Robust Particle Swarm Optimizer based on Chemomimicry

TL;DR

This paper introduces a novel particle swarm optimizer inspired by crystallization processes and chaos theory, enhancing robustness against local minima in complex optimization problems.

Contribution

It presents a new PSO variant with phases modeled after crystallization, incorporating a chaos factor that improves robustness and convergence in multimodal functions.

Findings

More robust to local minima than standard PSO

Effective in problems with experimental precision

Demonstrated improved convergence behavior

Abstract

A particle swarm optimizer (PSO) loosely based on the phenomena of crystallization and a chaos factor which follows the complimentary error function is described. The method features three phases: diffusion, directed motion, and nucleation. During the diffusion phase random walk is the only contributor to particle motion. As the algorithm progresses the contribution from chaos decreases and movement toward global best locations is pursued until convergence has occurred. The algorithm was found to be more robust to local minima in multimodal test functions than a standard PSO algorithm and is designed for problems which feature experimental precision.