# Enhancing particle swarm optimization based on optical computing mechanism: application to dyslexia detection

## TL;DR

This paper introduces an improved Particle Swarm Optimization method using optical computing to better detect dyslexia through eye-tracking data.

## Contribution

A novel Particle Swarm Optimization variant using all-optical computation for improved search space exploration and dyslexia detection.

## Key findings

- The optical PSO outperformed traditional PSO on CEC benchmark functions.
- The method improved dyslexia detection accuracy using eye-tracking datasets.

## Abstract

This study presents a modified version of Particle Swarm Optimization (PSO) using an all-optical computational update mechanism. The primary innovation and objective of this collaboration aimed to leverage the inherent properties of coherent optical systems, including specialized complex-domain computation and nonlinear light-matter interactions, to enhance the exploration and exploitation of the search space process for particles.

To assess the performance of the developed model, it was compared with traditional PSO to solve the CEC benchmark functions. Furthermore, it was applied to enhance the detection of dyslexia using the eye-tracking dataset (ETDD).

The comparison between OPSO and other techniques established its high ability to enhance the detection of dyslexia over traditional techniques.

The use of an all-optical computational update mechanism demonstrated enhanced performance in both benchmark optimization problems and dyslexia detection tasks.