A High-Accuracy Parameter Estimation PSO Algorithm

A preferable value for parameters proved to be crucial in enhancing the performance and efficiency of particle swarm optimization (PSO) algorithm. To provide good solution for reasonable choice of parameter values within fairly wide range for particle swarm optimization, this paper presents a novel parameter optimizing configuration strategy based on multi-order rhombus thought (MRT), which depends on the optimization function to adaptively configure the most suitable set of parameters. With the divergent-concentrate-redivigent-reconcentrate nature of MRT, parameters are gradually optimized by the rhombus thought process as feedback information of the evolutionary process. Compared with other main improved methods, the computation procedures of MRTPSO algorithm are discussed, and numerical experiments based on typical benchmarks are given to illustrate the better convergence characteristic and shorter executing time of MRTPSO algorithm.