An Adaptive Cat Swarm Optimization Based on Particle Swarm Optimization Approach (ACPSO) for Clustering

Irvan Santoso; Robin Solala Gulo; Abba Suganda Girsang

doi:10.15866/irecos.v11i1.8008

An Adaptive Cat Swarm Optimization Based on Particle Swarm Optimization Approach (ACPSO) for Clustering

Irvan Santoso⁽¹⁾, Robin Solala Gulo⁽²⁾, Abba Suganda Girsang^(3*)

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/irecos.v11i1.8008

Abstract

This paper proposes an adaptive cat swarm optimization based on particle swarm optimization for clustering, called ACPSO. Unlike the cat swam optimization that operates one cat, this algorithm uses some cats as population bases to converge the result. ACPSO employs an adaptive method by choosing a seeking mode or tracing mode based on a mixture ratio (MR). In the tracing mode, this algorithm uses a modified particle swarm optimization to increase diversity solutions. To demonstrate the performance, ACPSO was conducted to solve some datasets clustering problem. The results show that ACPSO has a good performance compared to the other methods.
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Keywords

Cat Swarm Optimization; Data Clustering; CSO; Clustering; PSO

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