Multi Strategy Improved Sparrow Search Algorithm Based on Rough Data Reasoning

被引：0
作者：
Zhou N. [1 ]
Zhang S. [1 ]
Zhang C. [1 ]
机构：
[1] School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou
来源：
Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2022年 / 51卷 / 05期
关键词：
Adaptive algorithms; Low difference sequence; Rough data-deduction; Sparrow search algorithm; Swarm intelligence;
D O I：
10.12178/1001-0548.2021288
中图分类号：
学科分类号：
摘要：
Aiming at the problem that the diversity of sparrow search algorithm is reduced and it is easy to fall into local optimum in the iterative process, a multi strategy improved sparrow search algorithm (RSSA) based on rough data-deduction is proposed. Firstly, the algorithm initializes the population with the idea of low difference sequence to enhance the global search ability of the algorithm and ensure the integrity of rough data reasoning domain. Then, the rough reasoning data theory is introduced, and the relationship between individuals is established by combining fitness and distance, so as to improve the convergence speed and the ability to jump out of the local optimum. Moreover, the over bounded individuals in the iteration are assigned to the value near the boundary instead of the maximum or minimum value of the boundary at the same time, which ensures the diversity of the population and improves the convergence speed of the algorithm. Compared with the other three algorithms and traditional sparrow search algorithm, the simulation results based on 11 test functions show that RSSA has faster convergence speed, higher accuracy and better effect in the face of multi peak problems. © 2022, Editorial Board of Journal of the University of Electronic Science and Technology of China. All right reserved.
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页码：743 / 753
页数：10
相关论文
共 25 条
  • [11] XUE J, SHEN B., A novel swarm intelligence optimization approach: Sparrow search algorithm, Systems Science & Control Engineering, 8, 1, pp. 22-34, (2020)
  • [12] LI Y L, WANG S Q, CHEN Q R, Et al., Comparative study of several new swarm intelligence optimization algorithms, Computer Engineering and Applications, 56, 22, pp. 1-12, (2020)
  • [13] DEMO N, TEZZELE M, ROZZA G., A supervised learning approach involving active subspaces for an efficient genetic algorithm in high-dimensional optimization problems, SIAM Journal on Scientific Computing, 43, 3, pp. B831-B853, (2021)
  • [14] OLIVEIRA F, OLIVEIRA F R., A Global newton method for the nonsmooth vector fields on riemannian manifolds, Journal of Optimization Theory and Applications, 190, 1, pp. 259-273, (2021)
  • [15] HEDAR A R, DEABES W, AMIN H H, Et al., Global sensing search for nonlinear global optimization, Journal of Global Optimization, (2022)
  • [16] LYU X, MU X D, ZHANG J, Et al., Chaos sparrow search optimization algorithm, Journal of Beijing University of Aeronautics and Astronautics, 8, pp. 1712-1720, (2021)
  • [17] LYU X, MU X D, ZHANG J., Multi-threshold image segmentation based on improved sparrow search algorithm, Systems Engineering and Electronics, 43, 2, pp. 318-327, (2021)
  • [18] MA J, HAO Z, SUN W., Enhancing sparrow search algorithm via multi-strategies for continuous optimization problems, Information Processing & Management, 59, 2, (2022)
  • [19] MIRJALILI S, GANDOMI A H., Chaotic gravitational constants for the gravitational search algorithm, Applied Soft Computing, 53, pp. 407-419, (2017)
  • [20] SHUO Y, LIN Y, WU J Z., Rough data-deduction based on the upper approximation, Information Sciences, 373, pp. 308-320, (2016)
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