Improvement of the ACO meta-heuristic by using the method Artificial Bees Colony (ABC)

• Autorzy: Younes, M. , Maamar, M.

Warianty tytułu

PL

Wykorzystanie algorytmu mrówkowego do optymalizacji i analizy ekonomiczne przesyłu energii

• Języki publikacji: EN

Abstrakty

EN

This paper sets up an Artificial Bee Colony (ABC) algorithm for evolving ant direction Ant Colony Optimization (ACO) to solve the Economic Power Dispatch (EPD) problem. The method ABC-ACO employs the bee colony to find a suitable and the best values operators to improve the ACO. The feasibility of the proposed approach was tested on IEEE 57-bus system. The proposed approach simulation results, which show the effectiveness and robustness of ABC-ACO, have been compared to those that reported in the literature.

PL

W artykule opisano algorytm mrówkowy Artificial Colony Bees (ABC) zastosowany do analizy ekonomicznej rozsyłu energii. Metodę testowano na IEEE 57-magistrali systemowej. Otrzymane wyniki symulacji wskazują na skuteczność i stabilność systemu ABC-ACO.

Słowa kluczowe

PL

algorytm mrówkowy; analiza ekonomiczna; sieci przesyłowe

EN

Economic Power Dispatch; Artificial Bee Colony Algorithm; ant colony optimization

• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2012
• Tom: R. 88, nr 5b
• Strony: 174-178
• Opis fizyczny: Bibliogr. 12 poz., tab., schem.

Twórcy

autor

Younes, M.

autor

Maamar, M.

• ICEPS Laboratory, Djillali Liabes University,Sidi Bel Abbes, 22000, Algeria

Bibliografia

• [1] Zimmerman R.D., Murillo-Sanchez C.E, and Thomas, R. J., Matpower's extensible optimal power architecture, Power and Energy Society General Meeting, 2009 IEEE, ( 2009), pp. 1-7.
• [2] Younes M., Rahli M. Koridak L.A., Optimal Power Flow Based on Hybrid Genetic Algorithm, Journal of Information Science and Engineering,( 2007), Vol. 23, pp. 1801-1816.
• [3] Younes M., Rahli M., Koridak H., Genetic/ Evolutionary Algorithms and application to power systems’’, PCSE’05, O. E. Bouaghi, May,(2005), pp. 9-11.
• [4] Dorigo M., Di Caro G., The ant colony optimization metaheuristic, in Corne D., Dorigo M., Glover F., New Ideas in Optimization, McGraw-Hill,( 1997), p. 11-32.
• [5] Den Besteb,M.-Stützle., T.-Dorigo M., Ant colony optimization for the total weighted tardiness problem, Proc. 6th Int. Conf. Parallel Problem Solving from Nature, Berlin, (2000), p.611-620.
• [6] Merkle,D.-Middendorf,M., An ant algorithm with a new pheromone evaluation rule for total tardiness problems, Proceedings of the EvoWorkshops (2000), Berlin, Germany: Springer-Verlag, Vol. 1803 then Lecture Notes in Computer Science, (2000), pp. 287-296.
• [7] Colorni,A., Dorigo,M.-Maniezzo., V.- Trubian M.: Ant system for job-shop scheduling,Belgian J. Oper. Res., Statist. Comp. Sci. (JORBEL), Vol. 34, No. 1, (1994), pp. 39-53.
• [8] Allaoua,B., LAOUFI. A., Collective Intelligence for Optimal Power Flow Solution Using Ant Colony Optimization , Leonardo Electronic Journal of Practices and Technologies ISSN 1583-1078, Issue 13, (2008), pp. 88-105.
• [9] D. Karaboga and B. Basturk., A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm,Journal of Global Optimization,( 2007), Vol. 37, pp. 459-471.
• [10] S.N. Omkar J., Senthilnath R., Khandelwal, G.N.Naik and S. Gopalakrishnan., Artificial Bee Colony (ABC) for Multi-Objective Design Optimization of Composite Structures”, Applied Soft Computing, Article in Press (2010).
• [11] Naama,B., Bouzeboudja,H., RAMDANI.Y., Chaker,A., Hybrid approach to the economic dispatch problem using a genetic”, Acta Electrotechnica et Informatica Vol. 8, No. 3, (2008), pp.31–35.
• [12] Younes M., hadjeri S., Zidi,S., Houari,S., Laarioua,M., Economic Power Dispatch using an Ant Colony Optimization Method, 10th International conference on Sciences and Techniques of Automatic control & computer engineering, Hammamet, Tunisia, (2009), 785-794