A Fast Bacterial Swarming Algorithm for high-dimensional function optimization

A novel Fast Bacterial Swarming Algorithm (FBSA) for high-dimensional function optimization is presented in this paper. The proposed algorithm combines the foraging mechanism of E-coli bacterium introduced in Bacterial Foraging Algorithm (BFA) with the swarming pattern of birds in block introduced in Particle Swarm Optimization (PSO). It incorporates the merits of the two bio-inspired algorithms to improve the convergence for high-dimensional function optimization. A new parameter called attraction factor is introduced to adjust the bacterial trajectory according to the location of the best bacterium (bacterium with best fitness value). An adaptive step length is adopted to improve the local search ability. The algorithm has been evaluated on standard high-dimensional benchmark functions in comparison with BFA and PSO respectively. The simulation results have demonstrated the fast convergence ability and the improved optimization accuracy of FBSA.