A novel swarm intelligence optimization inspired by evolution process of a bacterial colony

Traditional swarm intelligence algorithms lack of evolution ability and are easy to fall into premature convergence. Therefore, a new kind of swarm intelligence algorithm, called bacterial colony optimization (BCO) algorithm, was proposed in this paper. The solution space of the problem was considered as a certain culture medium. A single bacterium or a few bacteria were placed randomly in the space. The BCO algorithm was designed through simulating the evolution process of the bacterial colony. The BCO itself has a certain evolutionary mechanism and could be terminated naturally, which had given a new termination criterion for swarm intelligence algorithms. A series of simulation experiments on three test functions were used to verify the effectiveness of the BCO algorithm. The simulation results showed that the BCO algorithm can converge to the global optimization solution.