Optimization techniques play a pivotal role in enhancing the performance of engineering components across various real-world applications. Traditional optimization methods are often augmented with exploitation-boostin...
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Optimization techniques play a pivotal role in enhancing the performance of engineering components across various real-world applications. Traditional optimization methods are often augmented with exploitation-boosting techniques due to their inherent limitations. Recently, nature-inspired algorithms, known as metaheuristics (MHs), have emerged as efficient tools for solving complex optimization problems. However, these algorithms face challenges such as imbalance between exploration and exploitation phases, slow convergence, and local optima. Modifications incorporating oppositional techniques, hybridization, chaotic maps, and levy flights have been introduced to address these issues. This article explores the application of the recently developed crayfish optimization algorithm (COA), assisted by artificial neural networks (ANN), for engineering design optimization. The COA, inspired by crayfish foraging and migration behaviors, incorporates temperature-dependent strategies to balance exploration and exploitation phases. Additionally, ANN augmentation enhances the algorithm's performance and accuracy. The COA method optimizes various engineering components, including cantilever beams, hydrostatic thrust bearings, three-bar trusses, diaphragm springs, and vehicle suspension systems. Results demonstrate the effectiveness of the COA in achieving superior optimization solutions compared to other algorithms, emphasizing its potential for diverse engineering applications.
This paper proposes a parameter optimization method for a terminal sliding mode controller (TSMC) based on a multi-strategy improved crayfish algorithm (JLSCOA) to enhance the performance of ship dynamic positioning s...
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This paper proposes a parameter optimization method for a terminal sliding mode controller (TSMC) based on a multi-strategy improved crayfish algorithm (JLSCOA) to enhance the performance of ship dynamic positioning systems. The TSMC is designed for the "Xinhongzhuan" vessel of Dalian Maritime University. JLSCOA integrates subtractive averaging, Levy Flight, and sparrow search strategies to overcome the limitations of traditional crayfish algorithms. Compared to COA, WOA, and SSA algorithms, JLSCOA demonstrates superior optimization accuracy, convergence performance, and stability across 12 benchmark test functions. It achieves the optimal value in 83% of cases, outperforms the average in 83% of cases, and exhibits stronger robustness in 75% of cases. Simulations show that applying JLSCOA to TSMC parameter optimization significantly outperforms traditional non-optimized controllers, reducing the average time for three degrees of freedom position changes by over 300 s and nearly eliminating control force and velocity oscillations.
Analog circuits are an important component of integrated circuit systems, and circuit systems are the foundation for ensuring the normal operation of electronic devices. Therefore, it is necessary to efficiently diagn...
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Analog circuits are an important component of integrated circuit systems, and circuit systems are the foundation for ensuring the normal operation of electronic devices. Therefore, it is necessary to efficiently diagnose and maintain faults in analog circuits. However, due to the tolerance, high nonlinearity, and susceptibility to environmental interference of analog circuit components, the development of related research on fault diagnosis has been hindered, and it cannot meet the current practical requirements for high safety and reliability of electronic devices. With the continuous increase in circuit scale and integration level, how to effectively and as much as possible extract more discriminative fault features is the key research direction of analog circuit fault diagnosis. Therefore, this article proposes a variational model decomposition (VMD) feature extraction method that combines Butterfly and Cauchy Sparrow search algorithms (BCSSA) and relies on an improved crayfish optimization algorithm (COA) to optimize the Extreme Learning Machine (ELM). Decomposition (VMD) feature extraction method, and rely on Improved crayfish Optimization algorithm (COA) Optimized Extreme Learning Machine (ELM) to complete the classification of faults. Firstly, the BCSSA algorithm is used to optimize the number of VMD decomposition modes K and the penalty factor alpha to achieve the optimal VMD decomposition of the original fault signal, obtain a series of Intrinsic Mode Function (IMF) and calculate its envelope entropy, determine the optimal IMF component by selecting the IMF component with the lowest envelope entropy., and calculate its time-domain parameter, then normalize and reduce the dimensionality to construct the vector that contains the characteristics of the fault. The normalized dimensionality reduction process constitutes the fault feature vector;secondly, the ICOA algorithm is introduced to optimize the ELM;Ultimately, the fault feature vector is fed into the ELM
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