Adaptive Bacterial Foraging Optimization

摘要: Bacterial Foraging Optimization (BFO) is a recently developed nature-inspired optimization algorithm, which based on the foraging behavior of E. coli bacteria. Up to now, BFO has been applied successfully some engineering problems due to its simplicity and ease implementation. However, BFO possesses poor convergence over complex problems as compared other nature-inspired techniques. This paper first analyzes how run-length unit parameter controls exploration whole search space exploitation promising areas. Then it presents variation original BFO, called adaptive bacterial foraging (ABFO), employing strategies improve performance BFO. improvement achieved by enabling bacterial algorithm adjust parameter dynamically during execution in order balance exploration/exploitation tradeoff. The experiments compare performance two versions ABFO with standard particle swarm (PSO) real-coded genetic (GA) four widely-used benchmark functions. The proposed shows marked the appears be comparable PSO GA.