Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Comparison of Geometric Optimization Methods with Multiobjective Genetic Algorithms for Solving Integrated Optimal Design Problems

In this paper, system design methodologies for optimizing heterogenous power devices in electrical engineering are investigated. The concept of Integrated Optimal Design (IOD) is presented and a simplified but typical example is given. It consists in finding Pareto-optimal configurations for the motor drive of an electric vehicle. For that purpose, a geometric optimization method (i.e the Hooke and Jeeves minimization procedure) associated with an objective weighting sum and a Multiobjective Genetic Algorithm (i.e. the NSGA-II) are compared. Several performance issues are discussed such as the accuracy in the determination of Pareto-optimal configurations and the capability to well spread these solutions in the objective space

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

Evolutionary Dynamic Multi-Objective Optimisation : A survey

Peer reviewedPostprin

A dynamic multi-objective evolutionary algorithm based on decision variable classification

The file attached to this record is the author's final peer reviewed version.In recent years, dynamic multi-objective optimization problems (DMOPs) have drawn increasing interest. Many dynamic multi-objective evolutionary algorithms (DMOEAs) have been put forward to solve DMOPs mainly by incorporating diversity introduction or prediction approaches with conventional multi-objective evolutionary algorithms. Maintaining good balance of population diversity and convergence is critical to the performance of DMOEAs. To address the above issue, a dynamic multi-objective evolutionary algorithm based on decision variable classification (DMOEA-DVC) is proposed in this study. DMOEA-DVC divides the decision variables into two and three different groups in static optimization and change response stages, respectively. In static optimization, two different crossover operators are used for the two decision variable groups to accelerate the convergence while maintaining good diversity. In change response, DMOEA-DVC reinitializes the three decision variable groups by maintenance, prediction, and diversity introduction strategies, respectively. DMOEA-DVC is compared with the other six state-of-the-art DMOEAs on 33 benchmark DMOPs. Experimental results demonstrate that the overall performance of the DMOEA-DVC is superior or comparable to that of the compared algorithms

A Two-Step Optimisation Method for Dynamic Weapon Target Assignment Problem

International audienceno abstrac

Efficient radio resource management for the fifth generation slice networks

It is predicted that the IMT-2020 (5G network) will meet increasing user demands and, hence, it is therefore, expected to be as flexible as possible. The relevant standardisation bodies and academia have accepted the critical role of network slicing in the implementation of the 5G network. The network slicing paradigm allows the physical infrastructure and resources of the mobile network to be “sliced” into logical networks, which are operated by different entities, and then engineered to address the specific requirements of different verticals, business models, and individual subscribers. Network slicing offers propitious solutions to the flexibility requirements of the 5G network. The attributes and characteristics of network slicing support the multi-tenancy paradigm, which is predicted to drastically reduce the operational expenditure (OPEX) and capital expenditure (CAPEX) of mobile network operators. Furthermore, network slices enable mobile virtual network operators to compete with one another using the same physical networks but customising their slices and network operation according to their market segment's characteristics and requirements. However, owing to scarce radio resources, the dynamic characteristics of the wireless links, and its capacity, implementing network slicing at the base stations and the access network xix becomes an uphill task. Moreover, an unplanned 5G slice network deployment results in technical challenges such as unfairness in radio resource allocation, poor quality of service provisioning, network profit maximisation challenges, and rises in energy consumption in a bid to meet QoS specifications. Therefore, there is a need to develop efficient radio resource management algorithms that address the above mentioned technical challenges. The core aim of this research is to develop and evaluate efficient radio resource management algorithms and schemes that will be implemented in 5G slice networks to guarantee the QoS of users in terms of throughput and latency while ensuring that 5G slice networks are energy efficient and economically profitable. This thesis mainly addresses key challenges relating to efficient radio resource management. First, a particle swarm-intelligent profit-aware resource allocation scheme for a 5G slice network is proposed to prioritise the profitability of the network while at the same time ensuring that the QoS requirements of slice users are not compromised. It is observed that the proposed new radio swarm-intelligent profit-aware resource allocation (NR-SiRARE) scheme outperforms the LTE-OFDMA swarm-intelligent profit-aware resource (LO-SiRARE) scheme. However, the network profit for the NR-SiRARE is greatly affected by significant degradation of the path loss associated with millimetre waves. Second, this thesis examines the resource allocation challenge in a multi-tenant multi-slice multi-tier heterogeneous network. To maximise the total utility of a multi-tenant multislice multi-tier heterogeneous network, a latency-aware dynamic resource allocation problem is formulated as an optimisation problem. Via the hierarchical decomposition method for heterogeneous networks, the formulated optimisation problem is transformed to reduce the computational complexities of the proposed solutions. Furthermore, a genetic algorithmbased latency-aware resource allocation scheme is proposed to solve the maximum utility problem by considering related constraints. It is observed that GI-LARE scheme outperforms the static slicing (SS) and an optimal resource allocation (ORA) schemes. Moreover, the GI-LARE appears to be near optimal when compared with an exact solution based on spatial branch and bound. Third, this thesis addresses a distributed resource allocation problem in a multi-slice multitier multi-domain network with different players. A three-level hierarchical business model comprising InPs, MVNOs, and service providers (SP) is examined. The radio resource allocation problem is formulated as a maximum utility optimisation problem. A multi-tier multi-domain slice user matching game and a distributed backtracking multi-player multidomain games schemes are proposed to solve the maximum utility optimisation problem. The distributed backtracking scheme is based on the Fisher Market and Auction theory principles. The proposed multi-tier multi-domain scheme outperforms the GI-LARE and the SS schemes. This is attributed to the availability of resources from other InPs and MVNOs; and the flexibility associated with a multi-domain network. Lastly, an energy-efficient resource allocation problem for 5G slice networks in a highly dense heterogeneous environment is investigated. A mathematical formulation of energy-efficient resource allocation in 5G slice networks is developed as a mixed-integer linear fractional optimisation problem (MILFP). The method adopts hierarchical decomposition techniques to reduce complexities. Furthermore, the slice user association, QoS for different slice use cases, an adapted water filling algorithm, and stochastic geometry tools are employed to xxi model the global energy efficiency (GEE) of the 5G slice network. Besides, neither stochastic geometry nor a three-level hierarchical business model schemes have been employed to model the global energy efficiency of the 5G slice network in the literature, making it the first time such method will be applied to 5G slice network. With rigorous numerical simulations based on Monte-Carlo numerical simulation technique, the performance of the proposed algorithms and schemes was evaluated to show their adaptability, efficiency and robustness for a 5G slice network

A comprehensive survey on cultural algorithms

Peer reviewedPostprin

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