An Investigation Into the use of Swarm Intelligence for an Evolutionary Algorithm Optimisation; The Optimisation Performance of Differential Evolution Algorithm Coupled with Stochastic Diffusion Search

The integration of Swarm Intelligence (SI) algorithms and Evolutionary algorithms (EAs) might be one of the future approaches in the Evolutionary Computation (EC). This work narrates the early research on using Stochastic Diffusion Search (SDS) -- a swarm intelligence algorithm -- to empower the Differential Evolution (DE) -- an evolutionary algorithm -- over a set of optimisation problems. The results reported herein suggest that the powerful resource allocation mechanism deployed in SDS has the potential to improve the optimisation capability of the classical evolutionary algorithm used in this experiment. Different performance measures and statistical analyses were utilised to monitor the behaviour of the final coupled algorithm

Stochastic diffusion search review

Stochastic Diffusion Search, first incepted in 1989, belongs to the extended family of swarm intelligence algorithms. In contrast to many nature-inspired algorithms, stochastic diffusion search has a strong mathematical framework describing its behaviour and convergence. In addition to concisely exploring the algorithm in the context of natural swarm intelligence systems, this paper reviews various developments of the algorithm, which have been shown to perform well in a variety of application domains including continuous optimisation, implementation on hardware and medical imaging. This algorithm has also being utilised to argue the potential computational creativity of swarm intelligence systems through the two phases of exploration and exploitatio

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Stochastic Diffusion Search: A Comparison of Swarm Intelligence Parameter Estimation Algorithms with RANSAC

Stochastic diffusion search (SDS) is a multi-agent global optimisation technique based on the behaviour of ants, rooted in the partial evaluation of an objective function and direct communication between agents. Standard SDS, the fundamental algorithm at work in all SDS processes, is presented here. Parameter estimation is the task of suitably fitting a model to given data; some form of parameter estimation is a key element of many computer vision processes. Here, the task of hyperplane estimation in many dimensions is investigated. Following RANSAC (random sample consensus), a widely used optimisation technique and a standard technique for many parameter estimation problems, increasingly sophisticated data-driven forms of SDS are developed. The performance of these SDS algorithms and RANSAC is analysed and compared for a hyperplane estimation task. SDS is shown to perform similarly to RANSAC, with potential for tuning to particular search problems for improved results

Weak and Strong Computational Creativity

In the spirit of Searle’s definition of weak and strong artificial intelligence, this paper presents a discussion on weak computational creativity in swarm intelligence systems. It addresses the concepts of freedom and constraint and their impact on the creativity of the underlying systems. An analogy is drawn on mapping these two ‘prerequisites’ of creativity onto the two well-known phases of exploration and exploitation in swarm intelligence algorithms, followed by the visualisation of the behaviour of the swarms whose performance are evaluated in the context of arguments presented. The paper also discusses that the strong computational creativity is presented in ways emphasising that genuine creativity implies ‘genuine under- standing’ and other cognitive states, along with autonomy – asserting that without ‘Strong Embodiment’, computational systems are not genuinely autonomous

Novel sampling techniques for reservoir history matching optimisation and uncertainty quantification in flow prediction

Modern reservoir management has an increasing focus on accurately predicting the likely range of field recoveries. A variety of assisted history matching techniques has been developed across the research community concerned with this topic. These techniques are based on obtaining multiple models that closely reproduce the historical flow behaviour of a reservoir. The set of resulted history matched models is then used to quantify uncertainty in predicting the future performance of the reservoir and providing economic evaluations for different field development strategies. The key step in this workflow is to employ algorithms that sample the parameter space in an efficient but appropriate manner. The algorithm choice has an impact on how fast a model is obtained and how well the model fits the production data. The sampling techniques that have been developed to date include, among others, gradient based methods, evolutionary algorithms, and ensemble Kalman filter (EnKF). This thesis has investigated and further developed the following sampling and inference techniques: Particle Swarm Optimisation (PSO), Hamiltonian Monte Carlo, and Population Markov Chain Monte Carlo. The inspected techniques have the capability of navigating the parameter space and producing history matched models that can be used to quantify the uncertainty in the forecasts in a faster and more reliable way. The analysis of these techniques, compared with Neighbourhood Algorithm (NA), has shown how the different techniques affect the predicted recovery from petroleum systems and the benefits of the developed methods over the NA. The history matching problem is multi-objective in nature, with the production data possibly consisting of multiple types, coming from different wells, and collected at different times. Multiple objectives can be constructed from these data and explicitly be optimised in the multi-objective scheme. The thesis has extended the PSO to handle multi-objective history matching problems in which a number of possible conflicting objectives must be satisfied simultaneously. The benefits and efficiency of innovative multi-objective particle swarm scheme (MOPSO) are demonstrated for synthetic reservoirs. It is demonstrated that the MOPSO procedure can provide a substantial improvement in finding a diverse set of good fitting models with a fewer number of very costly forward simulations runs than the standard single objective case, depending on how the objectives are constructed. The thesis has also shown how to tackle a large number of unknown parameters through the coupling of high performance global optimisation algorithms, such as PSO, with model reduction techniques such as kernel principal component analysis (PCA), for parameterising spatially correlated random fields. The results of the PSO-PCA coupling applied to a recent SPE benchmark history matching problem have demonstrated that the approach is indeed applicable for practical problems. A comparison of PSO with the EnKF data assimilation method has been carried out and has concluded that both methods have obtained comparable results on the example case. This point reinforces the need for using a range of assisted history matching algorithms for more confidence in predictions

Analysis of physiological signals using machine learning methods

Technological advances in data collection enable scientists to suggest novel approaches, such as Machine Learning algorithms, to process and make sense of this information. However, during this process of collection, data loss and damage can occur for reasons such as faulty device sensors or miscommunication. In the context of time-series data such as multi-channel bio-signals, there is a possibility of losing a whole channel. In such cases, existing research suggests imputing the missing parts when the majority of data is available. One way of understanding and classifying complex signals is by using deep neural networks. The hyper-parameters of such models have been optimised using the process of back propagation. Over time, improvements have been suggested to enhance this algorithm. However, an essential drawback of the back propagation can be the sensitivity to noisy data. This thesis proposes two novel approaches to address the missing data challenge and back propagation drawbacks: First, suggesting a gradient-free model in order to discover the optimal hyper-parameters of a deep neural network. The complexity of deep networks and high-dimensional optimisation parameters presents challenges to find a suitable network structure and hyper-parameter configuration. This thesis proposes the use of a minimalist swarm optimiser, Dispersive Flies Optimisation(DFO), to enable the selected model to achieve better results in comparison with the traditional back propagation algorithm in certain conditions such as limited number of training samples. The DFO algorithm offers a robust search process for finding and determining the hyper-parameter configurations. Second, imputing whole missing bio-signals within a multi-channel sample. This approach comprises two experiments, namely the two-signal and five-signal imputation models. The first experiment attempts to implement and evaluate the performance of a model mapping bio-signals from A toB and vice versa. Conceptually, this is an extension to transfer learning using CycleGenerative Adversarial Networks (CycleGANs). The second experiment attempts to suggest a mechanism imputing missing signals in instances where multiple data channels are available for each sample. The capability to map to a target signal through multiple source domains achieves a more accurate estimate for the target domain. The results of the experiments performed indicate that in certain circumstances, such as having a limited number of samples, finding the optimal hyper-parameters of a neural network using gradient-free algorithms outperforms traditional gradient-based algorithms, leading to more accurate classification results. In addition, Generative Adversarial Networks could be used to impute the missing data channels in multi-channel bio-signals, and the generated data used for further analysis and classification tasks

Handling Class Imbalance Using Swarm Intelligence Techniques, Hybrid Data and Algorithmic Level Solutions

This research focuses mainly on the binary class imbalance problem in data mining. It investigates the use of combined approaches of data and algorithmic level solutions. Moreover, it examines the use of swarm intelligence and population-based techniques to combat the class imbalance problem at all levels, including at the data, algorithmic, and feature level. It also introduces various solutions to the class imbalance problem, in which swarm intelligence techniques like Stochastic Diffusion Search (SDS) and Dispersive Flies Optimisation (DFO) are used. The algorithms were evaluated using experiments on imbalanced datasets, in which the Support Vector Machine (SVM) was used as a classifier. SDS was used to perform informed undersampling of the majority class to balance the dataset. The results indicate that this algorithm improves the classifier performance and can be used on imbalanced datasets. Moreover, SDS was extended further to perform feature selection on high dimensional datasets. Experimental results show that SDS can be used to perform feature selection and improve the classifier performance on imbalanced datasets. Further experiments evaluated DFO as an algorithmic level solution to optimise the SVM kernel parameters when learning from imbalanced datasets. Based on the promising results of DFO in these experiments, the novel approach was extended further to provide a hybrid algorithm that simultaneously optimises the kernel parameters and performs feature selection