Ant Colony Optimization Algorithm for Frontend Resource Prioritization

Keywords:
Ant Colony Optimization, Frontend Performance Optimization, Resource Loading, Swarm Intelligence

Abstract:
Optimizing frontend resource loading is crucial for enhancing web performance, as it directly affects user experience and application responsiveness. Traditional optimization techniques, such as lazy loading and dependency management, often fail to address the combinatorial complexity of resource sequencing in dynamic web environments. This paper presents a novel approach using ant colony optimization (ACO) to optimize frontend resource loading. By modeling the problem as a combinatorial optimization task, we developed a dynamic solution that considers resource dependencies, priorities, and load times. Our Python implementation demonstrates the effectiveness of ACO, achieving a 4% reduction in load time compared to the particle swarm optimization (PSO) algorithm, a 12% reduction in load time compared to the Greedy Algorithm and a 23% reduction compared to Random Loading. The algorithm consistently converges to high-quality solutions, highlighting its potential for improving web performance in complex applications. Key contributions include an ACO-based model, empirical validation, and practical insights for frontend optimization. This work underscores the value of ACO as a robust and adaptive tool for enhancing frontend performance and user experience.

CITATION:

IEEE format

Copy to clipboard

D. Bulaja, K. Stojiljković, M. Živković, N. Bačanin Džakula, T. Živković, “Ant Colony Optimization Algorithm for Frontend Resource Prioritization,” in Sinteza 2025 - International Scientific Conference on Information Technology, Computer Science, and Data Science, Belgrade, Singidunum University, Serbia, 2025, pp. 16-22. doi:10.15308/Sinteza-2025-16-22

APA format

Copy to clipboard

Bulaja, D., Stojiljković, K., Živković, M., Bačanin Džakula, N., Živković, T. (2025). Ant Colony Optimization Algorithm for Frontend Resource Prioritization. Paper presented at Sinteza 2025 - International Scientific Conference on Information Technology, Computer Science, and Data Science. doi:10.15308/Sinteza-2025-16-22

BibTeX format
Download

RefWorks Tagged format
Download