Web-Based Comparative Analysis Framework for Metaheuristic Optimization Algorithms: A Unified Educational Approach
8th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, ICHORA 2026, Ankara, Türkiye, 21 - 23 Mayıs 2026, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/ichora69329.2026.11537126
- Basıldığı Şehir: Ankara
- Basıldığı Ülke: Türkiye
- Anahtar Kelimeler: Algorithm Benchmarking, Interactive Visualization, Metaheuristic Optimization
- Kayseri Üniversitesi Adresli: Evet
Özet
The rapid proliferation of metaheuristic optimization algorithms presents a steep learning curve for researchers, often aggravated by the black box nature of algorithmic execution. Existing visualization tools partially address this issue but are frequently limited by desktop-based dependencies or lack realtime comparative capabilities. To bridge this gap, this study introduces a comprehensive, web-based educational framework designed for the real-time visualization and comparative analysis of Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Grey Wolf Optimizer (GWO). Built on a modular React-FastAPI architecture, the platform enables users to observe stochastic search dynamics across both continuous benchmark functions (Sphere, Rastrigin) and discrete real-world engineering problems (Traveling Salesman Problem and Knapsack Problem). Experimental validation, conducted over 20 independent runs, demonstrates the problem-dependent efficacy of these algorithms; quantitative results reveal that GWO achieves superior stability and convergence rates in both unimodal and multimodal environments, while PSO outperforms in combinatorial routing. The platform is intended as an educational testbed that makes stochastic search trajectories more interpretable and supports reproducible baseline comparisons. Future work will include user studies to quantify learning gains and broader scalability tests with additional algorithms and problem instances.