Source Code Implementation of Bacterial Foraging Optimization Algorithm in MATLAB

Bacterial Foraging Optimization Algorithm (BFOA) is a swarm intelligence algorithm inspired by the foraging behavior of E. coli bacteria, commonly used to solve complex optimization problems. The algorithm simulates bacterial behaviors including chemotaxis, reproduction, and elimination-dispersal to search for global optimal solutions. The MATLAB implementation of BFOA typically involves the following core steps: Population Initialization: Randomly generate a population of bacteria individuals, where each bacterium represents a potential solution to the optimization problem. In code, this is implemented using rand() or randn() functions to create initial positions within search space boundaries. Chemotaxis Operation: Simulates bacterial movement in concentration gradients, including both swimming and tumbling motions. The algorithm evaluates fitness functions to determine if bacteria are moving toward better regions. MATLAB's vectorization capabilities efficiently handle simultaneous position updates for all bacteria using matrix operations. Reproduction Operation: Bacteria are sorted based on their fitness values. Lower-fitness individuals are eliminated while higher-fitness individuals reproduce to maintain population size. This can be implemented using sort() function and selective replication of top-performing bacteria. Elimination and Dispersal: Simulates bacterial death or dispersion due to environmental changes, enhancing population diversity to avoid local optima. Code implementation involves random selection and repositioning of a portion of population using probability thresholds. Termination Condition Check: The algorithm typically terminates based on either maximum iteration count or convergence of fitness values. Implementation includes while/for loops with break conditions monitoring solution improvement. MATLAB's matrix operations and built-in functions are particularly suitable for simulating swarm behaviors in BFOA. Vectorized computations improve efficiency, while built-in optimization functions simplify fitness evaluation. The algorithm finds wide applications in function optimization, engineering design, and machine learning domains. Key MATLAB functions utilized include array operations for parallel computation, plotting functions for visualization, and statistical functions for performance analysis.