# Introducing languid particle dynamics to a selection of PSO variants

## TL;DR

This paper introduces languid particle dynamics (LPD) to enhance five popular PSO variants, demonstrating statistically significant accuracy improvements across various benchmark functions and dimensions.

## Contribution

The study applies LPD to multiple PSO variants and provides comprehensive empirical evidence of its effectiveness in improving optimization accuracy.

## Key findings

- LPD improves accuracy in 4 out of 5 PSO variants.
- Statistically significant improvements observed in 13-50% of test functions.
- Enhanced PSO variants outperform original versions across multiple dimensions.

## Abstract

Previous research showed that conditioning a PSO agent's movement based on its personal fitness improvement enhances the standard PSO method. In this article, languid particle dynamics (LPD) technique is used on five adequate and widely used PSO variants. Five unmodified PSO variants were tested against their LPD-implemented counterparts on three search space dimensionalities (10, 20, and 50 dimensions) and 30 test functions of the CEC 2014 benchmark test. In the preliminary phase of the testing four of the five tested PSO variants showed improvement in accuracy. The worst and best-achieving variants from preliminary test went through detailed investigation on 220 and 770 combinations of method parameters, where both variants showed overall gains in accuracy when enhanced with LPD. Finally, the results obtained with best achieving PSO parameters were subject to statistical analysis which showed that the two variants give statistically significant improvements in accuracy for 13-50% of the test functions.