The Bacterial Forage Optimization Algorithm belongs to the Field of Bacterial Optimization and Swarm Optimization Algorithms, and more broadly to the fields of Computational Intelligence and Metaheuristics. It is related to other Bacterial Optimization Algorithms such as the Bacterial Chemotaxis Algorithm [Muller2002], and other Swarm Intelligence algorithms such as Ant Colony Optimization and Particle Swarm Optimization. There have been many extensions of the approach that attempt to hybridize the algorithm with other Computational Intelligence algorithms and Metaheuristics such as Particle Swarm Optimization, Genetic Algorithm and Tabu Search.
The bacterial forage optimization algorithm (BFOA) has been widely accepted as a global optimization algorithm of current interest for distributed optimization and control. BFOA is inspired by the social foraging behavior of Escherichia coli. BFOA has already caught the attention of researchers because of its efficiency in solving real-world optimization problems that arise in various application domains. The underlying biology behind the E. coli forage strategy is emulated in an extraordinary way and is used as a simple optimization algorithm. It then analyzes the dynamics of the simulated chemotaxis stage in BFOA with the help of a simple mathematical model. Taking a signal from the analysis, it presents a new adaptive variant of BFOA, where the size of the chemotactic step is adjusted in the stroke according to the current suitability of a virtual bacterium.
The bacterial foraging optimization algorithm (BFOA) has been widely accepted as a current optimization algorithm of current interest for optimization and control. BFOA is inspired by the social foraging behavior of Escherichia coli. BFOA has already caught the attention of researchers because of its efficiency in solving real-world optimization problems that arise in various application domains. In the present work, a detailed explanation of this algorithm is given.