多策略融合的改进麻雀搜索算法及其应用
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辽宁工程技术大学电气与控制工程学院

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TD713

基金项目:

国家自然科学基金项目(51974151;71771111);辽宁省高等学校国(境)外培养项目(2019GJWZD002);辽宁省高等学校创新团队项目(LT2019007);辽宁省教育厅科技项目(LJ2019QL015)


Improved sparrow search algorithm with multi-strategy integration and its application
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College of Electrical and Control Engineering, Liaoning Technical University

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Project Supported by National Natural Science Foundation of China(NSFC)(51974151;71771111);Foreign Education Program of Liaoning Universities(2019GJWZD002);Innovative Team Project of Universities of Liaoning Province(LT2019007); Science and Technology Project of Liaoning Provincial Department of Education (LJ2019QL015).

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    摘要:

    针对麻雀搜索算法易陷入局部最优、收敛速度慢等不足,提出一种多策略融合的改进麻雀搜索算法.采用精英混沌反向学习策略生成初始种群,增强初始个体的质量和种群多样性,实现对更多优质搜索区域的勘探以提升算法的局部极值逃逸能力和收敛性能;结合鸡群算法的随机跟随策略优化麻雀搜索算法中跟随者的位置更新过程,平衡算法的局部开发性能和全局搜索能力;采用柯西-高斯变异策略提升算法的种群多样性保持能力和抗停滞能力.对10个不同特征的基准测试函数进行寻优,测试结果与 Wilcoxon符号秩检验结果均表明改进算法具有更好的寻优精度、收敛性能和稳定性.最后,改进算法对最小二乘支持向量机的参数进行优化,应用于煤与瓦斯突出危险性辨识,通过实验进一步验证了改进策略的有效性和改进算法的优越性.

    Abstract:

    Aiming at the shortcomings of sparrow search algorithm, such as easy to fall into local optimum and slow convergence speed, an improved sparrow search algorithm based on multi-strategy fusion is proposed. Elite chaotic reverse learning strategy is used to generate the initial population, which enhance the quality of the initial individuals and the diversity of the population, and realize the exploration of more high-quality search areas to improve the local extremum escape ability and convergence performance of the algorithm. Combined with the random following strategy of chicken swarm algorithm, the position updating process of the followers in the sparrow search algorithm was optimized, and the local development performance and global search ability of the algorithm were balanced. The Cauchy-Gauss mutation strategy was used to improve the ability of maintaining population diversity and resisting stagnation. Ten benchmark test functions with different characteristics are optimizated. The test results and Wilcoxon"s signed rank test results both show that the improved algorithm has better optimization accuracy, convergence performance and stability. Finally, the improved algorithm is used to optimize the parameters of the least square support vector machine and is applied to the identification of coal and gas outburst risk. The effectiveness of the improved strategy and the superiority of the improved algorithm are further verified by comparetive experiments.

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  • 收稿日期:2021-04-07
  • 最后修改日期:2021-07-24
  • 录用日期:2021-07-29
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