Bacterial Foraging Random Optimization: Research Paper and MATLAB Source Code

In the following paragraphs, we will discuss the Bacterial Foraging Random Optimization algorithm and its MATLAB source code in detail. As a type of evolutionary algorithm, this approach can solve various optimization problems including global optimization, multi-objective optimization, constraint optimization, and dynamic optimization. The core concept of Bacterial Foraging Random Optimization algorithm simulates the foraging behavior of bacteria in a petri dish to search for optimal solutions. In this process, bacteria move by sensing environmental information, with representative cells indicating solution positions. The MATLAB implementation includes key functions for bacterial population initialization, chemotaxis movement simulation, reproduction mechanisms, and elimination-dispersal events. The algorithm has been widely applied in numerous fields such as image processing, machine learning, and computer vision. Our MATLAB source code, developed based on this algorithm, provides a practical framework with clear parameter configuration and optimization process visualization to help users better understand and apply Bacterial Foraging Random Optimization in their research projects.