A Quantum Behaved Gravitational Search Algorithm

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DOI: 10.4236/iim.2012.46043    7,442 Downloads   9,635 Views   Citations

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.

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.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[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

  
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