基于改进樽海鞘群算法的含瓦斯煤破裂过程信号特征识别
Identification of Signal Characteristics of Gas Bearing Coal Fracture Process Based on Improved Salp Swarm Algorithm
付华 1管智峰 1刘尚霖 1刘昊 2陈子林1
作者信息
- 1. 辽宁工程技术大学电气与控制工程学院,辽宁 葫芦岛 125105
- 2. 中海油天津化工研究设计院有限公司,天津 300131
- 折叠
摘要
针对标准樽海鞘群算法存在的计算精度不足、易陷入局部停滞等缺陷,提出一种多策略融合的樽海鞘群算法.在初始化阶段,引入线性同余法随机发生器;利用野马算法优化樽海鞘领导者位置;采用金豺算法改进樽海鞘种群追随机制.通过测试函数寻优对比实验,证明多策略融合的樽海鞘群算法相比于其他智能算法在鲁棒性与稳定性方面均有显著提升.将多策略融合的樽海鞘群算法应用到含瓦斯煤破裂过程信号特征识别,实验结果表明:提出的含瓦斯煤破裂过程信号特征识别模型具有更好的表现,准确率可达 93.33％,相比其他识别模型,识别率更高.
Abstract
Aiming at the shortcomings of salp swarm algorithms,such as insufficient calculation accuracy and ease to fall into local opti-mum,an improved salp swarm algorithm with multi-strategy integtation is proposed.In the initialization stage,the linear congruence ran-dom generators is introduced.The wild horse optimization algorithm is used for improving the leader's position,and the following mecha-nism of salp swarm algorithm is improved by using golden jackal optimization.Through the comparison of test function optimization ex-periments,it is proved that the improved salp swarm algorithm based on multi-strategy integration has a significant improvement in ro-bustness and stability compared with other intelligent algorithms.The improved salp swarm algorithm with multi-strategy integration is applied to the signal feature identification of the gas bearing coal fracture process.The experimental results show that the proposed sig-nal feature identification model of the gas bearing coal fracture process has a better performance,with an accuracy rate of 93.33％.Com-pared with other identification models,the identification rate is higher.
关键词
含瓦斯煤破裂/智能优化算法/樽海鞘群算法/多策略融合/信号特征识别Key words
coal containing gas fracture/intelligent optimization algorithm/salp swarm algorithm/multi-strategy fusion/identification of signal characteristics引用本文复制引用
基金项目
国家自然科学基金项目(51974151)
国家自然科学基金项目(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)
出版年
2024
国家科技期刊平台
NSTL
万方数据