Artificial Bee Colony Algorithm for Function Optimization Problem Solving

The Artificial Bee Colony (ABC) algorithm is a widely used optimization algorithm designed to solve various function optimization problems. This implementation includes several standard benchmark functions such as the Sphere function (unimodal optimization), Rastrigin function (multimodal with many local minima), and Rosenbrock function (non-convex valley optimization). By incorporating these test functions, we can effectively evaluate and compare the performance of the ABC algorithm through fitness calculations and convergence analysis. The code implementation demonstrates how to define these benchmark functions as objective functions, where each function is typically implemented as a separate module or function call that takes a solution vector as input and returns a fitness value. This approach allows researchers to better understand and apply the ABC algorithm's three-phase process (employed bees, onlooker bees, and scout bees) to solve complex function optimization problems through iterative population updates and neighbor solution generation mechanisms.