Swarm intelligence-based clustering algorithms: A survey

Abstract

Swarm intelligence (SI) is an artificial intelligence technique that depends on the collective properties emerging from multi-agents in a swarm. In this work, the SI-based algorithms for hard (crisp) clustering are reviewed. They are studied in five groups: particle swarm optimization, ant colony optimization, ant-based sorting, hybrid algorithms, and other SI-based algorithms. Agents are the key elements of the SI-based algorithms, as they determine how the solutions are generated and directly affect the exploration and exploitation capabilities of the search procedure. Hence, a new classification scheme is proposed for the SI-based clustering algorithms according to the agent representation. We elaborate on which representation schemes are used in different algorithm categories. We also examine how the SI-based algorithms, together with the representation schemes, address the challenging characteristics of the clustering problem such as multiple objectives, unknown number of clusters, arbitrary-shaped clusters, data types, constraints, and scalability. The pros and cons of each representation scheme are discussed. Finally, future research directions are suggested.