Engineering design often requires the optimisation of multiple objectives, and becomes significantly more difficult and time consuming when the response surfaces are multimodal, rather than unimodal. A surrogate model, also known as a metamodel, can be used to replace expensive computer simulations, accelerating single and multi-objective optimisation and the exploration of new design concepts. The main research focus of this work is to investigate the use of a neural network surrogate model to improve optimisation of multimodal surfaces. Several significant contributions derive from evaluating the Cascade Correlation neural network as the basis of a surrogate model. The contributions to the neural network community ultimately outnumber those to the optimisation community. The effects of training this surrogate on multimodal test functions are explored. The Cascade Correlation neural network is shown to map poorly such response surfaces. A hypothesis for this weakness is formulated and tested. A new subdivision technique is created that addresses this problem; however, this new technique requires excessively large datasets upon which to train. The primary conclusion of this work is that Cascade Correlation neural networks form an unreliable basis for a surrogate model, despite successes reported in the literature. A further contribution of this work is the enhancement of an open source optimisation toolkit, achieved by the first integration of a truly multi-objective optimisation algorithm.

Engineering design often requires the optimisation of multiple objectives, and becomes significantly more difficult and time consuming when the response surfaces are multimodal, rather than unimodal. A surrogate model, also known as a metamodel, can be used to replace expensive computer simulations, accelerating single and multi-objective optimisation and the exploration of new design concepts. The main research focus of this work is to investigate the use of a neural network surrogate model to improve optimisation of multimodal surfaces. Several significant contributions derive from evaluating the Cascade Correlation neural network as the basis of a surrogate model. The contributions to the neural network community ultimately outnumber those to the optimisation community. The effects of training this surrogate on multimodal test functions are explored. The Cascade Correlation neural network is shown to map poorly such response surfaces. A hypothesis for this weakness is formulated and tested. A new subdivision technique is created that addresses this problem; however, this new technique requires excessively large datasets upon which to train. The primary conclusion of this work is that Cascade Correlation neural networks form an unreliable basis for a surrogate model, despite successes reported in the literature. A further contribution of this work is the enhancement of an open source optimisation toolkit, achieved by the first integration of a truly multi-objective optimisation algorithm.

This thesis presents and evaluates a new cascading machine learning model framework for anomaly detection, which are essential for modern industrial applications where computing efficiency is crucial. Traditional deep learning algorithms frequently struggle to effectively deploy in edge computing due to the limitations of processing power and memory. This study addresses the challenge by creating a cascading model framework that strategically combines lightweight and more complex models to improve the efficiency of inference while maintaining the accuracy of detection.  We proposed a cascading model framework consisting of a One-Class Support Vector Machine (OCSVM) for rapid initial anomaly detection and a Variational Autoencoder (VAE) for more precise prediction in uncertain cases. The cascading technique between the OCSVM and VAE enables the system to efficiently handle regular data instances, while assigning more complex analyses only when required. This framework was tested in real-world scenarios, including anomaly detection in air pressure system of automotive industry as well as with the MNIST datasets. These tests demonstrate the framework's practical applicability and effectiveness across diverse settings, underscoring its potential for broad implementation in industrial applications.

The rapid evolution of Connected and Autonomous Vehicles (CAVs) has led to a surge in research on efficient anomaly detection methods to ensure their safe and reliable operation. While state-of-the-art deep learning models offer promising results in this domain, their high computational requirements present challenges for deployment in resource-constrained environments, such as Electronic Control Units (ECU) in vehicles. In this context, we consider using the ensemble learning technique specifically the cascaded modeling approach for real-time and resource-efficient multivariate time series anomaly detection in CAVs. The study was done in collaboration with SCANIA, a transport solutions provider. The company is now undergoing a transformation of providing autonomous and sustainable solutions and this work will contribute towards that transformation. Our methodology employs unsupervised learning techniques to construct a cascade of models, comprising a coarse-grained model with lower computational complexity at level one, and a more intricate fine-grained model at level two. Furthermore, we incorporate cascaded model training to refine the complex model's ability to make decisions on uncertain and anomalous events, leveraging insights from the simpler model. Through extensive experimentation, we investigate the trade-off between model performance and computational complexity, demonstrating that our proposed cascaded model achieves greater efficiency with no performance degradation. Further, we do a comparative analysis of the impact of probabilistic versus deterministic approaches and assess the feasibility of model training at edge environments using the Federated Learning concept.