Anakatabatic Inertia: Particle-wise Adaptive Inertia for PSO

TL;DR

This paper introduces an adaptive inertia technique for Particle Swarm Optimization that adjusts particle inertia based on individual fitness changes, leading to improved optimization accuracy.

Contribution

It proposes a novel anakatabatic inertia model that adapts particle inertia based on fitness trends, enhancing PSO performance.

Findings

Moderate accuracy improvements for Standard PSO.

Significant accuracy improvements for TVAC-PSO.

No adverse effects observed on method performance.

Abstract

Throughout the course of the development of Particle Swarm Optimization, particle inertia has been established as an important aspect of the method for researching possible method improvements. As a continuation of our previous research, we propose a novel generalized technique of inertia weight adaptation based on individual particle's fitness improvement, called anakatabatic inertia. This technique allows for adapting inertia weight value for each particle corresponding to the particle's increasing or decreasing fitness, i.e. conditioned by particle's ascending (anabatic) or descending (katabatic) movement. The proposed inertia weight control framework was metaoptimized and tested on the 30 test functions of the CEC 2014 test suite. The conducted procedure produced four anakatabatic models, two for each of the PSO methods used (Standard PSO and TVAC-PSO). The benchmark testing results show that using the proposed anakatabatic inertia models reliably yield moderate improvements in accuracy of Standard PSO (final fitness minimum reduced up to 0.09 orders of magnitude) and rather strong improvements for TVAC-PSO (final fitness minimum reduced up to 0.59 orders of magnitude), mostly without any adverse effects on the method's performance.