Hybrid Bacterial Foraging with parameter free PSO

This paper presents fusion of Bacterial Foraging with parameter free Particle Swarm Optimization (HBF-pfPSO). The proposed technique is used to enhance quality of global optima of multimodal functions. The authors propose two major modifications in Bacterial Foraging Optimization (BFO). Firstly, all bacteria position and direction are updated after all fitness evaluations instead of each fitness evaluation in chemotaxis step. In order to accelerate the global performance of BFO, the bacteria update their current positions by pfPSO called as mutation. Due to pfPSO, the proposed technique does not require any additional parameter and velocity equation for fine-tuning as bacteria positions are updated directly by local and global best positions. The experimental results on three bencmark functions validadte claims. The proposed technique attains good quality of optima as compared to other techniques on mutimodal functions while showing faster convergence.