AI-Driven Optimization Techniques for Advanced Computational Systems

Barry Smyth

AI-Driven Optimization Techniques for Advanced Computational Systems

Author(s)	Barry Smyth
Country	Ireland
Abstract	The increasing complexity of modern computational systems has created a strong demand for intelligent optimization techniques capable of improving computational efficiency, resource utilization, scheduling, scalability, and decision-making. Conventional optimization approaches can become computationally expensive when systems involve large search spaces, nonlinear relationships, dynamic workloads, and multiple competing objectives. Artificial intelligence provides new mechanisms for addressing these challenges through machine learning, reinforcement learning, evolutionary computation, surrogate modeling, and hybrid optimization frameworks. This study examines AI-driven optimization techniques for advanced computational systems, focusing on adaptive optimization, intelligent scheduling, resource allocation, algorithm selection, and performance enhancement. A qualitative descriptive research design was adopted through structured literature analysis and conceptual synthesis of relevant scholarly research. Recent research demonstrates that AI can contribute across several stages of the optimization process, including parameter generation, model formulation, solution generation, algorithm selection, and solution interpretation. Reinforcement learning-assisted evolutionary algorithms have also emerged as an important direction for improving search strategies, operator selection, parameter adaptation, and solution generation. The findings indicate that AI-driven optimization can improve computational efficiency, adaptability, scalability, and decision quality when appropriately integrated with optimization methods. However, challenges involving computational overhead, generalization, explainability, data requirements, convergence reliability, and multi-objective trade-offs remain important. The study concludes that hybrid AI-optimization frameworks provide a promising pathway toward intelligent, adaptive, and efficient computational systems.
Keywords	Artificial Intelligence, Optimization, Machine Learning, Reinforcement Learning, Computational Systems, Evolutionary Algorithms, Intelligent Optimization, Resource Management, Adaptive Computing
Field	Engineering
Published In	Volume 1, Issue 3, May-June 2019
Published On	2019-05-31

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American International Journal of Advanced Research and Development

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AI-Driven Optimization Techniques for Advanced Computational Systems

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