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

A new particle swarm optimization algorithm based on the Bayesian techniques(BPSO) is proposed. Fig. 1 is the comparisons between different inertia weight strategies for f5 on 10 dimensions. Fig. 2 is comparisons between different PSO methods for f5 on 10 dimensions. Parameter s is the interval of the adjacent two inertia weight change in all iterations. As shown in Fig. 3, different values of s affect the convergence rate in the test function. Fig. 4 is the change of ω in the iterations.