Aiming at the disadvantages of particle swarm optimization algorithm (PSO), which is easy to trap into local optima and converge slowly in later period of iteration, an improved particle swarm optimization algorithm based on social psychology (BSPSO) was proposed. Unlike the standard PSO algorithm, this BSPSO algorithm used asynchronous version of PSO algorithm, and adopted two strategies (divided particles into some growth stages and introduced mutations) to improve the original PSO algorithm. Division of growth stages can make particles have different learning factors at different stages, and mutations can make particles jump out of local optima effectively, so the algorithm performance was improved effectively. The simulation result shows that the BSPSO is more available than those previously proposed PSO algorithms through experiments with several benchmark functions.
The study manipulated the neighborhood topologies of particle swarms optimizing four test functions. Several social network structures were tested, with "small-world" randomization of a specified number of links. Sociometric structure and the small-world manipulation interacted with function to produce a significant effect on performance.
A novel technique using particle swarm optimization (PSO) is proposed for optimizing PI parameters controllers of a wind turbine (WT) based on direct drive permanent magnet synchronous generator (DDPMSG). The PSO algorithm is used to search for the optimal combination of PI controllers parameters. A small signal stability (SSS) mode of the WT system is derived to implement PSO algorithm based on eigenvalues analysis as objective function. Simulations are performed to illustrate this control method validity.