Particle Swarm Optimization: the Gradient Correction

• Autorzy: Borowska B. , Nadolski S.
• Języki publikacji: EN

Abstrakty

EN

In the following paper, the solution of the optimization problem that relied on the determination of the optimal geometry of two cylindrical coil arrangement evoking magnetic field of specific parameters was presented. The objective of the task was to generate, in a defined active area, the magnetic field of the largest possible gradient and simultaneously keep this gradient relatively constant. The computations were performed using the classical particle swarm optimization as well as the modified method with the correction sensitive to the fitness function gradient introduced to the formula describing the movement of the specific swarm particles. As a result, a considerable enhancement of the optimization process was achieved.

Słowa kluczowe

EN

electromagnetism; optimization; evolutionary algorithms; particle swarm optimization (PSO)

PL

elektromagnetyzm; optymalizacja; algorytmy ewolucyjne

• Wydawca: Wydawnictwo Politechniki Łódzkiej
• Czasopismo: Journal of Applied Computer Science
• Rocznik: 2009
• Tom: Vol. 17, nr 2
• Strony: 7--15
• Opis fizyczny: Bibliogr. 11 poz.

Twórcy

autor

Borowska B.

autor

Nadolski S.

• Institute of Information Technology, Technical University of Łódź Wólczańska 215, 90-924 Łódź, Poland,

Bibliografia

• [1] Vasconcelos, J. A., Ramirez, J. A., Takahashi R. H. C. and Saldanha R. R.,Improvements in Genetic Algorithms, IEEE Trans. Magn., Vol. 37, pp. 3414-3417, 2001.
• [2] Robinson, J., and Rahmat-Samii, Y., Particle Swarm Optimization in Electromagnetics, IEEE Trans. Antennas and Propagation, Vol. 52, pp. 397-407, 2004.
• [3] Sato, S., Sakaguchi, S., Futamata, K. and Katou, K., Coil Optimization for Homogeneous Magnetic Field with Small Leakage Field,, IEEE Trans. Magn., Vol. 36, pp. 649-653, 2000.
• [4] Forbes, L. K., Crozier, S., Doddrell, M., Rapid Computation of static Fields Produced by Thick circular solenoids, IEEE Transaction on Magnetics, Vol. 33, pp. 4405-4410, 1997.
• [5] Kennedy, J., Eberhart, R., Particle swarm optimization, IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942-1948, 1995.
• [6] Chowdhury, P. R., Singh Y. P., Chansarkar R. A, Hybridization of Gradient Descent Algorithms with Dynamic Tunneling Methods for Global Optimization, IEEE Transactions on Systems, Man and Cybernetics, Vol. 30, No. 3, pp. 384-390, 2000.
• [7] Noel, M., Jannett, T. C., Simulation of a New Hybrid Particle Swarm Optimization Algorithm, Proceedings of the Thirty-Sixth Southeastern Symposium on System Theory, pp. 150-153, 2004.
• [8] Findeisen,W., Szymanowski, J.,Wierzbicki, A., Teoria i metody obliczeniowe optymalizacji, Wydawnictwo Naukowe PWN, Warszawa, 1980.
• [9] Ostanin, A., Metody i algorytmy optymalizacji, Wydawnictwo Politechniki Białostockiej, Białystok, 2003.
• [10] Borowska, B., Nadolski, S., Application of the PSO Algorithm with Subdomain Approach for the Optimization of a Radio Telescope Array, Journal of Applied Computer Science, Vol. 16, No. 1, pp. 7-14, Łódź, 2008.
• [11] Borowska, B., Nadolski, S., Genetic Algorithms in Electromagnetism: The Helmholtz Coils, Journal of Applied Computer Science, Vol. 14, No. 2, pp. 109-118, Łódź, 2006.