Particle swarm optimization incorporating simplex search and center particle for global optimization

This paper proposes a hybrid approach incorporating an enhanced Nelder-Mead simplex search scheme into a particle swarm optimization (PSO) with the use of a center particle in a swarm for effectively solving multi-dimensional optimization problems. Because of the strength of PSO in performing exploration search and NM simplex search in exploitation search, in addition to the help of a center particle residing closest to the optimum during the optimization process, both convergence rate and accuracy of the proposed optimization algorithm can be significantly improved. To show the effectiveness of the proposed approach, 18 benchmark functions will be adopted for optimization via the proposed approach in comparison to existing methods.