Machine Learning-Based Optimization Models for Complex Systems

Remigiusz Michalski

Machine Learning-Based Optimization Models for Complex Systems

Author(s)	Remigiusz Michalski
Country	Ireland
Abstract	Machine learning-based optimization has emerged as an important approach for addressing complex systems characterized by nonlinear relationships, uncertainty, multiple interacting variables, and dynamic operational conditions. Traditional optimization methods may encounter limitations when system behavior is difficult to model mathematically or when optimization must be performed using large and continuously changing datasets. Machine learning can overcome some of these limitations by learning patterns from historical and real-time information and integrating predictive intelligence with optimization procedures. This study examines machine learning-based optimization models for complex systems, focusing on predictive optimization, adaptive decision-making, resource allocation, intelligent scheduling, and dynamic system management. A qualitative descriptive research design based on secondary scholarly literature was adopted, and thematic analysis was used to identify major patterns related to machine learning-enabled optimization. The findings indicate that machine learning can improve optimization performance by identifying complex relationships, predicting system states, evaluating alternative solutions, and supporting adaptive decision-making. Such models have applications in manufacturing, transportation, energy systems, logistics, healthcare, telecommunications, and intelligent infrastructure. The analysis also demonstrates that hybrid optimization approaches combining machine learning with conventional mathematical and metaheuristic techniques can provide greater flexibility for complex problems. However, challenges remain concerning data quality, computational requirements, model interpretability, generalization, optimization stability, scalability, and real-time implementation. The study emphasizes that optimization performance should be evaluated alongside reliability, computational efficiency, adaptability, and operational feasibility.
Keywords	Machine Learning, Optimization Models, Complex Systems, Predictive Optimization, Intelligent Optimization, Decision Support, Adaptive Systems, Computational Intelligence, Resource Allocation, Artificial Intelligence
Field	Engineering
Published In	Volume 1, Issue 5, September-October 2019
Published On	2019-10-14

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American International Journal of Advanced Research and Development

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Machine Learning-Based Optimization Models for Complex Systems

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