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Table of Contents
💥1 Overview
📚2 Results
🎉3 References
🌈4 MATLAB Code Implementation
1 Overview
The Sparrow Optimization Algorithm based on the Fusion of Sine-Cosine and Cauchy Mutation (SCSSA) is an optimization algorithm applied in time series prediction models to improve prediction accuracy. This algorithm is mainly divided into the following parts: 1. Sine-Cosine and Cauchy Mutation: Sine-Cosine mutation and Cauchy mutation are two common mutation strategies used to generate new individuals by performing mutation operations on the current population. These mutation strategies are based on mathematical functions and can help the algorithm quickly explore the search space. 2. Sparrow Optimization Algorithm (SCSSA): The Sparrow Optimization Algorithm is an optimization algorithm based on the foraging behavior of birds. This algorithm simulates the movement strategy of sparrows during foraging, optimizing the solution space through a combination of adaptive search and local search. SCSSA, combined with Sine-Cosine and Cauchy mutation, can help the algorithm better search for optimal solutions. 3. CNN-BiLSTM Model: The CNN-BiLSTM is a model that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory Networks (BiLSTM). CNN is used to extract spatial features from input data, while BiLSTM captures temporal dependencies in the input data. This combination can better handle time series data and improve the accuracy of prediction models. The basic principle of SCSSA, which integrates Sine-Cosine and Cauchy mutation, is as follows: First, SCSSA initializes the individuals of the Sparrow Algorithm using a refractive backward learning strategy. This strategy expands the search range by calculating the reverse solution of the current solution to find better candidate solutions. Second, SCSSA replaces the original position update formula of the discoverer in the Sparrow Algorithm with the Sine-Cosine strategy. When the food sought by the discoverer is located at a local optimum, a large number of followers will flock to that position, causing the discoverer and the entire population to stagnate, resulting in a loss of population position diversity. Therefore, the introduction of the Sine-Cosine strategy can effectively balance the global search and local development capabilities of the population. Additionally, SCSSA improves the step size search factor of the Sine-Cosine strategy to further enhance the performance of the algorithm. Finally, SCSSA replaces the original position update formula of the followers in the Sparrow Algorithm with the Cauchy mutation strategy. The Cauchy distribution is similar to the standard normal distribution but has smaller values at the origin and is flatter at both ends, approaching zero at a slower rate. Therefore, using Cauchy mutation to perturb the positions of the sparrows can expand the search scale of the algorithm, thereby enhancing the ability to escape local optima. Compared to the original Sparrow Algorithm, SCSSA has stronger global search and local development capabilities, making it better suited to tackle complex optimization problems. Furthermore, CNN-BiLSTM is a commonly used deep learning model for sequence prediction tasks. In this model, training data is first input into the CNN model, where feature extraction is performed through the construction of convolutional and pooling layers. Then, the BiLSTM model is used for sequence prediction. However, this model has many parameters that need to be adjusted, including learning rate, regularization parameters, number of neural network layers, number of convolutional layers, batch size, and maximum training iterations. Therefore, when using the CNN-BiLSTM model, parameter tuning is necessary to achieve optimal performance.
In summary, the SCSSA-CNN-BiLSTM model based on the fusion of Sine-Cosine and Cauchy mutation can fully utilize the optimization capabilities of the SCSSA algorithm and the time series modeling capabilities of the CNN-BiLSTM model to improve the accuracy of time series predictions. This model can find the optimal parameter combinations in time series data and utilize CNN and BiLSTM to extract features and model temporal relationships, thereby better predicting future values.
2 Results
3 References
Some content in this article is sourced from the internet, and references will be noted. If there are any inaccuracies, please feel free to contact us for removal.
[1] Zhang Bing, Zhou Dandan, Sun Jian, et al. Bus Arrival Time Prediction Model Based on Bidirectional Long Short-Term Memory Network [J]. Transportation System Engineering and Information, 2023, 23(2):148-160.
[2] Jiang Nanlin. Research on Short-Term Power Load Prediction Based on Improved Sparrow Search Algorithm Optimizing Long Short-Term Memory Network [J]. [2023-12-18].
4 MATLAB Code Implementation
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