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

Half a billion simulations: evolutionary algorithms and distributed computing for calibrating the SimpopLocal geographical model

Multi-agent geographical models integrate very large numbers of spatial interactions. In order to validate those models large amount of computing is necessary for their simulation and calibration. Here a new data processing chain including an automated calibration procedure is experimented on a computational grid using evolutionary algorithms. This is applied for the first time to a geographical model designed to simulate the evolution of an early urban settlement system. The method enables us to reduce the computing time and provides robust results. Using this method, we identify several parameter settings that minimise three objective functions that quantify how closely the model results match a reference pattern. As the values of each parameter in different settings are very close, this estimation considerably reduces the initial possible domain of variation of the parameters. The model is thus a useful tool for further multiple applications on empirical historical situations

Spatially optimised sustainable urban development

PhD ThesisTackling urbanisation and climate change requires more sustainable and resilient cities, which in turn will require planners to develop a portfolio of measures to manage climate risks such as flooding, meet energy and greenhouse gas reduction targets, and prioritise development on brownfield sites to preserve greenspace. However, the policies, strategies and measures put in place to meet such objectives can frequently conflict with each other or deliver unintended consequences, hampering long-term sustainability. For example, the densification of cities in order to reduce transport energy use can increase urban heat island effects and surface water flooding from extreme rainfall events. In order to make coherent decisions in the presence of such complex multi-dimensional spatial conflicts, urban planners require sophisticated planning tools to identify and manage potential trade-offs between the spatial strategies necessary to deliver sustainability. To achieve this aim, this research has developed a multi-objective spatial optimisation framework for the spatial planning of new residential development within cities. The implemented framework develops spatial strategies of required new residential development that minimize conflicts between multiple sustainability objectives as a result of planning policy and climate change related hazards. Five key sustainability objectives have been investigated, namely; (i) minimizing risk from heat waves, (ii) minimizing the risk from flood events, (iii) minimizing travel costs in order to reduce transport emissions, (iv) minimizing urban sprawl and (v) preventing development on existing greenspace. A review identified two optimisation algorithms as suitable for this task. Simulated Annealing (SA) is a traditional optimisation algorithm that uses a probabilistic approach to seek out a global optima by iteratively assessing a wide range of spatial configurations against the objectives under consideration. Gradual ‘cooling’, or reducing the probability of jumping to a different region of the objective space, helps the SA to converge on globally optimal spatial patterns. Genetic Algorithms (GA) evolve successive generations of solutions, by both recombining attributes and randomly mutating previous generations of solutions, to search for and converge towards superior spatial strategies. The framework works towards, and outputs, a series of Pareto-optimal spatial plans that outperform all other plans in at least one objective. This approach allows for a range of best trade-off plans for planners to choose from. ii Both SA and GA were evaluated for an initial case study in Middlesbrough, in the North East of England, and were able to identify strategies which significantly improve upon the local authority’s development plan. For example, the GA approach is able to identify a spatial strategy that reduces the travel to work distance between new development and the central business district by 77.5% whilst nullifying the flood risk to the new development. A comparison of the two optimisation approaches for the Middlesbrough case study revealed that the GA is the more effective approach. The GA is more able to escape local optima and on average outperforms the SA by 56% in in the Pareto fronts discovered whilst discovering double the number of multi-objective Pareto-optimal spatial plans. On the basis of the initial Middlesbrough case study the GA approach was applied to the significantly larger, and more computationally complex, problem of optimising spatial development plans for London in the UK – a total area of 1,572km2. The framework identified optimal strategies in less than 400 generations. The analysis showed, for example, strategies that provide the lowest heat risk (compared to the feasible spatial plans found) can be achieved whilst also using 85% brownfield land to locate new development. The framework was further extended to investigate the impact of different development and density regulations. This enabled the identification of optimised strategies, albeit at lower building density, that completely prevent any increase in urban sprawl whilst also improving the heat risk objective by 60% against a business as usual development strategy. Conversely by restricting development to brownfield the ability of the spatial plan to optimise future heat risk is reduced by 55.6% against the business as usual development strategy. The results of both case studies demonstrate the potential of spatial optimisation to provide planners with optimal spatial plans in the presence of conflicting sustainability objectives. The resulting diagnostic information provides an analytical appreciation of the sensitivity between conflicts and therefore the overall robustness of a plan to uncertainty. With the inclusion of further objectives, and qualitative information unsuitable for this type of analysis, spatial optimization can constitute a powerful decision support tool to help planners to identify spatial development strategies that satisfy multiple sustainability objectives and provide an evidence base for better decision making

Digital Ecosystems: Ecosystem-Oriented Architectures

We view Digital Ecosystems to be the digital counterparts of biological ecosystems. Here, we are concerned with the creation of these Digital Ecosystems, exploiting the self-organising properties of biological ecosystems to evolve high-level software applications. Therefore, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. The Digital Ecosystem was then measured experimentally through simulations, with measures originating from theoretical ecology, evaluating its likeness to biological ecosystems. This included its responsiveness to requests for applications from the user base, as a measure of the ecological succession (ecosystem maturity). Overall, we have advanced the understanding of Digital Ecosystems, creating Ecosystem-Oriented Architectures where the word ecosystem is more than just a metaphor.Comment: 39 pages, 26 figures, journa

Data-driven modelling of biological multi-scale processes

Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual spatial scales and how they are integrated into holistic models. We discuss the relation between spatial and temporal scales and the implication of that on multi-scale modelling. Based upon this overview over state-of-the-art modelling approaches, we formulate key challenges in mathematical and computational modelling of biological multi-scale and multi-physics processes. In particular, we considered the availability of analysis tools for multi-scale models and model-based multi-scale data integration. We provide a compact review of methods for model-based data integration and model-based hypothesis testing. Furthermore, novel approaches and recent trends are discussed, including computation time reduction using reduced order and surrogate models, which contribute to the solution of inference problems. We conclude the manuscript by providing a few ideas for the development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and Multiscale Dynamics (American Scientific Publishers

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

Combining evolutionary algorithms and agent-based simulation for the development of urbanisation policies

Urban-planning authorities continually face the problem of optimising the allocation of green space over time in developing urban environments. To help in these decision-making processes, this thesis provides an empirical study of using evolutionary approaches to solve sequential decision making problems under uncertainty in stochastic environments. To achieve this goal, this work is underpinned by developing a theoretical framework based on the economic model of Alonso and the associated methodology for modelling spatial and temporal urban growth, in order to better understand the complexity inherent in this kind of system and to generate and improve relevant knowledge for the urban planning community. The model was hybridised with cellular automata and agent-based model and extended to encompass green space planning based on urban cost and satisfaction. Monte Carlo sampling techniques and the use of the urban model as a surrogate tool were the two main elements investigated and applied to overcome the noise and uncertainty derived from dealing with future trends and expectations. Once the evolutionary algorithms were equipped with these mechanisms, the problem under consideration was defined and characterised as a type of adaptive submodular. Afterwards, the performance of a non-adaptive evolutionary approach with a random search and a very smart greedy algorithm was compared and in which way the complexity that is linked with the configuration of the problem modifies the performance of both algorithms was analysed. Later on, the application of very distinct frameworks incorporating evolutionary algorithm approaches for this problem was explored: (i) an ‘offline’ approach, in which a candidate solution encodes a complete set of decisions, which is then evaluated by full simulation, and (ii) an ‘online’ approach which involves a sequential series of optimizations, each making only a single decision, and starting its simulations from the endpoint of the previous run