An improved Central Force Optimization (CFO) algorithm for antenna optimization is presented. CFO locates the global extrema an objective function to be maximized, in this case antenna directivity, by flying "probes" through the decision space (DS). The new implementation includes variable initial probe distribution and decision space adaptation. CFO's performance is assessed against a recognized antenna benchmark problem specifically designed to evaluate optimization evolutionary algorithms for antenna applications. In addition, summary results also are presented for a standard twenty-three function suite of analytic benchmarks. The improved CFO implementation exhibits excellent performance.
2. Formato, R. A., "Central force optimization: A new computtional framework for multidimensional search and optimization," Nature Inspired Cooperative Strategies for Optimization (NICSO 2007), Studies in Computational Intelligence 129, N. Krasnogor, G. Nicosia, M. Pavone, and D. Pelta (eds.), Vol. 129, Springer-Verlag, Heidelberg, 2008.
3. Formato, R. A., "Central force optimisation: A new gradient-like metaheuristic for multidimensional search and optimisation," Int. J. Bio-inspired Computation, Vol. 1, No. 4, 217-238, 2009.
doi:10.1504/IJBIC.2009.024721
4. Formato, R. A., "Central force optimization: A new deterministic gradient-like optimization metaheuristic," OPSEARCH, Jour. of the Operations Research Society of India, Vol. 46, No. 1, 25-51, 2009.
5. Mohammad, G. and N. Dib, "Synthesis of antenna arrays using central force optimization," Mosharaka International Conference on Communications, Computers and Applications, 6-8, Amman, Jordan, Feb. 2009.
6. Qubati, G. M., R. A. Formato, and N. I. Dib, "Antenna benchmark performance and array synthesis using central force optimization," IET (U.K.) Microwaves, Antennas & Propagation, 2000 (in press).
7. Pantoja, M. F., A. R. Bretones, and R. G. Martin, "Benchmark antenna problems for evolutionary optimization algorithms," IEEE Trans. Antennas and Propagation, Vol. 55, No. 4, 1111-1121, Apr. 2007.
doi:10.1109/TAP.2007.893396
8. Li, W. T., X. W. Shi, and Y. Q. Hei, "An improved particle swarm optimization algorithm for pattern synthesis of phased arrays," Progress In Electromagnetics Research, Vol. 82, 319-332, 2008.
doi:10.2528/PIER08030904
9. Ghaffari-Miab, M., A. Farmahini-Farahani, R. Faraji-Dana, and C. Lucas, "An efficient hybrid swarm intelligence-gradient optimization method for complex time Green's functions of multilayered media," Progress In Electromagnetics Research, Vol. 77, 181-192, 2007.
doi:10.2528/PIER07072504
10. Sijher, T. S. and A. A. Kishk, "Antenna modeling by infinitesimal dipoles using genetic algorithms," Progress In Electromagnetics Research, Vol. 52, 225-254, 2005.
doi:10.2528/PIERC08010205
11. Cengiz, Y. and H. Tokat, "Linear antenna array design with use of genetic, memetic and tabu search optimization algorithms," Progress In Electromagnetics Research C, Vol. 1, 63-72, 2008.
doi:10.2528/PIERM09012404
12. Zainud-Deen, H. H., W. M. Hassen, and K. H. Awadalla, "Crack detection using a hybrid finite difference frequency domain and particle swarm optimization techniques," Progress In Electromagnetics Research M, Vol. 6, 47-58, 2009.
doi:10.2528/PIERB07121005
13. Mangaraj, B. B., I. S. Misra, and A. K. Barisal, "Optimizing included angle of symmetrical V-dipoles for higher directivity using bacteria foraging optimization algorithm," Progress In Electromagnetics Research B, Vol. 3, 295-314, 2008.
doi:10.2528/PIER02062602
14. Yau, D. and S. Crozier, "A genetic algorithm/method of moments approach to the optimization of an RF coil for MRI applications --- Theoretical considerations," Progress In Electromagnetics Research, Vol. 39, 177-192, 2003.
15. Burke, G. J., "Numerical electromagnetics code --- NEC-4, method of moments, Part I: User's manual and Part II: Program description --- Theory,", UCRL-MA-109338, Lawrence Livermore National Laboratory, Livermore, California, USA, Jan. 1992. https://ipo.llnl.gov/technology/software/softwaretitles/nec.php.
16. Schweickart, R., C. Chapman, D. Durda, P. Hut, B. Bottke, and D. Nesvorny, "Threat characterization: Trajectory dynamics (white paper 039),", 2006. http://arxiv.org/abs/physics/0608155.
doi:10.1051/0004-6361:20031039
17. Valsecchi, G. B., A. Milani, G. F. Gronchi, and S. R. Chesley, "Resonant returns to close approaches: Analytical theory," Astronomy & Astrophysics, Vol. 408, No. 3, 1179-1196, 2003.
18. Formato, R. A., "Are near earth objects the key to optimization theory?,", arXiv:0912.1394v1 [astro-ph.EP].
doi:10.1109/TEVC.2009.2011992
19. He, S., Q. H. Wu, and J. R. Saunders, "Group search optimizer: An optimization algorithm inspired by animal searching behavior," IEEE Trans. Evol. Computation, Vol. 13, No. 5, 973-990, Oct. 2009.
20. Formato, R. A., "Pseudorandomness in central force optimization,", arXiv:1001.0317v1[cs.NE], www.arXiv.org, 2010.
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