KohonAnts: A Self-Organizing Ant Algorithm for Clustering and Pattern Classification

TL;DR

KohonAnts is a novel ant-inspired algorithm that leverages self-organization principles for effective clustering and pattern recognition, simplifying parameter tuning and outperforming existing methods on benchmark datasets.

Contribution

It introduces a new ant-based clustering method inspired by self-organizing maps, with fewer parameters and improved simplicity and performance.

Findings

Achieved very good results on benchmark problems

Simpler algorithm with fewer parameters

Effective self-organizing clustering and pattern recognition

Abstract

In this paper we introduce a new ant-based method that takes advantage of the cooperative self-organization of Ant Colony Systems to create a naturally inspired clustering and pattern recognition method. The approach considers each data item as an ant, which moves inside a grid changing the cells it goes through, in a fashion similar to Kohonen's Self-Organizing Maps. The resulting algorithm is conceptually more simple, takes less free parameters than other ant-based clustering algorithms, and, after some parameter tuning, yields very good results on some benchmark problems.