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

TL;DR

This paper introduces PSO-TPME, a particle swarm optimization variant that significantly enhances convergence speed and global search ability through targeted elitism and mutation strategies, outperforming existing methods in accuracy and efficiency.

Contribution

The paper presents a novel PSO variant with targeted, position-mutated elitism that improves convergence speed and global exploration, validated through extensive benchmarking.

Findings

Outperforms five popular PSO variants in convergence speed.

Achieves higher accuracy in finding global minima.

Demonstrates robustness across multi-dimensional functions.

Abstract

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.