An improved sparrow search algorithm with multi-strategy integration.

IF 3.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Reports Pub Date : 2025-01-26 DOI:10.1038/s41598-025-86298-z

Zongyao Wang, Qiyang Peng, Wei Rao, Dan Li

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Abstract

Addressing the shortcomings of the Sparrow Search Algorithm (SSA), such as low accuracy of convergence and tendency of falling into local optimum, a Multi-strategy Integrated Sparrow Search Algorithm (MISSA) is proposed. In this method, by improving the black-winged kite algorithm and applying it to the producer's position update formula, an improved search strategy (ISS) is firstly proposed to enhance search ability. Secondly, a new strategy inspired by the Coot algorithm, called the group follow strategy (GFS), is proposed to improve the ability to jump out of the local optimum. Finally, a proposed random opposition-based learning strategy (ROBLS) is applied to the population after each iteration to enhance its diversity. To verify MISSA's effectiveness, extensive testing is conducted on 24 benchmark functions as well as CEC 2017 functions. The experimental results, complemented by Wilcoxon rank-sum tests, conclusively demonstrate that MISSA outperforms SSA and other advanced optimization algorithms, exhibiting superior overall performance.

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一种改进的多策略集成麻雀搜索算法。

针对麻雀搜索算法（SSA）收敛精度低、容易陷入局部最优的缺点，提出了一种多策略集成麻雀搜索算法（MISSA）。该方法通过改进黑翼风筝算法，并将其应用于生产者位置更新公式中，提出了一种改进的搜索策略（ISS），提高了搜索能力。其次，在Coot算法的启发下，提出了一种新的策略——群跟随策略（group follow strategy， GFS），以提高跳出局部最优的能力。最后，提出了一种基于随机对立的学习策略（ROBLS），用于每次迭代后的种群，以增强其多样性。为了验证MISSA的有效性，我们对24个基准函数以及CEC 2017函数进行了广泛的测试。实验结果与Wilcoxon秩和检验相结合，最终证明MISSA优于SSA等先进的优化算法，整体性能优越。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Scientific Reports Natural Science Disciplines-

CiteScore

7.50

自引率

4.30%

发文量

19567

审稿时长

3.9 months

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