Performance Analysis of Metaheuristic Algorithms for Solving Data Clustering Problems
Saurav B. Chandra, Sumit Kumar, Yugal Kumar · Procedia Computer Science · 2025
Metaheuristic algorithms are powerful techniques that are widely adopted by research community to compute the optimal solutions for complex and diverse optimization problems. Meta-heuristic algorithms provide more flexibility compared to traditional clustering algorithm and more suitable to handle high-dimensional, noisy, or irregular datasets. This work investigates efficacy of the several state of art metaheuristic algorithms like K-Means, Particle swarm optimization (PSO), Ant colony optimization (ACO), Cat swarm optimization (CSO), Teaching-learning-based optimization (TLBO), Bat algorithm (BA) and Chemical reaction algorithm (CRO) for handling clustering problems. These algorithms efficiency is evaluated using intra cluster distance and f-measure parameters. For experimental analysis, Iris, Cancer, CMC, Wine, and Glass datasets are considered and findings stated that a single meta-heuristic algorithm cannot outperform with all datasets. It is also found that exploration and exploitation trade-off have significant impact on the performance of the meta-heuristic algorithms. The tuning of the user-defined parameters also affects the performance of the meta-heuristic algorithm.