American Journal of Advanced Multidisciplinary Research and Innovation

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Advanced Computational Intelligence Models for Engineering Optimisation Problems

Author(s) Keith Molenaar
Country United States
Abstract Engineering optimisation has become increasingly important in addressing complex design, manufacturing, energy, transportation, healthcare, communication, and industrial engineering challenges. Traditional optimisation techniques often struggle with high-dimensional, nonlinear, multi-objective, and uncertain engineering problems due to computational complexity and dynamic constraints. Recent advances in Computational Intelligence (CI), including Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Artificial Neural Networks (ANNs), Fuzzy Logic, Evolutionary Algorithms (EAs), Genetic Algorithms (GAs), Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), Differential Evolution (DE), Reinforcement Learning (RL), Digital Twin Technology, and Big Data Analytics, have significantly enhanced engineering optimisation by providing intelligent, adaptive, and robust solutions for complex decision-making problems.
This study presents a comprehensive analysis of Advanced Computational Intelligence Models for Engineering Optimisation Problems. A qualitative analytical research methodology based on secondary data is employed to investigate AI-driven optimisation models, swarm intelligence, evolutionary computation, intelligent decision support, hybrid optimisation techniques, and multidisciplinary engineering applications. The research evaluates how computational intelligence improves optimisation efficiency, solution quality, computational performance, robustness, and sustainability across engineering domains.
The findings indicate that computational intelligence models substantially improve structural design optimisation, manufacturing scheduling, energy management, transportation planning, robotics, smart infrastructure, and predictive maintenance. Artificial Intelligence supports intelligent decision-making, Machine Learning enables predictive optimisation, Genetic Algorithms solve complex search problems, Particle Swarm Optimisation accelerates convergence, and Deep Learning enhances feature extraction for engineering applications. Furthermore, Digital Twin Technology enables real-time simulation and optimisation of engineering systems, while cloud computing facilitates scalable computational analysis.
Despite these opportunities, challenges remain concerning computational complexity, model interpretability, data quality, convergence stability, cybersecurity, scalability, algorithm selection, and integration with legacy engineering systems. Future research should investigate explainable computational intelligence, quantum-inspired optimisation algorithms, federated engineering intelligence, autonomous optimisation systems, and sustainable engineering design frameworks.
The study concludes that advanced computational intelligence provides a transformative framework for solving engineering optimisation problems by enabling intelligent automation, adaptive decision-making, multidisciplinary optimisation, and sustainable engineering innovation.
Keywords Computational Intelligence, Engineering Optimisation, Artificial Intelligence, Machine Learning, Genetic Algorithms, Particle Swarm Optimisation, Evolutionary Computation, Deep Learning, Intelligent Systems, Digital Twin.
Field Engineering
Published In Volume 2, Issue 3, May-June 2020
Published On 2020-06-25
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