Artificial Intelligence-Based Optimization Models for Modern Engineering Applications

Felipe Gonzalez

Artificial Intelligence-Based Optimization Models for Modern Engineering Applications

Author(s)	Felipe Gonzalez
Country	Australia
Abstract	Artificial intelligence (AI) has increasingly become an important computational paradigm for solving complex optimization problems encountered in modern engineering systems. Conventional optimization approaches often face limitations when engineering problems involve nonlinear relationships, multiple constraints, high-dimensional variables, uncertainty, dynamic operating conditions, and competing objectives. AI-based optimization models provide an alternative by integrating machine learning, deep learning, evolutionary computation, swarm intelligence, surrogate modeling, reinforcement learning, and hybrid computational strategies. These approaches can support engineering decision-making by learning relationships from data, identifying promising solution spaces, predicting system responses, and continuously improving optimization outcomes. This paper examines the role of AI-based optimization models in modern engineering applications, with particular attention to engineering design, manufacturing, energy systems, infrastructure, process optimization, and intelligent maintenance. The study follows a structured review-based methodology to examine established AI optimization approaches, their application characteristics, performance considerations, and implementation challenges. The analysis indicates that hybrid AI optimization models can provide substantial advantages when conventional mathematical formulations are computationally expensive or unable to represent complex system behavior adequately. Surrogate-assisted optimization can reduce repeated simulation costs, while evolutionary and swarm-based methods can effectively address nonlinear and multi-objective engineering problems. Reinforcement learning additionally provides opportunities for adaptive optimization in dynamic environments. However, challenges related to data quality, computational requirements, model interpretability, generalization, constraint handling, validation, and engineering safety remain important. The paper therefore emphasizes the need for integrated physics-informed, explainable, robust, and domain-specific AI optimization frameworks. The findings suggest that future engineering optimization will increasingly depend on the integration of AI models with established engineering knowledge rather than replacing conventional engineering principles.
Keywords	Artificial Intelligence, Engineering Optimization, Machine Learning, Deep Learning, Metaheuristic Optimization, Engineering Design, Surrogate Modeling, Intelligent Systems.
Field	Engineering
Published In	Volume 2, Issue 3, March 2025
Published On	2025-03-17

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Artificial Intelligence-Based Optimization Models for Modern Engineering Applications

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