Adaptive Multi-Objective Optimization in Large-Scale Artificial Intelligence Systems
DOI:
https://doi.org/10.66280/ijair.v1i1.11Keywords:
Multi-objective optimization; Large-scale AI systems; System architecture; Adaptive optimization; AI governance; Fairness; Energy efficiencyAbstract
Large-scale artificial intelligence systems operate within environments characterized by competing operational, ethical, computational, and regulatory demands. While traditional machine learning optimization has primarily focused on predictive accuracy, contemporary AI infrastructures must balance additional objectives including fairness, robustness, latency, energy consumption, scalability, and governance compliance. The coexistence of these objectives introduces structural trade-offs that cannot be resolved through static or single-dimensional optimization strategies. This paper presents a comprehensive conceptual and architectural framework for adaptive multi-objective optimization in large-scale AI systems. The discussion emphasizes systemic integration, real-time adaptive control, deployment-aware trade-off management, and infrastructure-level governance mechanisms. By analyzing multi-layer interactions across training, deployment, and monitoring stages, the study proposes a unified perspective for managing dynamic objective conflicts in production-scale AI ecosystems.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
