Bacterial Foraging Optimization Algorithm (BFOA) Numerical Example Simulation with Code Implementation

The Bacterial Foraging Optimization Algorithm (BFOA) is a swarm intelligence-based optimization method inspired by the foraging behavior of E. coli bacteria in the human intestinal tract. This algorithm solves complex optimization problems by simulating three key bacterial processes: chemotaxis (movement toward nutrients), reproduction, and elimination-dispersal. It is particularly suitable for engineering applications such as controller parameter optimization. In numerical simulation implementations, the algorithm typically constructs three critical phases: Chemotaxis operation simulates bacterial random walk and tumbling/swimming motions in the solution space, ensuring local search capability through directional movement calculations. Reproduction operation preserves high-fitness individuals while eliminating inefficient solutions using selection mechanisms. Elimination-dispersal operation probabilistically reinitializes portions of the population to maintain diversity through random regeneration. For controller parameter optimization scenarios, each bacterium represents a set of controller parameters to be optimized. The algorithm iteratively evaluates control system performance metrics (such as overshoot, settling time, etc.) through objective function calculations, continuously adjusting parameters until design requirements are met. Compared to traditional trial-and-error methods, BFOA efficiently locates near-optimal solutions in high-dimensional parameter spaces using population-based search strategies. In numerical simulations, special attention must be paid to step size parameter configuration. Excessive chemotaxis step sizes may cause oscillations, while insufficient step sizes hinder convergence speed. Typical improvement schemes include adaptive step size strategies with dynamic adjustment mechanisms and hybrid optimization frameworks that combine BFOA with other intelligent algorithms like Particle Swarm Optimization (PSO) to enhance global search capabilities through complementary optimization techniques.