The Learnable Ant Colony Optimization to Satellite Ground Station System Scheduling Problems

• Autorzy: Yang K.-W. , Xing L.

Warianty tytułu

PL

Uczący się algorytm mrówkowy do rozwiązywania problemu planowania działań naziemnej stacji satelitarnej

• Języki publikacji: EN

Abstrakty

EN

The Learnable Ant Colony Optimization (LACO) is proposed to satellite ground station system scheduling problems. The LACO employs an integrated modelling idea which combines the ant colony model with the knowledge model. In order to improve the performance, LACO largely pursues the complementary advantages of ant colony model and knowledge model. Experimental results suggest that LACO is a feasible and effective approach for the satellite ground station system scheduling problem.

PL

Zaproponowanie wykorzystanie algorytmu LACO (Learnable Ant Colony Optimization) do rozwiązywania problemu planowania działań naziemnej stacji satelitarnej.

Słowa kluczowe

EN

mission planning; ant colony optimization; knowledge

PL

algorytm mrówkowy; stacja satelitarna naziemna; algorytm LACO

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2012
• Tom: R. 88, nr 9b
• Strony: 62--65
• Opis fizyczny: Bibliogr. 9 poz., rys., tab.

Twórcy

autor

Yang K.-W.

• School of Information System and Management, National University of Defence Technology, Changsha, 410073 P. R. China

autor

Xing L.

• School of Information System and Management, National University of Defence Technology, Changsha, 410073 P. R. China

Bibliografia

• [1] Liu Y, Chen YW, Tan YJ. “Mission planning method of the satellite ground station based on the greedy algorithm,” Systems Engineering and Electronics, 2003, 25(10): 1239-1241
• [2] Liu Y, Chen YW, Tan YJ. “The method of mission planning of the ground station of satellite based on dynamic programming,” Chinese Space Science and Technology, 2005, 25(1): 47-50.
• [3] Liu Y, Chen YW, Tan YJ. “Modeling and solution of a multiresources dynamic scheduling problem with time windows,” Systems Engineering, 2004, 22(6): 8-11.
• [4] He RJ, Tan YJ. “Apply constraint satisfaction to optimal allocation of satellite ground station resource,” Computer Engineering and Applications, 2004, 40(18): 229-232
• [5] Dorigo M, Stutzle T. “Ant Colony Optimization”. Cambridge, USA: MIT Press, 2004
• [6] Voudouris C, Tsang E. “Guided local search and its application to the traveling salesman problem,” European Journal of Operational Research, 1999, 113(2): 469-499
• [7] Tsang E, Voudouris C. “Fast local search and guided local search and their application to British Telecom’s workforce scheduling problem.,” Operations Research Letters, 1997, 20(3): 119-127
• [8] Li JF, Tan YJ. “The guided local search method for a class of work-force scheduling problems,” Systems Engineering, 2004, 22(9): 87-91.
• [9] Blum C. “Beam-ACO-hybridizing ant colony optimization with beam search: an application to open shop scheduling,” Computers and Operations Research, 2005, 32(6):1565-1591