[1]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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Parameter identification of chaotic system based on a multi-strategy improved whale optimization algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 1 Page number: 176-189 Column: 学术论文—人工智能基础 Public date: 2024-01-05
Title:
Parameter identification of chaotic system based on a multi-strategy improved whale optimization algorithm
Author(s):
PAN Yueyue1;  WU Lifei2;  YANG Xiaozhong2
1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;
2. Institute of Information and Computing, School of Mathematics and Physics, North China Electric Power University, Beijing 102206, China
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
CLC:
TP18
DOI:
10.11992/tis.202303043
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.
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