## 1 Introduction  
As science and technology advance swiftly, and with the continuous evolution of social needs, optimization algorithms are becoming increasingly crucial in solving complex problems in various fields. From mechanical design and manufacturing to artificial intelligence, the rapid progress in these fields not only promotes the development of industry but also triggers the demand for optimization algorithms to be more efficient and innovative [1]. Within signal decomposition, numerous techniques have been developed for extracting fault features. Common algorithms include wavelet transform (WT) [2], empirical mode decomposition (EMD) [3], empirical wavelet transform (EWT) [4], and the local mean decomposition (LMD) [5], among others. Although these algorithms are widely applied and play a positive role in many fields, they also exhibit certain issues that impact the effectiveness of signal decomposition. When using WT for signal processing, the fixed choice of mother wavelet leads to a lack of flexibility and less applicability when dealing with different types of signals. EMD suffers from severe mode mixing and endpoint effects when decomposing signals. Although the LMD method efficiently mitigates the emergence of over- and under- envelopes in EMD decomposition, it continues to demonstrate endpoint effects. The limited adaptability and robustness of EWT restrict its capability for signal decomposition. In 2014, [PERSON] proposed VMD [6], a method with both a strong theoretical basis and notable robustness in extracting fault features in complex environments, such as strong noise. This algorithm has found application across diverse fields and has shown excellent performance [7; 8; 9]. However, the VMD algorithm itself has limitations, and the effectiveness of its decomposition may be affected by both the quantity of intrinsic mode functions (IMFs), denoted as \(K\), and the parameter \(\alpha\) for the penalty factor. With inappropriate parameter settings, issues such as mode mixing may arise when employing the VMD algorithm in signal processing. To address the shortcomings of the VMD algorithm, many researchers opt to employ swarm intelligence optimization algorithms for parameter tuning. [PERSON] et al. [10] utilized a genetic algorithm for the adaptive searching of optimal parameters for VMD, while [PERSON] et al. [11] utilized the grey wolf algorithm to acquire the optimal parameters for VMD. Other swarm intelligence algorithms used for optimizing VMD parameters include the PSO [12], WOA [13], multiverse optimization (MVO) algorithm [14], sailfish optimization (SFO) algorithm [15], Archimedes optimization algorithm (AOA) [16], etc. In 2020, [PERSON] et al. [17] introduced the SSA, known for its computational efficiency, simplicity of implementation, and ease of scalability. According to [PERSON] et al. 's analysis, the SSA demonstrates superior performance compared to several established algorithms like the PSO, GWO, and gravity search algorithm (GSA) regarding convergence accuracy, convergence speed, and robustness. Nevertheless, akin to other swarm intelligence algorithms, the SSA may suffer from low initial solution quality, and in solving complex optimization problems, it may experience a decrease in population diversity and vulnerability to becoming trapped in local optima toward the later stages of the solution process. Researchers have addressed the aforementioned issues and achieved positive results through various studies. [PERSON] et al. [18] proposed a multistrategy enhanced evolutionary sparrow search algorithm (ESSA), integrating tent map chaos during the initialization phase to expedite convergence speed and improve convergence accuracy. [PERSON] et al. [19] introduced chaotic back-propagation learning and dynamic weights to prevent the SSA from being entrapped in local optima. [PERSON] et al. [20] combined Levy flights and nonlinear inertia weight to present an improved SSA. [PERSON] et al.