Artificial Bee Colony Algorithm MATLAB Implementation with Source Code

This article presents the MATLAB implementation and source code for the Artificial Bee Colony (ABC) algorithm. The ABC algorithm is an optimization technique inspired by the intelligent foraging behavior of honey bees. This algorithm exhibits inherent parallelism and adaptability, making it suitable for solving various optimization problems across different domains. The MATLAB implementation typically includes three main phases: employed bees phase, onlooker bees phase, and scout bees phase. Key functions commonly implemented involve: - Initialization of food source positions representing potential solutions - Fitness evaluation mechanisms for solution quality assessment - Employed bees exploring neighborhood solutions using modification equations - Onlooker bees selecting food sources based on probability proportional to fitness - Scout bees replacing abandoned solutions with new random ones The source code provides valuable learning resources for researchers to understand the algorithm's underlying principles and practical applications. It serves as an excellent reference for implementing swarm intelligence techniques in research projects, featuring modular code structure that allows easy customization of parameters like colony size, limit values, and maximum cycles. The implementation demonstrates proper handling of optimization constraints and visualization of convergence behavior, making it suitable for both educational purposes and advanced research applications.