Improved sparrow search algorithm based on good point set

被引：0

作者：

Yan, Shaoqiang ^{[1
]}

Yang, Ping ^{[1
]}

Zhu, Donglin ^{[2
]}

Wu, Fengxuan ^{[1
]}

Yan, Zhe ^{[1
]}

机构：

[1] School of Combat Support, Rocket Military Engineering University, Xi’an

[2] School of Information Engineering, Jiangxi University of Technology, Ganzhou

来源：

Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2023年 / 49卷 / 10期

基金：

中国国家自然科学基金;

关键词：

dimension by dimension lens reverse learning; good point set; iterative local search; optimization algorithm; sparrow search algorithm;

D O I：

10.13700/j.bh.1001-5965.2021.0730

中图分类号：

学科分类号：

摘要：

An enhanced sparrow search algorithm based on a good point set (GSSA) is developed to address the sparrow search algorithm (SSA) weak starting population quality, instability, and susceptibility to local optimization. Firstly, adding a good point set makes the initial population more uniform and improves the population diversity. Second, while retaining the benefits of the original algorithm’s quick convergence speed, an enhanced iterative local search is merged with the features of the SSA algorithm to increase the search capabilities of the latter. Finally, a dimension by dimension lens imaging reverse learning mechanism is added to the algorithm to reduce the interference between various dimensions, help the algorithm jump out of local optimization and accelerate convergence. Through 12 test function simulation experiments, with the help of the Wilcoxon rank sum test and mean error M, it is proved that GSSA has greatly improved the optimization performance such as optimization accuracy and stability, and the convergence speed is faster. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.

引用

页码：2790 / 2798

页数：8

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