PERFORMANCE ANALYSIS OF MULTI-SWARM AI-BASED PSO, DE, AND K-MEANS FOR OPTIMIZATION TASKS Article Swipe

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Sitanath Biswas , Vipul Kumar , Ananth Ravi , Divya Kumari Tankala , Bhupchand Kumhar , Sayan Chakraborty , Santanu Kumar Sahoo ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.61586/ztphv

This study evaluates a multi-swarm AI framework that embeds K-Means within Particle Swarm Optimization (PSO) and Differential Evolution (DE) to overcome classic clustering weaknesses. Across benchmark datasets, DE delivers the best balance of accuracy and speed: Silhouette = 0.67, Davies-Bouldin = 0.69, MSE = 0.045, RMSE = 0.212, and quicker convergence than PSO. PSO improves on plain K-Means (Silhouette = 0.61 vs 0.52; DBI = 0.75 vs 0.86; MSE = 0.052 vs 0.078) but converges slower than DE because of velocity-update dependence. K-Means remains the fastest computationally yet lags markedly in clustering quality. Overall, multi-swarm optimization boosts clustering performance but increases computational demands and complicates hyper-parameter tuning. Future work should devise adaptive mechanisms to trim runtime and automate parameter selection to broaden real-world applicability.

Related Topics

Particle Swarm Optimization

Computer Science

Swarm Behaviour

Artificial Intelligence

Machine Learning

Concepts

Particle swarm optimization Computer science Swarm behaviour Artificial intelligence Machine learning

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Type: article
Language: en
Landing Page: https://doi.org/10.61586/ztphv
PDF: https://doi.org/10.61586/ztphv
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DOI: https://doi.org/10.61586/ztphv

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Title: PERFORMANCE ANALYSIS OF MULTI-SWARM AI-BASED PSO, DE, AND K-MEANS FOR OPTIMIZATION TASKS

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Language: en

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Publication year: 2025

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Authors: Sitanath Biswas, Vipul Kumar, Ananth Ravi, Divya Kumari Tankala, Bhupchand Kumhar, Sayan Chakraborty, Santanu Kumar Sahoo

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Landing page: https://doi.org/10.61586/ztphv

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Concepts: Particle swarm optimization, Computer science, Swarm behaviour, Artificial intelligence, Machine learning

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Cited by: 0

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