Quantum Inspired Differential Evolution Algorithm

Binxu Li; Panchi Li

doi:10.4236/ojop.2015.42004

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Open Journal of Optimization > Vol.4 No.2, June 2015

Quantum Inspired Differential Evolution Algorithm ()

Binxu Li, Panchi Li

School of Computer and Information Technology, Northeast Petroleum University, Daqing, China.

DOI: 10.4236/ojop.2015.42004 PDF HTML XML 7,694 Downloads 8,415 Views Citations

Abstract

To enhance the optimization performance of differential evolution algorithm, by studying the implementation mechanism of differential evolution algorithm, a new idea of incorporating differential strategy and rotation of qubits in the Bloch sphere is proposed in this paper. In the proposed approach, the individuals are encoded by qubits described on Bloch sphere, and the rotation angles of qubits in current individual are obtained by differential strategy. The axis of rotation is designed by using vector product theory, and the rotation matrixes are constructed by using Pauli matrixes. Taking the corresponding qubits in current best individual as targets, the qubits in current individual are rotated to the target qubits about the rotation axis on the Bloch sphere. The Hadamard gates are used to mutate individuals. The simulation results of optimizing the minimum value of functions indicate that, for an iterative step, the average time of the proposed approach is 13 times as long as that of the classical differential evolution algorithm. When the same limited steps are applied in two approaches, the average optimization result of the proposed approach is 0.3 times as great as that of the classical differential evolution algorithm; when the same running time is applied in two approaches, the average optimization result of the proposed approach is 0.4 times as great as that of the classical differential evolution algorithm. These results suggest that the proposed approach is inefficient in computational ability; however, it is obviously efficient in optimization ability, and the overall optimization performance is better than that of the classical differential evolution algorithm.

Keywords

Quantum Computation, Qubits Encoding, Bloch Spherical Search, Quantum Differential Evolution

Share and Cite:

Li, B. and Li, P. (2015) Quantum Inspired Differential Evolution Algorithm. Open Journal of Optimization, 4, 31-39. doi: 10.4236/ojop.2015.42004.

Conflicts of Interest

The authors declare no conflicts of interest.

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