Artificial Bee Colony Optimization Algorithm Source Code

This repository provides a comprehensive source code implementation of the Artificial Bee Colony (ABC) optimization algorithm. The codebase serves as an excellent foundation for both developing custom optimization solutions and gaining deeper insights into swarm intelligence mechanisms. The ABC algorithm biologically mimics the foraging behavior of honeybee colonies in nature, employing sophisticated simulation of bee foraging patterns, information exchange among colony members, and cooperative search strategies to locate optimal solutions. The implementation includes key components such as employed bees, onlooker bees, and scout bees with their respective roles in solution exploration and exploitation. This optimization technique finds widespread applications across various domains including engineering optimization, combinatorial optimization problems, and graph theory challenges. By studying this source code, developers can better understand parameter configuration, fitness evaluation functions, and the iterative improvement process characteristic of swarm intelligence algorithms. The implementation facilitates hands-on learning of how probability-based selection mechanisms and neighborhood search operations contribute to global optimization performance, ultimately enhancing one's capabilities in algorithm design and complex problem-solving.