A bacterial foraging global optimization algorithm based on the particle swarm optimization

In this paper, a new hybrid algorithm is introduced to improve the efficiency, accuracy and overcome the drawbacks of weak ability to perceive the environment and vulnerable to perception of local extreme in the optimization process of bacterial foraging optimization (BFO) algorithm. In the new algorithm, the idea of particle swarm optimization (PSO) is merged into the chemotaxis of bacterial foraging optimization algorithms and elimination probability is proposed in elimination-dispersion according to the energy of bacteria. In order to compare the performance of this new hybrid algorithm with BFO and PSO, some typical high dimensional complex functions was proposed to test these three bionic algorithms. The results show that the new algorithm has a better searching speed an obvious improvement in accuracy. This algorithm is suitable to solve the complex functions optimization.