[1]潘悦悦,吴立飞,杨晓忠.一种多策略改进鲸鱼优化算法的混沌系统参数辨识[J].智能系统学报,2024,19(1):176-189.[doi:10.11992/tis.202303043]
PAN Yueyue,WU Lifei,YANG Xiaozhong.Parameter identification of chaotic system based on a multi-strategy improved whale optimization algorithm[J].CAAI Transactions on Intelligent Systems,2024,19(1):176-189.[doi:10.11992/tis.202303043]
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PAN Yueyue,WU Lifei,YANG Xiaozhong.Parameter identification of chaotic system based on a multi-strategy improved whale optimization algorithm[J].CAAI Transactions on Intelligent Systems,2024,19(1):176-189.[doi:10.11992/tis.202303043]
一种多策略改进鲸鱼优化算法的混沌系统参数辨识
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
19
期数:
2024年第1期
页码:
176-189
栏目:
学术论文—人工智能基础
出版日期:
2024-01-05
- Title:
- Parameter identification of chaotic system based on a multi-strategy improved whale optimization algorithm
- 关键词:
- 多策略改进鲸鱼优化算法; 混沌系统; 参数辨识; Chebyshev 混沌映射; 自适应t分布; 蚁狮优化算法; 基准函数; Wilcoxon 秩和检验
- Keywords:
- multi-strategy improved whale optimization algorithm; chaotic system; parameter identification; Chebyshev chaotic map; adaptive t distribution; ant lion optimization algorithm; benchmark function; Wilcoxon rank sum test
- 分类号:
- TP18
- 文献标志码:
- 2023-09-20
- 摘要:
- 针对混沌系统参数辨识精度不高的问题，以鲸鱼优化算法(whale optimization algorithm，WOA)为基础，提出一种多策略改进鲸鱼优化算法(multi-strategy improved whale optimization algorithm，MIWOA)。采用Chebyshev混沌映射选取高质量初始种群，采用非线性收敛因子和自适应权重，提高算法收敛速度，为了避免算法陷入局部最优，动态选择自适应t分布或蚁狮优化算法更新后期位置，提高处理局部极值的能力。通过对10个基准函数和高维测试函数进行仿真试验，表明MIWOA具有良好的稳定性和收敛精度。将MIWOA应用于辨识$ {\rm{R}}\ddot {\rm{o}}{\rm{ssler}} $和$ {\rm{L}}\ddot {\rm{u}} $混沌系统参数，仿真结果优于现有成果，表明本文MIWOA辨识混沌系统参数的高效性和实用性。
- Abstract:
- Aimed at the problem of low parameter identification accuracy of chaotic systems, a multi-strategy improved whale optimization algorithm (MIWOA) is proposed based on the whale optimization algorithm (WOA). MIWOA uses Chebyshev chaotic mapping to select high-quality initial populations, and nonlinear convergence factor and adaptive weight to improve the convergence speed of the algorithm. In order to avoid falling into local optimal solution, MIWOA dynamically selects adaptive t distribution or ant lion optimization algorithm to update the later position and improve the ability to handle local extremum. Through simulation experiments on 10 benchmark functions and high-dimensional test functions, it is shown that MIWOA has good stability and convergence accuracy. Applying MIWOA to identify the parameters of $ {\rm{R}}\ddot {\rm{o}}{\rm{ssler}} $ and $ {\rm{L}}\ddot {\rm{u}} $ chaotic systems, the simulation results are superior to existing achievements, indicating the efficiency and practicality of MIWOA in identifying chaotic system parameters in this paper.
- 参考文献/References:
-
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备注/Memo
收稿日期:2023-03-30。
基金项目:中央高校基本科研业务费专项基金项目(2021MS045)；华北电力大学国内外联合培养博士生资助项目(2020).
作者简介:潘悦悦，博士研究生，主要研究方向为智能优化算法。E-mail：panyueyue@ncepu.edu.cn;吴立飞，高级工程师，博士，主要研究方向为系统辨识、过程控制。主持教育部中央高校基本科研业务费专项基金2项。发表学术论文30 余篇。E-mail：wulf@ncepu.edu.cn;杨晓忠，教授，博士生导师，主要研究方向为智能优化算法、人工智能。主持国家科技重大专项子课题3项、国家自然科学基金面上项目2项。发表学术论文100 余篇。E-mail：yxiaozh@ncepu.edu.cn
通讯作者:杨晓忠. E-mail：yxiaozh@ncepu.edu.cn
更新日期/Last Update:
1900-01-01