Analysing convergence, consistency and trajectory of Artificial Bee Colony Algorithm

Recently, swarm intelligence based algorithms gained attention of the researchers due to their wide applicability and ease of implementation. However, much research has been made on the development of swarm intelligence algorithms but theoretical analysis of these algorithms is still a less explored area of the research. Theoretical analyses of trajectory and convergence of potential solutions towards the equilibrium point in the search space can help the researchers to understand the iteration-wise behaviour of the algorithms which can further help in making them efficient. Artificial Bee Colony (ABC) optimization algorithm is swarm intelligence based algorithm. This paper presents the convergence analysis of ABC algorithm by using results from the theory of dynamical system and convergent boundaries for the parameters $\phi$ and $\psi$ is proposed. Also the trajectory of potential solutions in the search space is analysed by obtaining a partial differential equation corresponding to the position update equation of ABC algorithm. The analysis reveals that the ABC algorithm performs better/efficiently when parameters $\phi$ and $\psi$ are in the convergent region and potential solutions movement follows 1-Dimensional advection equation

Modified analytical approach for PV-DGs integration into radial distribution network considering loss sensitivity and voltage stability

Abstract: Achieving the goals of distribution systems operation often involves taking vital decisions with adequate consideration for several but often contradictory technical and economic criteria. Hence, this paper presents a modified analytical approach for optimal location and sizing of solar PV-based DG units into radial distribution network (RDN) considering strategic combination of important power system planning criteria. The considered criteria are total planning cost, active power loss and voltage stability, under credible distribution network operation constraints. The optimal DG placement approach is derived from the modification of the analytical approach for DG placement using line-loss sensitivity factor and the multiobjective constriction factor-based particle swarm optimization is adopted for optimal sizing. The effectiveness of the proposed procedure is tested on the IEEE 33-bus system modeled using Matlab considering three scenarios. The results are compared with existing reports presented in the literature and the results obtained from the proposed approach shows credible improvement in the RDN steady-state operation performance for line-loss reduction, voltage profile improvement and voltage stability improvement

Dense RGB-D SLAM and object localisation for robotics and industrial applications

Dense reconstruction and object localisation are two critical steps in robotic and industrial applications. The former entails a joint estimation of camera egomotion and the structure of the surrounding environment, also known as Simultaneous Localisation and Mapping (SLAM), and the latter aims to locate the object in the reconstructed scenes. This thesis addresses the challenges of dense SLAM with RGB-D cameras and object localisation towards robotic and industrial applications. Camera drift is an essential issue in camera egomotion estimation. Due to the accumulated error in camera pose estimation, the estimated camera trajectory is inaccurate, and the reconstruction of the environment is inconsistent. This thesis analyses camera drift in SLAM under the probabilistic inference framework and proposes an online map fusion strategy with standard deviation estimation based on frame-to-model camera tracking. The camera pose is estimated by aligning the input image with the global map model, and the global map merges the information in the images by weighted fusion with standard deviation modelling. In addition, a pre-screening step is applied before map fusion to preclude the adverse effect of accumulated errors and noises on camera egomotion estimation. Experimental results indicated that the proposed method mitigates camera drift and improves the global consistency of camera trajectories. Another critical challenge for dense RGB-D SLAM in industrial scenarios is to handle mechanical and plastic components that usually have reflective and shiny surfaces. Photometric alignment in frame-to-model camera tracking tends to fail on such objects due to the inconsistency in intensity patterns of the images and the global map model. This thesis addresses this problem and proposes RSO-SLAM, namely a SLAM approach to reflective and shiny object reconstruction. RSO-SLAM adopts frame-to-model camera tracking and combines local photometric alignment and global geometric registration. This study revealed the effectiveness and excellent performance of the proposed RSO-SLAM on both plastic and metallic objects. In addition, a case study involving the cover of a electric vehicle battery with metallic surface demonstrated the superior performance of the RSO-SLAM approach in the reconstruction of a common industrial product. With the reconstructed point cloud model of the object, the problem of object localisation is tackled as point cloud registration in the thesis. Iterative Closest Point (ICP) is arguably the best-known method for point cloud registration, but it is susceptible to sub-optimal convergence due to the multimodal solution space. This thesis proposes the Bees Algorithm (BA) enhanced with the Singular Value Decomposition (SVD) procedure for point cloud registration. SVD accelerates the speed of the local search of the BA, helping the algorithm to rapidly identify the local optima. It also enhances the precision of the obtained solutions. At the same time, the global outlook of the BA ensures adequate exploration of the whole solution space. Experimental results demonstrated the remarkable performance of the SVD-enhanced BA in terms of consistency and precision. Additional tests on noisy datasets demonstrated the robustness of the proposed procedure to imprecision in the models

OptPlatform: metaheuristic optimisation framework for solving complex real-world problems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWe optimise daily, whether that is planning a round trip that visits the most attractions within a given holiday budget or just taking a train instead of driving a car in a rush hour. Many problems, just like these, are solved by individuals as part of our daily schedule, and they are effortless and straightforward. If we now scale that to many individuals with many different schedules, like a school timetable, we get to a point where it is just not feasible or practical to solve by hand. In such instances, optimisation methods are used to obtain an optimal solution. In this thesis, a practical approach to optimisation has been taken by developing an optimisation platform with all the necessary tools to be used by practitioners who are not necessarily familiar with the subject of optimisation. First, a high-performance metaheuristic optimisation framework (MOF) called OptPlatform is implemented, and the versatility and performance are evaluated across multiple benchmarks and real-world optimisation problems. Results show that, compared to competing MOFs, the OptPlatform outperforms in both the solution quality and computation time. Second, the most suitable hardware platform for OptPlatform is determined by an in-depth analysis of Ant Colony Optimisation scaling across CPU, GPU and enterprise Xeon Phi. Contrary to the common benchmark problems used in the literature, the supply chain problem solved could not scale on GPUs. Third, a variety of metaheuristics are implemented into OptPlatform. Including, a new metaheuristic based on Imperialist Competitive Algorithm (ICA), called ICA with Independence and Constrained Assimilation (ICAwICA) is proposed. The ICAwICA was compared against two different types of benchmark problems, and results show the versatile application of the algorithm, matching and in some cases outperforming the custom-tuned approaches. Finally, essential MOF features like automatic algorithm selection and tuning, lacking on existing frameworks, are implemented in OptPlatform. Two novel approaches are proposed and compared to existing methods. Results indicate the superiority of the implemented tuning algorithms within constrained tuning budget environment

An evolutionary algorithm approach to ecological optimal control problems

There are several challenges associated with applying conventional (hereafter classic) optimal control (OC) methods to ecological optimal control problems (OCPs). Conditions required by these methods, including differentiability and convexity, for example, are not always met, and ecological problems do not always adhere to solvable OCP formulations. Moreover, mathematically optimal solutions do not always translate to optimal ecological strategies in practice. Despite this, alternative OC approaches are relatively under-explored. Evolutionary algorithms (EAs) circumvent many of the complex aspects of classic OC methods and have been successfully applied to diverse OCPs. Nevertheless, EAs have sel dom been applied to ecological OCPs. The viability of an EA approach to ecological OCPs was therefore investigated in the current study, facilitated by four case studies of increasing complexity and a genetic algorithm (GA) as a representative EA approach. To ascertain the accuracy of a GA approach, comparisons between a GA and classic OC methods were conducted in the first three case studies. The GA generated near-optima in these comparisons, comparable to the corresponding classical solutions, whilst avoiding non-trivial mathematical theory. Supported by these results, an unconventional OCP, that arguably cannot be solved using classic OC methods, was formulated in the fourth case study, and solved using a GA approach. The resulting solution was feasible and further conformed with strategies found to be successful in practice. Additionally, the GA approach was rela tively simple to apply in all case studies. These collective outcomes of demonstrated the viability of a GA as an OC method in eco logical OCPs, thereby supporting the use of an EA approach as an alternative to classic OC methods in ecological OCPs. The feasibility of an EA approach to atypical OCPs was further demonstrated, which may act to increase realism in OC applications. Further investigation in this regard is thus warranted by this study.Thesis (MS) -- Faculty of Science, Mathematics and Applied Mathematics, 202

An evolutionary algorithm approach to ecological optimal control problems

There are several challenges associated with applying conventional (hereafter classic) optimal control (OC) methods to ecological optimal control problems (OCPs). Conditions required by these methods, including differentiability and convexity, for example, are not always met, and ecological problems do not always adhere to solvable OCP formulations. Moreover, mathematically optimal solutions do not always translate to optimal ecological strategies in practice. Despite this, alternative OC approaches are relatively under-explored. Evolutionary algorithms (EAs) circumvent many of the complex aspects of classic OC methods and have been successfully applied to diverse OCPs. Nevertheless, EAs have sel dom been applied to ecological OCPs. The viability of an EA approach to ecological OCPs was therefore investigated in the current study, facilitated by four case studies of increasing complexity and a genetic algorithm (GA) as a representative EA approach. To ascertain the accuracy of a GA approach, comparisons between a GA and classic OC methods were conducted in the first three case studies. The GA generated near-optima in these comparisons, comparable to the corresponding classical solutions, whilst avoiding non-trivial mathematical theory. Supported by these results, an unconventional OCP, that arguably cannot be solved using classic OC methods, was formulated in the fourth case study, and solved using a GA approach. The resulting solution was feasible and further conformed with strategies found to be successful in practice. Additionally, the GA approach was rela tively simple to apply in all case studies. These collective outcomes of demonstrated the viability of a GA as an OC method in eco logical OCPs, thereby supporting the use of an EA approach as an alternative to classic OC methods in ecological OCPs. The feasibility of an EA approach to atypical OCPs was further demonstrated, which may act to increase realism in OC applications. Further investigation in this regard is thus warranted by this study.Thesis (MS) -- Faculty of Science, Mathematics and Applied Mathematics, 202

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

Spatial energetics:a thermodynamically-consistent methodology for modelling resource acquisition, distribution, and end-use networks in nature and society

Resource acquisition, distribution, and end-use (RADE) networks are ubiquitous in natural and human-engineered systems, connecting spatially-distributed points of supply and demand, to provide energy and material resources required by these systems for growth and maintenance. A clear understanding of the dynamics of these networks is crucial to protect those supported and impacted by them, but past modelling efforts are limited in their explicit consideration of spatial size and topology, which are necessary to the thermodynamically-realistic representation of the energetics of these networks. This thesis attempts to address these limitations by developing a spatially-explicit modelling framework for generalised energetic resource flows, as occurring in ecological and coupled socio-ecological systems. The methodology utilises equations from electrical engineering to operationalise the first and second laws of thermodynamics in flow calculations, and places these within an optimisation algorithm to replicate the selective pressure to maximise resource transfer and consumption and minimise energetic transport costs. The framework is applied to the nectar collection networks of A. mellifera as a proof-of-concept. The promising performance of the methodology in calculating the energetics of these networks in a flow-conserving manner, replicating attributes of foraging networks, and generating network structures consistent with those of known RADE networks, demonstrate the validity of the methodology, and suggests several potential avenues for future refinement and application