一种基于捕食优化的捕食鸟的新型杂交,具有差异进化突变和混沌动态识别交叉
Serdar Ekinci1, Davut Izci2,3, Murat Kayri4
1Department of Computer Engineering, Bitlis Eren University, 13100, Bitlis, Turkey.
Scientific reports
|December 9, 2025
概括
一个新的混合优化算法 (h-BPBODE) 准确地识别了洛伦兹,陈和罗斯勒等混乱系统中的参数. 这种方法改进了现有技术,为复杂的非线性系统识别提供了更快的融合和更高的可靠性.
科学领域:
- 非线性动力学是一种非线性动力学.
- 计算智能是一种计算智能.
- 系统工程 系统工程
背景情况:
- 在混乱系统中参数识别是一个具有挑战性的反向问题,因为对扰动的敏感性.
- 传统的时间域方法经常面临不良条件,需要强大的客观函数和元启发式方法.
研究的目的:
- 引入一种新的混合优化算法h-BPBODE,用于准确识别混乱系统的参数.
- 为了提高探索和利用之间的平衡,在改进系统识别的元启发性搜索中进行探索和利用.
主要方法:
- 开发了一种基于猎物优化的混合鸟类与差异进化 (h-BPBODE) 算法.
- 整合差异进化突变和交叉操作员到捕食鸟类基于优化的行为阶段.
- 在洛伦茨,陈和罗斯勒混乱系统上验证了算法,以恢复参数.
主要成果:
- 在所有测试的混沌系统中,h-BPBODE 实现了精确的参数恢复,其余量可以忽略不计.
- 与其他元启发方法相比,拟议的算法证明了更快的收率和明显较低的运行到运行的方差.
- 统计分析证实了h-BPBODE在混乱系统识别中的稳定性和精度.
结论:
- 基于捕食优化的杂交鸟类与差异进化 (h-BPBODE) 是混乱系统识别的可靠和有效的框架.
- 与现有方法相比,h-BPBODE在准确性,速度和一致性方面提供了卓越的性能.
- 开发的方法显示了在更广泛的非线性估计任务中应用的潜力.
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