LDWMeanPSO: A new improved particle swarm optimization technique

Different optimization functions are used to develop the particle swarm optimization (PSO) technique, based on natural and physical phenomena. The presented techniques range from Standard PSO, Linearly Decreasing Weight PSO, Center PSO, Mean PSO and many others. In this paper, a new hybrid particle swarm optimization technique, called Linearly Decreasing Weight Mean PSO, is presented, based on the philosophy of mixing the effect of linearly decreasing weight with the linear combination of the two original terms in the velocity formula. The performance of the LDWMeanPSO is evaluated and compared with the performance of standard PSO, LDWPSO, CenterPSO and MeanPSO, using a number of scalable and multimodal test functions. The experimental results show that the proposed technique outperforms the other compared algorithms.