A Quantum Behaved Gravitational Search Algorithm
Affiliation(s)
Electrical Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran.
Electrical Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran.
ABSTRACT
Gravitational search algorithm (GSA) is a recent introduced global convergence guaranteed algorithm. In this paper, a quantum-behaved gravitational search algorithm, namely called as QGSA, is proposed. In the proposed QGSA each individual mass moves in a Delta potential well in feasible search space with a center which is weighted average of all kbests. The QGSA is tested on several benchmark functions and compared with the GSA. It is shown that the quantum-behaved gravitational search algorithm has faster convergence speed with good precision, and thus generating a better performance.
Gravitational search algorithm (GSA) is a recent introduced global convergence guaranteed algorithm. In this paper, a quantum-behaved gravitational search algorithm, namely called as QGSA, is proposed. In the proposed QGSA each individual mass moves in a Delta potential well in feasible search space with a center which is weighted average of all kbests. The QGSA is tested on several benchmark functions and compared with the GSA. It is shown that the quantum-behaved gravitational search algorithm has faster convergence speed with good precision, and thus generating a better performance.
Cite this paper
M. Moghadam, H. Nezamabadi-Pour and M. Farsangi, "A Quantum Behaved Gravitational Search Algorithm," Intelligent Information Management, Vol. 4 No. 6, 2012, pp. 390-395. doi: 10.4236/iim.2012.46043.
M. Moghadam, H. Nezamabadi-Pour and M. Farsangi, "A Quantum Behaved Gravitational Search Algorithm," Intelligent Information Management, Vol. 4 No. 6, 2012, pp. 390-395. doi: 10.4236/iim.2012.46043.
References
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[1] E. Rashedi, H. Nezamabadi-Pour and S. Saryazdi, “GSA: A Gravitational Search Algorithm,” Information Science, Vol. 179, No. 13, 2009, pp. 2232-2248. doi:10.1016/j.ins.2009.03.004 [2] K. S. Tang, K. F. Man, S. Kwong and Q. He, “Genetic Algorithms and Their Applications,” IEEE Signal Processing Magazine, Vol. 13, No. 6, 1996, pp. 22-37 doi:10.1109/79.543973 [3] F. V. D. Bergh and A. P. Engelbrecht, “A Study of Particle Swarm Optimization Particle Trajectories,” Information Sciences, Vol. 176, No. 8, 2006, pp. 937-971. doi:10.1016/j.ins.2005.02.003 [4] X. F. Pang, “Quantum Mechanics in Nonlinear Systems,” World Scientific Publishing Company, River Edge, 2005. doi:10.1142/9789812567789 [5] W. Schweizer, “Numerical Quantum Dynamics,” Hingham, 2001. [6] M. Dorigo, V. Maniezzo and A. Colorni, “The Ant System: Optimization by a Colony of Cooperating Agents,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 26, 1, 1996, pp. 29-41. doi:10.1109/3477.484436