Self-adaptation in Bacterial Foraging Optimization algorithm

Bacterial Foraging Optimization (BFO) is a recently developed nature-inspired optimization algorithm, which is based on the foraging behavior of E. coli bacteria. However, BFO possesses a poor convergence behavior over complex optimization problems as compared to other nature-inspired optimization techniques like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). This paper first analyzes how the run-length unit parameter controls the exploration and exploitation ability of BFO, and then presents a variation on the original BFO algorithm, called the Self-adaptive Bacterial Foraging Optimization (SA-BFO), employing the adaptive search strategy to significantly improve the performance of the original algorithm. This is achieved by enabling SA-BFO to adjust the run-length unit parameter dynamically during evolution to balance the exploration/exploitation tradeoff. Application of SA-BFO on several benchmark functions shows a marked improvement in performance over the original BFO.