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

Hybrid multi-objective trajectory optimization of low-thrust space mission design

Mención Internacional en el título de doctorThe overall goal of this dissertation is to develop multi-objective optimization algorithms for computing low-thrust trajectories. The thesis is motivated by the increasing number of space projects that will benefit from low-thrust propulsion technologies to gain unprecedented scientific, economic and social return. The low-cost design of such missions and the inclusion of concurrent engineering practices during the preliminary design phase demand advanced tools to rapidly explore different solutions and to benchmark them with respect to multiple conicting criteria. However, the determination of optimal low-thrust transfers is a challenging task and remains an active research field that seeks performance improvements. This work contributes to increase the efficiency of searching wide design spaces, reduce the amount of necessary human involvement, and enhance the capabilities to include complex operational constraints. To that end, the general low-thrust trajectory optimization problem is stated as a multi-objective Hybrid Optimal Control Problem. This formulation allows to simultaneously optimize discrete decisionmaking processes, discrete dynamics, and the continuous low-thrust steering law. Within this framework, a sequential two-step solution approach is devised for two different scenarios. The first problem considers the optimization of low-thrust multi-gravity assist trajectories. The proposed solution procedure starts by assuming a planar shape-based model for the interplanetary trajectory. A multi-objective heuristic algorithm combined with a gradient-based solver optimize the parameters de_ning the shape of the trajectory, the number and sequence of the gravity assists, the departure and arrival dates, and the launch excess velocity. In the second step, candidate solutions are deemed as initial guesses to solve the Nonlinear Programming Problem resulting from applying a direct collocation transcription scheme. In this step, the sequence of planetary gravity assists is known and provided by the heuristic search, dynamics is three-dimensional, and the steering law is not predefined. Operational constraints to comply with launch asymptote declination limits and fixed reorientation times during the transfer apply. The presented approach is tested on a rendezvous mission to Ceres, on a yby mission to Jupiter, and on a rendezvous mission to Pluto. Pareto-optimal solutions in terms of time of ight and propellant mass consumed (or alternatively delivered mass) are obtained. Results outperform those found in the literature in terms of optimality while showing the effectiveness of the proposed methodology to generate quick performance estimates. The second problem considers the simultaneous optimization of fully electric, fully chemical and combined chemical-electric orbit raising transfers between Earth's orbits is considered. In the first step of the solution approach, the control law of the electric engine is parameterized by a Lyapunov function. A multi-objective heuristic algorithm selects the optimal propulsion system, the transfer type, the low-thrust control history, as well as the number, orientation, and magnitude of the chemical firings. Earth's shadow, oblateness and Van-Allen radiation effects are included. In the second step, candidate solutions are deemed as initial guesses to solve the Nonlinear Programming Problem resulting from applying a direct collocation scheme. Operational constraints to avoid the GEO ring in combination to slew rate limits and slot phasing constraints are included. The proposed approach is applied to two transfer scenarios to GEO orbit. Pareto-optimal solutions trading of propellant mass, time of ight and solar-cell degradation are obtained. It is identified that the application of operational restrictions causes minor penalties in the objective function. Additionally, the analysis highlights the benefits that combined chemical-electric platforms may provide for future GEO satellites.El objetivo principal de esta trabajo es desarrollar algoritmos de optimización multi-objetivo para la obtención de trayectorias espaciales con motores de bajo empuje. La tesis está motivada por el creciente número de misiones que se van a beneficiar del uso de estas tecnologías para conseguir beneficios científicos, económicos y sociales sin precedentes. El diseño de bajo coste de dichas misiones ligado a los principios de ingeniería concurrente requieren herramientas computacionales avanzadas que exploren rápidamente distintas soluciones y las comparen entre sí respecto a varios criterios. Sin embargo, esta tarea permanece como un campo de investigación activo que busca continuamente mejoras de rendimiento durante el proceso. Este trabajo contribuye a aumentar la eficiencia cuando espacio de diseño es amplio, a reducir la participación humana requerida y a mejorar las capacidades para incluir restricciones operacionales complejas. Para este fin, el problema general de optimización de trayectorias de bajo empuje se presenta como un problema híbrido de control óptimo. Esta formulación permite optimizar al mismo tiempo procesos de toma de decisiones, dinámica discreta y la ley de control del motor. Dentro de este marco, se idea un algoritmo secuencial de dos pasos para dos escenarios diferentes. El primer problema considera la optimización de trayectorias de bajo empuje con múltiples y-bys. El proceso de solución propuesto comienza asumiendo un modelo plano y shape-based para la trayectoria interplanetaria. Un algoritmo de optimización heurístico y multi-objetivo combinado con un resolvedor basado en gradiente optimizan los parámetros de la espiral que definen la forma de la trayectoria, el número y la secuencia de las maniobras gravitacionales, las fechas de salida y llegada, y la velocidad de lanzamiento. En el segundo paso, las soluciones candidatas se usan como estimación inicial para resolver el problema de optimización no lineal que resulta de aplicar un método de transcripción directa. En este paso, las secuencia de y-bys es conocida y determinada por el paso anterior, la dinámica es tridimensional, y la ley de control no está prefinida. Además, se pueden aplicar restricciones operacionales relacionadas con las declinación de la asíntota de salida e imponer tiempos de reorientación fijos. Este enfoque es probado en misiones a Ceres, a Júpiter y a Plutón. Se obtienen soluciones óptimas de Pareto en función del tiempo de vuelo y la masa de combustible consumida (o la masa entregada). Los resultados obtenidos mejoran los disponibles en la literatura en términos de optimalidad, a la vez que reflejan la efectividad de la metodología a propuesta para generar estimaciones rápidas. El segundo problema considera la optimización simultanea de transferencias entre órbitas terrestres que usan propulsión eléctrica, química o una combinación de ambas. En el primer paso del método de solución, la ley de control del motor eléctrico se parametriza por una función de Lyapunov. Un algoritmo de optimización heurístico y multi-objetivo selecciona el sistema propulsivo óptimo, el tipo de transferencia, la ley de control del motor de bajo empuje, así como el número, la orientación y la magnitud de los impulsos químicos. Se incluyen los efectos de la sombra y de la no esfericidad de la Tierra, además de la radiación de Van-Allen. En el segundo paso, las soluciones candidatas se usan como estimación inicial para resolver el problema de optimización no lineal que resulta de aplicar un método de transcripción directa. El método de solución propuesto se aplica a dos transferencias a GEO diferentes. Se obtienen soluciones óptimas de Pareto con respecto a la masa de combustible, el tiempo de vuelo y la degradación de las células solares. Se identifican que la aplicación de las restricciones operacionales penaliza mínimamente la función objetivo. Además, los análisis presentados destacan los beneficios que la propulsión química y eléctrica combinada proporcionarían a los satélites en GEO.Programa de Doctorado en Mecánica de Fluidos por la Universidad Carlos III de Madrid; la Universidad de Jaén; la Universidad de Zaragoza; la Universidad Nacional de Educación a Distancia; la Universidad Politécnica de Madrid y la Universidad Rovira i Virgili.Presidente: Rafael Vázquez Valenzuela.- Secretario: Claudio Bombardelli.- Vocal: Bruce A. Conwa

Design optimisation of complex space systems under epistemic uncertainty

This thesis presents an innovative methodology for System Design Optimisation (SDO) through the framework of Model-Based System Engineering (MBSE) that bridges system modelling, Constrained Global Optimisation (CGO), Uncertainty Quantification (UQ), System Dynamics (SD) and other mathematical tools for the design of Complex Engineered and Engineering Systems (CEdgSs) under epistemic uncertainty. The problem under analysis has analogies with what is nowadays studied as Generative Design under Uncertainty. The method is finally applied to the design of Space Systems which are Complex Engineered Systems (CEdSs) composed of multiple interconnected sub-systems. A critical aspect in the design of Space Systems is the uncertainty involved. Much of the uncertainty is epistemic and is here modelled with Dempster Shafer Theory (DST). Designing space systems is a complex task that involves the coordination of different disciplines and problems. The thesis then proposes a set of building blocks, that is a toolbox of methodologies for the solution of problems which are of interest also if considered independently. It proposes then a holistic framework that couples these building blocks to form a SDO procedure. With regard to the building blocks, the thesis includes a network-based modelling procedure for CEdSs and a generalisation for CEdgSs where the system and the whole design process are both taken into account. Then, it presents a constraint min-max solver as an algorithmic procedures for the solution of the general Optimisation Under Uncertainty (OUU) problem. An extension of the method for the Multi-Objective Problems (MOP) is also proposed in Appendix as a minor result. A side contribution for the optimisation part refers to the extension of the global optimiser Multi Population Adaptive Inflationary Differential Evolution Algorithm (MP-AIDEA) with the introduction of constraint handling and multiple objective functions. The Constraint Multi-Objective Problem (CMOP) solver is however a preliminary result and it is reported in Appendix. Furthermore, the thesis proposes a decomposition methodology for the computational reduction of UQ with DST. As a partial contribution, a second approach based on a Binary Tree decomposition is also reported in Appendix. With regard to the holistic approach, instead, the thesis gives a new dentition and proposes a framework for system network robustness and for system network resilience. It finally presents the framework for the optimisation of the whole design process through the use of a multi-layer network model.This thesis presents an innovative methodology for System Design Optimisation (SDO) through the framework of Model-Based System Engineering (MBSE) that bridges system modelling, Constrained Global Optimisation (CGO), Uncertainty Quantification (UQ), System Dynamics (SD) and other mathematical tools for the design of Complex Engineered and Engineering Systems (CEdgSs) under epistemic uncertainty. The problem under analysis has analogies with what is nowadays studied as Generative Design under Uncertainty. The method is finally applied to the design of Space Systems which are Complex Engineered Systems (CEdSs) composed of multiple interconnected sub-systems. A critical aspect in the design of Space Systems is the uncertainty involved. Much of the uncertainty is epistemic and is here modelled with Dempster Shafer Theory (DST). Designing space systems is a complex task that involves the coordination of different disciplines and problems. The thesis then proposes a set of building blocks, that is a toolbox of methodologies for the solution of problems which are of interest also if considered independently. It proposes then a holistic framework that couples these building blocks to form a SDO procedure. With regard to the building blocks, the thesis includes a network-based modelling procedure for CEdSs and a generalisation for CEdgSs where the system and the whole design process are both taken into account. Then, it presents a constraint min-max solver as an algorithmic procedures for the solution of the general Optimisation Under Uncertainty (OUU) problem. An extension of the method for the Multi-Objective Problems (MOP) is also proposed in Appendix as a minor result. A side contribution for the optimisation part refers to the extension of the global optimiser Multi Population Adaptive Inflationary Differential Evolution Algorithm (MP-AIDEA) with the introduction of constraint handling and multiple objective functions. The Constraint Multi-Objective Problem (CMOP) solver is however a preliminary result and it is reported in Appendix. Furthermore, the thesis proposes a decomposition methodology for the computational reduction of UQ with DST. As a partial contribution, a second approach based on a Binary Tree decomposition is also reported in Appendix. With regard to the holistic approach, instead, the thesis gives a new dentition and proposes a framework for system network robustness and for system network resilience. It finally presents the framework for the optimisation of the whole design process through the use of a multi-layer network model

Evolutionary multiobjective optimization : review, algorithms, and applications

Programa Doutoral em Engenharia Industrial e SistemasMany mathematical problems arising from diverse elds of human activity can be formulated as optimization problems. The majority of real-world optimization problems involve several and con icting objectives. Such problems are called multiobjective optimization problems (MOPs). The presence of multiple con icting objectives that have to be simultaneously optimized gives rise to a set of trade-o solutions, known as the Pareto optimal set. Since this set of solutions is crucial for e ective decision-making, which generally aims to improve the human condition, the availability of e cient optimization methods becomes indispensable. Recently, evolutionary algorithms (EAs) have become popular and successful in approximating the Pareto set. The population-based nature is the main feature that makes them especially attractive for dealing with MOPs. Due to the presence of two search spaces, operators able to e ciently perform the search in both the decision and objective spaces are required. Despite the wide variety of existing methods, a lot of open research issues in the design of multiobjective evolutionary algorithms (MOEAs) remains. This thesis investigates the use of evolutionary algorithms for solving multiobjective optimization problems. Innovative algorithms are developed studying new techniques for performing the search either in the decision or the objective space. Concerning the search in the decision space, the focus is on the combinations of traditional and evolutionary optimization methods. An issue related to the search in the objective space is studied in the context of many-objective optimization. Application of evolutionary algorithms is addressed solving two di erent real-world problems, which are modeled using multiobjective approaches. The problems arise from the mathematical modelling of the dengue disease transmission and a wastewater treatment plant design. The obtained results clearly show that multiobjective modelling is an e ective approach. The success in solving these challenging optimization problems highlights the practical relevance and robustness of the developed algorithms.Muitos problemas matemáticos que surgem nas diversas áreas da atividade humana podem ser formulados como problemas de otimização. A maioria dos problemas do mundo real envolve vários objetivos conflituosos. Tais problemas chamam-se problemas de otimização multiobjetivo. A presença de vários objetivos conflituosos, que têm de ser otimizados em simultâneo, dá origem a um conjunto de soluções de compromisso, conhecido como conjunto de soluções ótimas de Pareto. Uma vez que este conjunto de soluções é fundamental para uma tomada de decisão eficaz, cujo objetivo em geral é melhorar a condição humana, o desenvolvimento de métodos de otimização eficientes torna-se indispensável. Recentemente, os algoritmos evolucionários tornaram-se populares e bem-sucedidos na aproximação do conjunto de Pareto. A natureza populacional é a principal característica que os torna especialmente atraentes para lidar com problemas de otimização multiobjetivo. Devido à presença de dois espaços de procura, operadores capazes de realizar a procura de forma eficiente, tanto no espaço de decisão como no espaço dos objetivos, são necessários. Apesar da grande variedade de métodos existentes, várias questões de investigação permanecem em aberto na área do desenvolvimento de algoritmos evolucionários multiobjetivo. Esta tese investiga o uso de algoritmos evolucionários para a resolução de problemas de otimização multiobjetivo. São desenvolvidos algoritmos inovadores que estudam novas técnicas de procura, quer no espaço de decisão, quer no espaço dos objetivos. No que diz respeito à procura no espaço de decisão, o foco está na combinação de métodos de otimização tradicionais com algoritmos evolucionários. A questão relacionada com a procura no espaço dos objetivos é desenvolvida no contexto da otimização com muitos objetivos. A aplicação dos algoritmos evolucionários é abordada resolvendo dois problemas reais, que são modelados utilizando abordagens multiobjectivo. Os problemas resultam da modelação matemática da transmissão da doença do dengue e do desenho ótimo de estações de tratamento de águas residuais. O sucesso na resolução destes problemas de otimização constitui um desafio e destaca a relevância prática e robustez dos algoritmos desenvolvidos

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Nonsmooth and derivative-free optimization based hybrid methods and applications

"In this thesis, we develop hybrid methods for solving global and in particular, nonsmooth optimization problems. Hybrid methods are becoming more popular in global optimization since they allow to apply powerful smooth optimization techniques to solve global optimization problems. Such methods are able to efficiently solve global optimization problems with large number of variables. To date global search algorithms have been mainly applied to improve global search properties of the local search methods (including smooth optimization algorithms). In this thesis we apply rather different strategy to design hybrid methods. We use local search algorithms to improve the efficiency of global search methods. The thesis consists of two parts. In the first part we describe hybrid algorithms and in the second part we consider their various applications." -- taken from Abstract.Operational Research and Cybernetic

Trust-region based adaptive radial basis function algorithm for global optimization of expensive constrained black-box problems

It has been a very challenging task to develop efficient and robust techniques to solve real-world engineering optimization problems due to the unknown function properties, complex constraints and severely limited computational budget. To address this issue, TARBF algorithm (trust-region based adaptive radial basis function interpolation) for solving expensive constrained black-box optimization problems is proposed in this paper. The approach successfully decomposes the original optimization problem into a sequence of sub-problems approximated by radial basis functions in a series of trust regions. Then, the solution of each sub-problem becomes the starting point for the next iteration. According to the values of objective and constraint functions, an effective online normalization technique is further developed to adaptively improve the model accuracy in the trust region, where the surrogate is updated iteratively. Averagely, TARBF has the ability to robustly solve the 21 G-problems (CEC’2006) and 4 engineering problems within 535:69 and 234:44 function evaluations, respectively. The comparison results with other state-of-the-art metamodel-based algorithms prove that TARBF is a convergent, efficient and accurate paradigm. Moreover, the sophisticated trust region strategy developed in TARBF, which is a major contribution to the field of the efficient constrained optimization, has the capability to facilitate an effective balance of exploration and exploitation for solving constrained black-box optimization problems