Basic Fruit Fly Optimization Algorithm (BFOA)

In this section, we'll explore the Basic Fruit Fly Optimization Algorithm (BFOA) in greater detail. This algorithm employs a straightforward yet effective approach inspired by fruit flies' food-seeking behavior, making it particularly suitable for optimization beginners. As a heuristic algorithm, BFOA simulates how fruit flies locate food sources through olfactory and visual senses. The core implementation involves two phases: smell-based global search where solutions are generated randomly around the current best position, followed by vision-based local refinement where the algorithm evaluates and updates the optimal solution. Typically applied to continuous optimization problems like function optimization and parameter tuning, BFOA's key advantage lies in its rapid convergence to near-optimal solutions within limited iterations. The algorithm structure typically requires only 20-50 lines of code, primarily consisting of initialization, fitness evaluation, and position update functions. Consequently, Fruit Fly Optimization Algorithm finds extensive practical applications in machine learning (for hyperparameter optimization), data mining (feature selection), and image processing tasks where computational efficiency is crucial.