Line-by-Line Chinese Annotation of GWO Algorithm for Optimizing SVM Parameters

In the field of machine learning, Support Vector Machine (SVM) is a widely used classification algorithm whose performance largely depends on the selection of parameters C and gamma. While traditional grid search methods are straightforward but inefficient, the Grey Wolf Optimizer (GWO) algorithm, as an emerging swarm intelligence optimization technique, can more efficiently discover optimal parameter combinations.

The GWO algorithm mimics the social hierarchy and hunting behavior of grey wolf packs, guiding the search direction through three hierarchical levels: the alpha wolf (leader), beta wolf (second-in-command), and delta wolf (ordinary members). This mechanism maintains a strong balance between exploration and exploitation, making it particularly suitable for solving continuous space optimization problems like SVM parameter tuning.

In MATLAB implementation, the algorithm first initializes the grey wolf population, then enters the main iterative loop. During each iteration, the algorithm updates the positions of other individuals based on the current best solution's location - this process corresponds to the grey wolf pack's prey tracking behavior. As iterations progress, the convergence factor gradually decreases, shifting the search focus from global exploration to local exploitation.

The annotated code provides detailed explanations of how to integrate GWO with SVM parameter optimization, including key components such as fitness function definition, position update formula implementation, and termination condition settings. Through studying these annotations, one can gain deep understanding of swarm intelligence algorithms' application techniques in machine learning parameter optimization.

This GWO-based SVM parameter optimization method demonstrates faster convergence speed and superior global search capability compared to traditional approaches, effectively enhancing SVM model classification performance - with particularly significant advantages when handling high-dimensional data.