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

A fast converging particle swarm optimization through targeted, position-mutated, elitism (PSO-TPME)