A Fast-Converging Particle Swarm Optimization through Targeted, Position-Mutated, Elitism (PSO-TPME)

Abstract We improve convergence speed by two orders of magnitude and the global exploration capabilities particle swarm optimization (PSO) through targeted position-mutated elitism (TPME). The proposed fast-converging TPME operator requires a fitness-based classification technique to categorize particles. introduced is motivated its simplicity, low memory requirements, automated termination criteria based on convergence. three key innovations address classification, elitism, mutation in cognitive social model. PSO-TPME benchmarked against five popular PSO variants for multi-dimensional functions, which are extensively adopted field, In particular, accuracy, speed, capability find minima investigated. statistical error assessed numerous repetitions. simulations confirmed that ten thirteen investigated variant outperforms other terms rate accuracy at least magnitude. On hand, demonstrated early all tested functions. first iterations, outperformed