分类： 工程与技术科学 >> 工程通用技术 提交时间： 2024-03-28
Baiyi Wang
Zipeng Zhang
Patrick Siarry
Xinhua Liu
Grzegorz Królczyk
Dezheng Hua
Frantisek Brumercik
Zhixiong Li
摘要： As a new intelligentoptimization algorithm, the African vulturesoptimization algorithm(AVOA) has been widely used in various fields today. However, when solving complex multimodal problems, the AVOA still has some shortcomings, such as low searching accuracy, deficiency on the search capability and tendency to fall into local optimum. In order to alleviate the main shortcomings of the AVOA, a nonlinear African vulture optimization algorithm combining Henon chaotic mapping theory and reverse learning competition strategy (HWEAVOA) is proposed. Firstly, the Henon chaotic mapping theory and elite population strategy are proposed to improve the randomness and diversity of the vulture’s initial population; Furthermore, the nonlinear adaptive incremental inertial weight factor is introduced in the location update phase to rationally balance the exploration and exploitation abilities, and avoid individual falling into a local optimum; The reverse learning competition strategy is designed to expand the discovery fields for the optimal solution and strengthen the ability to jump out of thelocal optimal solution. HWEAVOA and other advanced comparison algorithms are used to solve classical and CEC2022 test functions. Compared with other algorithms, the convergence curves of the HWEAVOA drop faster and the line bodies are smoother. These experimental results show the proposed HWEAVOA is ranked first in all test functions, which is superior to the comparison algorithms in convergence speed, optimization ability, and solution stability. Meanwhile, HWEAVOA has reached the general level in thealgorithm complexity, and its overall performance is competitive in theswarm intelligence algorithms.
分类： 电子与通信技术 >> 信息处理技术 分类： 工程与技术科学 >> 工程通用技术 分类： 机械工程 >> 机械工程其他学科 提交时间： 2022-02-08
摘要： The spectral leakage (SL) from windowing and the picket fence effect (PEF) from discretization have been among the standard contents in textbooks for many decades. The SL and PEF would cause the distortions in amplitude, frequency, and phase of signals, which have always been of concern, and attempts have been made to solve them. This paper proposes two novel decomposition theorems that can totally eliminate the SL and PEF, they could broaden the knowledge of signal processing. First, two generalized eigenvalue equations are constructed for multifrequency discrete real signals and complex signals. The two decomposition theorems are then proved. On these bases, exact decomposition methods for real and complex signals are proposed. For a noise-free multifrequency real signal with m sinusoidal components, the frequency, amplitude, and phase of each component can be exactly calculated by using just 4m1 discrete values and its second-order derivatives. For a multifrequency complex signal, only 2m1 discrete values and its first-order derivatives are needed. The numerical experiments show that the proposed methods have very high resolution, and the sampling rate does not necessarily obey the Nyquist sampling theorem. With noisy signals, the proposed methods have extraordinary accuracy.
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