MATLAB Implementation of Fruit Fly Optimization Algorithm for Support Vector Regression

The Fruit Fly Optimization Algorithm (FOA) is an intelligent optimization algorithm inspired by the foraging behavior of fruit flies in nature. It simulates the process of fruit fly swarms searching for food to find optimal solutions, featuring rapid convergence speed and strong global search capabilities. Support Vector Regression (SVR) is a machine learning method based on statistical learning theory, which fits data by finding the optimal hyperplane and performs excellently in solving small-sample, nonlinear, and high-dimensional pattern recognition problems. Combining the fruit fly algorithm with support vector regression leverages FOA's powerful global search capability to optimize key parameters in SVR, such as the penalty factor C and kernel function parameters. This approach effectively avoids the tendency of traditional parameter selection methods to fall into local optima, thereby improving the prediction accuracy and generalization ability of the SVR model. In MATLAB implementation, two main components are typically required: the fruit fly algorithm optimizer and the support vector regression model. The FOA component is responsible for initializing fruit fly population positions, calculating individual fitness values, and iteratively updating positions to find optimal solutions using distance-based search mechanisms. The SVR component receives optimized parameters to construct regression models and perform performance evaluation through MATLAB's fitrsvm function or custom kernel implementations. Text-based program files typically contain pseudocode descriptions of the algorithm and key parameter explanations, facilitating researchers' understanding of algorithmic principles and implementation details. MATLAB M-files contain complete executable code, generally divided into functional modules including: initialization module (parameter setup and population generation), fruit fly search module (fitness calculation and position updates), SVR modeling module (model training with optimized parameters), and performance evaluation module (calculating metrics like RMSE and R²). This integration of biologically-inspired algorithms and machine learning methods provides new technical approaches for solving complex engineering optimization problems, with broad application prospects in predictive modeling, system identification, and related fields. Key implementation considerations include proper parameter encoding for FOA, efficient fitness function design incorporating SVR cross-validation, and integration with MATLAB's Statistics and Machine Learning Toolbox.