A new particle swarm optimization algorithm with adaptive inertia weight based on Bayesian techniques

Particle swarm optimization is a stochastic population-based algorithm based on social interaction of bird flocking or fish schooling. In this paper, a new adaptive inertia weight adjusting approach is proposed based on Bayesian techniques in PSO, which is used to set up a sound tradeoff between the exploration and exploitation characteristics. It applies the Bayesian techniques to enhance the PSO's searching ability in the exploitation of past particle positions and uses the cauchy mutation for exploring the better solution. A suite of benchmark functions are employed to test the performance of the proposed method. The results demonstrate that the new method exhibits higher accuracy and faster convergence rate than other inertia weight adjusting methods in multimodal and unimodal functions. Furthermore, to show the generalization ability of BPSO method, it is compared with other types of improved PSO algorithms, which also performs well.