A Parallel PSO Approach for Hyperparameter Optimization in Machine Learning Models

Year 2025, Volume: 14 Issue: 4, 112 - 120, 30.12.2025

Cebrail Barut , Harun Bingöl

https://doi.org/10.46810/tdfd.1763151

Abstract

Hyperparameter tuning is crucial for improving the performance of machine learning models, especially in high-dimensional and complex parameter spaces where traditional methods (Grid Search(GS) and Random Search (RS) fall short. This work introduces a parallelized Particle Swarm Optimization(P-PSO) approach for hyperparameter optimization, which is evaluated on three benchmark datasets (Iris, Breast Cancer, Red Wine Quality) across three models (Logistic Regression (LR), Random Forest (RF), and Support Vector Classifier (SVC)). Experimental results show that P-PSO achieves superior weighted F1-scores in most cases; for example, it reaches 0.96 on the Iris dataset across all models, 0.88 for RF on Breast Cancer, and 0.69 for RF on the particularly challenging Red Wine Quality dataset, outperforming other optimization techniques by margins of up to 0.02-0.05. Despite longer execution times, especially on complex models (up to 43 seconds for RF on Red Wine Quality), P-PSO offers more consistency and higher accuracy. These results confirm that P-PSO is an effective, scalable, and robust alternative for hyperparameter tuning, especially in cases where maximizing model performance rather than computational cost is prioritized.

Keywords

Hyperparameter optimization , Parallel particle swarm optimization , Optimization

Makine Öğrenimi Modellerinde Hiperparametre Optimizasyonu için Paralel PSO Yaklaşımı

Abstract

Hiperparametre ayarı, özellikle geleneksel yöntemlerin (Grid Search(GS) ve Random Search(RS)) yetersiz kaldığı yüksek boyutlu ve karmaşık parametre alanlarında, makine öğrenimi modellerinin performansını artırmak için çok önemlidir. Bu çalışma, üç modelde (Lojistik Regresyon (LR), Random Forest (RF) ve Destek Vektör Sınıflandırıcısı (SVM) ) üç veri kümesi (Iris, Meme Kanseri (Breast Cancer), Kırmızı Şarap Kalitesi (Red Wine Quality)) üzerinde değerlendirilen hiperparametre optimizasyonu için paralelleştirilmiş Parçacık Sürüsü Optimizasyonu (P-PSO) yaklaşımını tanıtmaktadır. Deneysel sonuçlar, P-PSO'nun çoğu durumda üstün ağırlıklı F1 puanları elde ettiğini göstermektedir; örneğin, tüm modellerde Iris veri kümesinde 0,96'ya, Breast Cancer'da RF için 0,88'e ve özellikle zorlu Red Wine Quality veri kümesinde RF için 0,69'a ulaşarak diğer optimizasyon tekniklerini 0,02-0,05'e varan marjlarla geride bırakmaktadır. Özellikle karmaşık modellerde daha uzun yürütme sürelerine rağmen, P-PSO daha tutarlı ve daha yüksek doğruluk sunmaktadır. Bu sonuçlar, P-PSO'nun, özellikle hesaplama maliyetinden ziyade model performansının en üst düzeye çıkarılmasının önceliklendirildiği durumlarda, hiperparametre ayarlaması için etkili, ölçeklenebilir ve sağlam bir alternatif olduğunu doğrulamaktadır.

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