Bacterial Foraging Optimization (BFO) Algorithm

The Bacterial Foraging Optimization (BFO) algorithm is a swarm intelligence optimization method inspired by biological behavior, whose core concept involves simulating Escherichia coli's foraging behavior in the intestinal tract to solve mathematical optimization problems. The algorithm's innovation lies in transforming microbial chemotaxis, reproduction, and dispersion mechanisms into computable mathematical models.

In function optimization tasks, BFO achieves global search through cooperation and competition within bacterial populations. Each bacterium represents a potential solution, with iterative optimization performed through the following biological behavior simulations: Chemotaxis operation - Bacteria move toward directions with higher nutrient concentrations (fitness values), corresponding to local refined search of solutions Reproduction operation - Eliminating low-fitness individuals while replicating high-fitness ones to preserve quality solutions Dispersion operation - Randomly reinitializing some individuals with a certain probability to avoid local optima

The algorithm's prominent advantage lies in its adaptive balancing mechanism: chemotaxis step size can be dynamically adjusted according to search phases, reproduction thresholds maintain population diversity, while dispersion probabilities control the balance between exploration and exploitation. In practical applications, simply replacing the objective function allows adaptation to various optimization scenarios such as engineering parameter tuning and neural network training. It's important to note that hyperparameters like population size and chemotaxis step significantly impact algorithm performance, typically requiring parameter tuning based on problem characteristics. Code implementation typically involves defining bacterial position updates using vector operations, fitness evaluation functions, and probabilistic dispersion conditions to achieve these biological behavior simulations.