Journal of Xidian University ›› 2020, Vol. 47 ›› Issue (2): 83-90.doi: 10.19665/j.issn1001-2400.2020.02.012

Previous Articles     Next Articles

Improved gravitational search algorithm for shaped beam forming

SUN Cuizhen1,2,DING Jun1,GUO Chenjiang1   

  1. 1.School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
    2.School of Communication and Information Engineering,Xi’an University of Science and Technology, Xi’an 710054, China
  • Received:2019-09-01 Online:2020-04-20 Published:2020-04-26


In view of the adverse effect of the random initial value on the performance and convergence speed of the gravitation search algorithm, a quasi-oppositional gravity search algorithm (QOGSA) is proposed. The quasi-oppositional based learning OBL is embedded into the GSA algorithm, the number of iteration is divided into multiple learning cycle, the oppositional probability is adjusted according to the success rate of the past learning cycle, and an adjustable oppositional probability is designed to optimize the timing of the mechanism in the evolution, which improves the speed of the algorithm to search for the optimal solution greatly. On this basis, in order to improve the population diversity, elite particles are retained to the next generation population. They replace the particles with a poor fitness value and acquire a higher optimization accuracy. Compared with the existing algorithms in the literature, the optimization accuracy of the QOGSA for the average optimal value of the single-peak and multi-peak test functions can be improved by 1016. For the shaping results of different types of beam, the optimization accuracy of the improved algorithm for the sidelobe can be improved from 1.26dB to 5.99dB. On the premise of the fastest convergence speed, the QOGSA can greatly avoid the problem that other optimization algorithms tend to fall into local optimization, with the overall performance being the best.

Key words: gravitational search algorithm, shaped beam synthesis, opposition-based learning, adjustable oppositional probability, elite particles

CLC Number: 

  • TN821+.91