Study of adaptive chaos embedded particle swarm optimization algorithm based on Skew Tent map

The existing chaos optimization algorithms were almost based on Logistic map. However, the probability density function of chaotic sequences for Logistic map is a Chebyshev type function, which may affect the global searching capacity and computational efficiency of chaos optimization algorithm. In this paper, firstly, a new chaotic sequences with Skew Tent map (STM) is established, and is improved by its iterative optimization property. Then, the Skew Tent map (STM) is introduced to perform the chaotic search. An adaptive chaos embedded particle swarm optimization algorithm combined with STM (STMACPSO) is proposed subsequently. The convergence speed and global optimal value of the presented algorithm are thus improved. Finally, The experiments with complex and Multi-dimensional functions demonstrate that STMACPSO outperforms the original CPSO in the global searching ability and convergence rate.