:
Scientific Issues of Jan Długosz University in Częstochowa. Mathematics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Ship Collision Avoidance by Distributed Tabu Search
Autorzy:
Kim, D.
Hirayama, K.
Okimoto, M.
Powiązania:
https://bibliotekanauki.pl/articles/116919.pdf
Data publikacji:
2015
Wydawca:
Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:
Safety of Navigation
Collision Avoidance
Distributed Tabu Search Algorithm (DTSA)
Distributed Local Search Algorithm (DLSA)
Quasi-Local Minimum (QLM)
Tabu Search Algorithm
Local Search Algorithm
Distributed Tabu Search
Opis:
More than 90% of world trade is transported by sea. The size and speed of ships is rapidly increasing in order to boost economic efficiency. If ships collide, the damage and cost can be astronomical. It is very difficult for officers to ascertain routes that will avoid collisions, especially when multiple ships travel the same waters. There are several ways to prevent ship collisions, such as lookouts, radar, and VHF radio. More advanced methodologies, such as ship domain, fuzzy theory, and genetic algorithm, have been proposed. These methods work well in one-on-one situations, but are more difficult to apply in multiple-ship situations. Therefore, we proposed the Distributed Local Search Algorithm (DLSA) to avoid ship collisions as a precedent study. DLSA is a distributed algorithm in which multiple ships communicate with each other within a certain area. DLSA computes collision risk based on the information received from neighboring ships. However, DLSA suffers from Quasi-Local Minimum (QLM), which prevents a ship from changing course even when a collision risk arises. In our study, we developed the Distributed Tabu Search Algorithm (DTSA). DTSA uses a tabu list to escape from QLM that also exploits a modified cost function and enlarged domain of next-intended courses to increase its efficiency. We conducted experiments to compare the performance of DLSA and DTSA. The results showed that DTSA outperformed DLSA.
Źródło:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2015, 9, 1; 23-29
2083-6473
2083-6481
Pojawia się w:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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