Research on Particle Swarm Optimization Algorithm Based on Quantum Computing Technology

Guanghui Wang^1,

¹Chinese Academy of Meteorological Sciences, Beijing 100081, China

Cite this paper

Guanghui Wang. Research on Particle Swarm Optimization Algorithm Based on Quantum Computing Technology. American Journal of Electrical and Electronic Engineering. 2020; 8(1):21-25. doi: 10.12691/AJEEE-8-1-3

Abstract

In view of the shortcomings of particle swarm optimization such as premature convergence to local optimization, particle swarm optimization algorithm based on quantum gate and particle swarm optimization algorithm based on quantum behavior are studied in this paper. The first algorithm uses the random observation method of quantum bit coding particles to simulate quantum particle collapse for generating a population and uses the quantum rotating gate to generate a new population. The adaptive mutation operator is used for ensuring the diversity of the population, effectively reducing the impact of local optimization; therefore, the robustness of the algorithm is improved. The second algorithm uses the probability density function of quantum computation to make the particles jump out of the local extreme points and fulfils the global search, which is more suitable for continuous optimization. The results of computational experiments show that both the two particle swarm optimization algorithms based on quantum technology have a good global convergence.

Keywords

particle swarm optimization algorithm, quantum gate, quantum behavior, quantum computation, optimal value

Copyright

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References

[1]	Narayanan A, Moore M, "Quantum-inspired genetic algorithm," International Conference on Evolutionary Computation. IEEE, 1996, 61-66.
[2]	Han K H, Kim J H, "Genetic quantum algorithm and its application to combinational optimization problem," Proceedings of the International Congress on Evolutionary Computation. IEEE Press. 2000, 1354-1360.
[3]	Jun Sun, Bin Feng, WenboXu. "Particle swarm optimization with particles having quantum behavior," Congress on Evolutionary Computation，2004, 1354-1360.
[4]	Shi Y. H., Eberhart R. C., "Parameter selection in particle swarm optimization," Lecture Notes in Computer Science, V10 (1447), 1998, 591-600.
[5]	Schaffer J. D, D. Garuana R A, Eshelman L J, Das R, "A study of control par-ameters affecting online performance of genetic algorithm for function optimization," In [337], 1993, 573-580.
[6]	Xu Bo, "A novel quantum continuous particle swarm optimization algorithm," Value Engineering, 2011, No.1 181-182.
[7]	Clerc, M.; Kennedy, J, "The particle swarm-explosion, stability, and convergence in a multidimensional complex space," IEEE Trans. Evolut. Comput. 2002(6), 58-73.
[8]	TAO Chongyang, YANG Xinyu, YU Xiangshen, ZHAO Hang, "Control parameter analysis of quantum behaved particle swarm optimization algorithm," Journal of Computer pplication, 34(s2), 2014, 169-171.