American Journal of Advanced Multidisciplinary Research and Innovation

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Advanced Computational Intelligence Models for Engineering Optimisation Problems

Author(s) Michael E. Porter
Country United States
Abstract Engineering optimisation has become increasingly important in solving complex real-world problems involving multiple objectives, nonlinear constraints, high-dimensional datasets, and uncertain environments. Traditional optimisation techniques often struggle with dynamic, large-scale, and computationally intensive engineering problems. Consequently, Computational Intelligence (CI) has emerged as a powerful paradigm that integrates Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Artificial Neural Networks (ANNs), Fuzzy Logic, Evolutionary Computing, Swarm Intelligence, Genetic Algorithms (GA), Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), Differential Evolution (DE), Reinforcement Learning (RL), and hybrid metaheuristic algorithms to achieve efficient and robust optimisation. These intelligent techniques have demonstrated remarkable success across engineering disciplines, including mechanical, civil, electrical, manufacturing, aerospace, transportation, energy, biomedical, and industrial engineering.
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 intelligent optimisation algorithms, hybrid computational models, data-driven engineering design, predictive optimisation, digital twin-assisted optimisation, and autonomous engineering decision support systems. The research evaluates how computational intelligence improves optimisation accuracy, computational efficiency, design reliability, resource utilisation, sustainability, and engineering innovation.
The findings indicate that computational intelligence models significantly outperform conventional optimisation approaches in solving nonlinear, multi-objective, stochastic, and constrained optimisation problems. Artificial Neural Networks improve predictive modelling, evolutionary algorithms provide global search capabilities, swarm intelligence enhances adaptive optimisation, and reinforcement learning enables autonomous decision-making. Furthermore, integrating Big Data Analytics, Cloud Computing, Edge Computing, Internet of Things (IoT), Digital Twin Technology, and High-Performance Computing (HPC) creates intelligent engineering ecosystems capable of continuous optimisation and real-time operational adaptation.
Despite these opportunities, challenges remain regarding computational complexity, convergence reliability, scalability, interpretability, parameter tuning, cybersecurity, and integration with legacy engineering systems. Future research should investigate explainable computational intelligence, federated optimisation, quantum-inspired algorithms, neuromorphic computing, and autonomous self-optimising engineering systems.
The study concludes that advanced computational intelligence models provide a multidisciplinary framework for developing intelligent, adaptive, efficient, and sustainable engineering optimisation systems capable of supporting next-generation industrial innovation and global technological advancement.
Keywords Computational Intelligence, Engineering Optimisation, Artificial Intelligence, Artificial Neural Networks, Evolutionary Algorithms, Swarm Intelligence, Machine Learning, Digital Twin, Metaheuristic Optimisation, Intelligent Engineering.
Field Engineering
Published In Volume 2, Issue 2, March-April 2020
Published On 2020-03-12
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