  We dramatically improve convergence speed and global exploration capabilities
of particle swarm optimization (PSO) through a targeted position-mutated
elitism (PSO-TPME). The three key innovations address particle classification,
elitism, and mutation in the cognitive and social model. PSO-TPME is
benchmarked against five popular PSO variants for multi-dimensional functions,
which are extensively adopted in the optimization field, In particular, the
convergence accuracy, convergence speed, and the capability to find global
minima is investigated. The statistical error is assessed by numerous
repetitions. The simulations demonstrate that proposed PSO variant outperforms
the other variants in terms of convergence rate and accuracy by orders of
magnitude.
