Introducing languid particle dynamics to a selection of PSO variants

by   Siniša Družeta, et al.
University in Rijeka

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


Anakatabatic Inertia: Particle-wise Adaptive Inertia for PSO

Throughout the course of the development of Particle Swarm Optimization,...

An improved multimodal PSO method based on electrostatic interaction using n- nearest-neighbor local search

In this paper, an improved multimodal optimization (MMO) algorithm,calle...

A Cooperative Framework for Fireworks Algorithm

This paper presents a cooperative framework for fireworks algorithm (CoF...

Image Contrast Enhancement using Fuzzy Technique with Parameter Determination using Metaheuristics

In this work, we have presented a way to increase the contrast of an ima...

Detection of statistically significant differences between process variants through declarative rules

Services and products are often offered via the execution of processes t...

Szenario-Optimierung für die Absicherung von automatisierten und autonomen Fahrsystemen

The verification and validation of automated and autonomous driving syst...

Please sign up or login with your details

Forgot password? Click here to reset