DANTE - The combination between an ant colony optimization algorithm and a depth search method

The ε-DANTE method is an hybrid meta-heuristic. In combines the evolutionary Ant Colony Optimization (ACO) algorithms with a limited Depth Search. This Depth Search is based in the pheromone trails used by the ACO, which allows it to be oriented to the more promising areas of the search space. Some results are presented for the multiple objective k-Degree Spanning Trees problem, proving the effectiveness of the method when compared with other already tested evolutionary methods. © 2008 IEEE

Ant colony optimization routing mechanisms with bandwidth sensing

The study and understanding of the social behavior of insects has contributed to the definition of some algorithms that are capable of solving several types of optimization problems. In 1997 Di Caro and Dorigo developed the first routing algorithm for wired networks, called AntNet, using an approach which was inspired in the behavior of ant colonies. At each node, AntNet, similar to others Ant Colony Optimization (ACO) based algorithms, forward ants based in the amount of pheromones present in the links and in response to the node's queue lengths. In this paper, an adaptation of the e-DANTE algorithm for discrete problems, as an IP based routing mechanism, was implemented. We also propose the inclusion of a new parameter for the computation of paths for both the AntNet and the newly proposed algorithm: the available bandwith. Those methods were tested in ns-2 using two dense network architectures and their efficiency is compared with the original AntNet and a Link-State routing algorithm, when considering the transmission of competing traffic flows between distinct nodes. © 2011 IEEE

Population-based algorithms for improved history matching and uncertainty quantification of Petroleum reservoirs

In modern field management practices, there are two important steps that shed light on a multimillion dollar investment. The first step is history matching where the simulation model is calibrated to reproduce the historical observations from the field. In this inverse problem, different geological and petrophysical properties may provide equally good history matches. Such diverse models are likely to show different production behaviors in future. This ties the history matching with the second step, uncertainty quantification of predictions. Multiple history matched models are essential for a realistic uncertainty estimate of the future field behavior. These two steps facilitate decision making and have a direct impact on technical and financial performance of oil and gas companies. Population-based optimization algorithms have been recently enjoyed growing popularity for solving engineering problems. Population-based systems work with a group of individuals that cooperate and communicate to accomplish a task that is normally beyond the capabilities of each individual. These individuals are deployed with the aim to solve the problem with maximum efficiency. This thesis introduces the application of two novel population-based algorithms for history matching and uncertainty quantification of petroleum reservoir models. Ant colony optimization and differential evolution algorithms are used to search the space of parameters to find multiple history matched models and, using a Bayesian framework, the posterior probability of the models are evaluated for prediction of reservoir performance. It is demonstrated that by bringing latest developments in computer science such as ant colony, differential evolution and multiobjective optimization, we can improve the history matching and uncertainty quantification frameworks. This thesis provides insights into performance of these algorithms in history matching and prediction and develops an understanding of their tuning parameters. The research also brings a comparative study of these methods with a benchmark technique called Neighbourhood Algorithms. This comparison reveals the superiority of the proposed methodologies in various areas such as computational efficiency and match quality

Cultural heritage visits supported on visitors' preferences and mobile devices

Monuments, museums and cities are great places to feel and experience neat and interesting things. But cultural heritage is experienced differently by different visitors. The more erudite may know beforehand what they intend to explore, while the least literate usually know and are capable of expressing some of their preferences but do not exactly realize what to see and explore. This paper proposes the use of a mobile application to set an itinerary where you can move at your own pace and, at the same time, have all the complementary information you need about each of the points of interest. The application is designed in face of an adaptive user interface where the routing and augmented reality are connected to acknowledge the needs of different user categories, such as elders, kids, experts or general usersPortuguese Foundation for Science and Technology (FCT)Portuguese Foundation for Science and Technology [UID/EEA/50009/2013, UID/SOC/04020/2013]CRESC ALGARVE 2020, PORTUGAL 2020 [3322]FEDER European Commissioninfo:eu-repo/semantics/publishedVersio

Modelling of interactions between rail service and travel demand: a passenger-oriented analysis

The proposed research is situated in the field of design, management and optimisation in railway network operations. Rail transport has in its favour several specific features which make it a key factor in public transport management, above all in high-density contexts. Indeed, such a system is environmentally friendly (reduced pollutant emissions), high-performing (high travel speeds and low values of headways), competitive (low unitary costs per seat-km or carried passenger-km) and presents a high degree of adaptability to intermodality. However, it manifests high vulnerability in the case of breakdowns. This occurs because a faulty convoy cannot be easily overtaken and, sometimes, cannot be easily removed from the line, especially in the case of isolated systems (i.e. systems which are not integrated into an effective network) or when a breakdown occurs on open tracks. Thus, re-establishing ordinary operational conditions may require excessive amounts of time and, as a consequence, an inevitable increase in inconvenience (user generalised cost) for passengers, who might decide to abandon the system or, if already on board, to exclude the railway system from their choice set for the future. It follows that developing appropriate techniques and decision support tools for optimising rail system management, both in ordinary and disruption conditions, would consent a clear influence of the modal split in favour of public transport and, therefore, encourage an important reduction in the externalities caused by the use of private transport, such as air and noise pollution, traffic congestion and accidents, bringing clear benefits to the quality of life for both transport users and non-users (i.e. individuals who are not system users). Managing to model such a complex context, based on numerous interactions among the various components (i.e. infrastructure, signalling system, rolling stock and timetables) is no mean feat. Moreover, in many cases, a fundamental element, which is the inclusion of the modelling of travel demand features in the simulation of railway operations, is neglected. Railway transport, just as any other transport system, is not finalised to itself, but its task is to move people or goods around, and, therefore, a realistic and accurate cost-benefit analysis cannot ignore involved flows features. In particular, considering travel demand into the analysis framework presents a two-sided effect. Primarily, it leads to introduce elements such as convoy capacity constraints and the assessment of dwell times as flow-dependent factors which make the simulation as close as possible to the reality. Specifically, the former allows to take into account the eventuality that not all passengers can board the first arriving train, but only a part of them, due to overcrowded conditions, with a consequent increase in waiting times. Due consideration of this factor is fundamental because, if it were to be repeated, it would make a further contribution to passengers’ discontent. While, as regards the estimate of dwell times on the basis of flows, it becomes fundamental in the planning phase. In fact, estimating dwell times as fixed values, ideally equal for all runs and all stations, can induce differences between actual and planned operations, with a subsequent deterioration in system performance. Thus, neglecting these aspects, above all in crowded contexts, would render the simulation distorted, both in terms of costs and benefits. The second aspect, on the other hand, concerns the correct assessment of effects of the strategies put in place, both in planning phases (strategic decisions such as the realisation of a new infrastructure, the improvement of the current signalling system or the purchasing of new rolling stock) and in operational phases (operational decisions such as the definition of intervention strategies for addressing disruption conditions). In fact, in the management of failures, to date, there are operational procedures which are based on hypothetical times for re-establishing ordinary conditions, estimated by the train driver or by the staff of the operation centre, who, generally, tend to minimise the impact exclusively from the company’s point of view (minimisation of operational costs), rather than from the standpoint of passengers. Additionally, in the definition of intervention strategies, passenger flow and its variation in time (different temporal intervals) and space (different points in the railway network) are rarely considered. It appears obvious, therefore, how the proposed re-examination of the dispatching and rescheduling tasks in a passenger-orientated perspective, should be accompanied by the development of estimation and forecasting techniques for travel demand, aimed at correctly taking into account the peculiarities of the railway system; as well as by the generation of ad-hoc tools designed to simulate the behaviour of passengers in the various phases of the trip (turnstile access, transfer from the turnstiles to the platform, waiting on platform, boarding and alighting process, etc.). The latest workstream in this present study concerns the analysis of the energy problems associated to rail transport. This is closely linked to what has so far been described. Indeed, in order to implement proper energy saving policies, it is, above all, necessary to obtain a reliable estimate of the involved operational times (recovery times, inversion times, buffer times, etc.). Moreover, as the adoption of eco-driving strategies generates an increase in passenger travel times, with everything that this involves, it is important to investigate the trade-off between energy efficiency and increase in user generalised costs. Within this framework, the present study aims at providing a DSS (Decision Support System) for all phases of planning and management of rail transport systems, from that of timetabling to dispatching and rescheduling, also considering space-time travel demand variability as well as the definition of suitable energy-saving policies, by adopting a passenger-orientated perspective

A Hybrid Meta-Heuristic Feature Selection Method Using Golden Ratio and Equilibrium Optimization Algorithms for Speech Emotion Recognition

Speech is the most important media of expressing emotions for human beings. Thus, it has often been an area of interest to understand the emotion of a person out of his/her speech by using the intelligence of the computing devices. Traditional machine learning techniques are very much popular in accomplishing such tasks. To provide a less expensive computational model for emotion classification through speech analysis, we propose a meta-heuristic feature selection (FS) method using a hybrid of Golden Ratio Optimization (GRO) and Equilibrium Optimization (EO) algorithms, which we have named as Golden Ratio based Equilibrium Optimization (GREO) algorithm. The optimally selected features by the model are fed to the XGBoost classifier. Linear Predictive Coding (LPC) and Linear Prediction Cepstral Coefficients (LPCC) based features are considered as the input here, and these are optimized by using the proposed GREO algorithm. We have achieved impressive recognition accuracies of 97.31% and 98.46% on two standard datasets namely, SAVEE and EmoDB respectively. The proposed FS model is also found to perform better than their constituent algorithms as well as many well-known optimization algorithms used for FS in the past. Source code of the present work is made available at: https://github.com/arijitdey1/Hybrid-GREO

Smart augmented reality application for enhanced museum experience

Dissertação de mestrado, Engenharia Eléctrica e Electrónica, Instituto Superior de Engenharia, Universidade do Algarve, 2017Museums’ collections can be almost endless, with countless objects, making it challenging to choose which ones to visit and appreciate. When a user enters a museum he usually encounters a guide or a predefined route to aid him, which more often than not is not suitable for his necessities and preferences. This dissertation focus on developing a mobile augmented reality framework and intelligent multiplatform application, that can be used as a museum guide and navigation helper. The work was divided into 3 main modules: (a) an intelligent routing system, (b) an adaptive user interface, and (c) an image recognition and augmented reality framework. Also presented is the integration of the above modules in an application. The first module, (a) intelligent routing system module, poses a solution for a "typical" museum problem. Museums routes do not take into account the physical, moral or psychological limitations of a user and/or their preferences. It resembles the traveling salesman problem where a route is calculated, only visiting once each point of interest, diminishing as much as possible the "walking" time, and extending the time spent admiring the museum’s objects. An Ant Colony Optimization algorithm was used to handle the calculations and compute an optimal walk, rendering the user’s preferences and limitations. This problem was formulated as a multi-criteria optimization problem. Also focusing on adapting the application for the user, (b) an adaptive user interface was developed, which adapts the application’s user interface on-thefly, according to the user’s preferences and conditions. This module is built upon a modular card system which is divided into structure and contents. It relies on a modular system in the sense that a complex interface can be divided into simpler and more manageable sub-modules, which can be used in other parts of the application or even in a completely different one. On an ideal application, each user would have a distinct interface/structure and contents. Nonetheless, different users could share the same interface structure only modifying the contents. The adaptive user interface is capable of (as the name implies) adapting itself to the user, either by changing both its structure and contents or only the contents displayed to the user. Regarding the augmented reality module (c), a mobile image recognition and tracking framework (MIRAR) was developed. The purpose of this framework is to recognize and track the innumerous objects of the museum in a mobile device. This framework is a marker-based augmented reality framework and even though the recognition happens on the client (mobile device) a server is required to keep the packaged markers accessible for the clients. These markers are preprocessed in the server and grouped by section. As the user navigates through the museum, an indoor beacon location system calculates his current position that is transmitted to the server which, in turn, sends the correct markers for that section to the mobile device. Finally, the integration of the above modules is presented in an alpha version of a mobile application, as well as tests and results for each module.Os espólios dos museus contêm inúmeros objetos, tornando-se difícil escolher quais as obras a visitar e apreciar. Quando um utilizador entra num museu, geralmente depara-se com um guia e/ou rotas predefinidas, que frequentemente não são adequadas às suas necessidades e preferências. Esta dissertação foca o desenvolvimento de uma framework de realidade aumentada e de uma aplicação inteligente para multiplataforma, que pode ser usada como guia de museu e auxiliar de navegação. O trabalho foi dividido em 3 módulos principais: (a) um sistema de cálculo de rotas inteligentes, (b) uma interface adaptativa de utilizador e (c) uma framework de reconhecimento de imagens com realidade aumentada. Também é apresentada a integração dos módulos acima mencionados numa aplicação. O primeiro módulo, o (a) módulo do sistema de cálculo de rotas inteligentes, representa uma solução para um problema "comum" dos museus: as rotas existentes nos museus não tomam em consideração as limitações físicas, morais ou psicológicas do utilizador e/ou suas preferências. O problema em causa consiste em calcular uma rota, visitando uma e só uma vez cada ponto de interesse existente (mas não necessariamente todos os disponíveis no museu), percorrendo o menor caminho possível, e estendendo ao máximo o tempo de visita aos objetos do museu. Neste caso, foi usada uma adaptação de um algoritmo de Ant Colony Optimization para calcular o melhor caminho, considerando as preferências e limitações do utilizador. Este problema foi formulado como um problema de otimização multi-critério. Ainda nesta temática, (b) foi desenvolvida uma interface adaptativa de utilizador, que se ajusta de acordo com as preferências e condições deste. Este módulo é constituído por um sistema modular de cartões os quais são dividido em estrutura e conteúdos. Foi escolhido este sistema pois permite que uma interface complexa possa ser dividida em sub-módulos mais simples, que podem ser usados noutras partes da aplicação ou mesmo noutra aplicação completamente distinta. Idealmente, cada utilizador teria uma interface com estrutura e conteúdos distintos. No entanto, diferentes utilizadores podem partilhar a mesma estrutura/layout apenas modificando o conteúdo apresentado. Assim, este modulo permite criar facilmente diferentes interfaces para os diferentes utilizadores, quer modificando apenas os conteúdos apresentados ou também toda a sua estrutura. Relativamente ao módulo de realidade aumentada (c), foi desenvolvido uma framework de reconhecimento de imagens com realidade aumentada (MIRAR - Mobile Image Recognition and Augmented Reality) para dispositivos móveis. O objetivo deste módulo é reconhecer e fazer o restreamento dos objetos do museu recorrendo ao dispositivo móvel do utilizador. A framework desenvolvida é baseada no reconhecimento de marcadores e apesar deste acontecer no cliente (dispositivo móvel) é necessário um servidor para guardar os marcadores préprocessados. Estes são, posteriormente, acedidos pelos dispositivos móveis à medida que os utilizadores navegam pelo museu. A localização do utilizador dentro do é calculada através de um sistema de beacons bluetooth a qual é transmitida para o servidor, que, por sua vez, envia os marcadores correspondentes a essa localização para o dispositivo do utilizador. Finalmente, a integração dos módulos supra-mencionados é apresentada numa versão alfa da aplicação móvel, bem como testes e resultados para cada módulo

Iterative restricted space search : a solving approach based on hybridization

Face à la complexité qui caractérise les problèmes d'optimisation de grande taille l'exploration complète de l'espace des solutions devient rapidement un objectif inaccessible. En effet, à mesure que la taille des problèmes augmente, des méthodes de solution de plus en plus sophistiquées sont exigées afin d'assurer un certain niveau d 'efficacité. Ceci a amené une grande partie de la communauté scientifique vers le développement d'outils spécifiques pour la résolution de problèmes de grande taille tels que les méthodes hybrides. Cependant, malgré les efforts consentis dans le développement d'approches hybrides, la majorité des travaux se sont concentrés sur l'adaptation de deux ou plusieurs méthodes spécifiques, en compensant les points faibles des unes par les points forts des autres ou bien en les adaptant afin de collaborer ensemble. Au meilleur de notre connaissance, aucun travail à date n'à été effectué pour développer un cadre conceptuel pour la résolution efficace de problèmes d'optimisation de grande taille, qui soit à la fois flexible, basé sur l'échange d'information et indépendant des méthodes qui le composent. L'objectif de cette thèse est d'explorer cette avenue de recherche en proposant un cadre conceptuel pour les méthodes hybrides, intitulé la recherche itérative de l'espace restreint, ±Iterative Restricted Space Search (IRSS)>>, dont, la principale idée est la définition et l'exploration successives de régions restreintes de l'espace de solutions. Ces régions, qui contiennent de bonnes solutions et qui sont assez petites pour être complètement explorées, sont appelées espaces restreints "Restricted Spaces (RS)". Ainsi, l'IRSS est une approche de solution générique, basée sur l'interaction de deux phases algorithmiques ayant des objectifs complémentaires. La première phase consiste à identifier une région restreinte intéressante et la deuxième phase consiste à l'explorer. Le schéma hybride de l'approche de solution permet d'alterner entre les deux phases pour un nombre fixe d'itérations ou jusqu'à l'atteinte d'une certaine limite de temps. Les concepts clés associées au développement de ce cadre conceptuel et leur validation seront introduits et validés graduellement dans cette thèse. Ils sont présentés de manière à permettre au lecteur de comprendre les problèmes que nous avons rencontrés en cours de développement et comment les solutions ont été conçues et implémentées. À cette fin, la thèse a été divisée en quatre parties. La première est consacrée à la synthèse de l'état de l'art dans le domaine de recherche sur les méthodes hybrides. Elle présente les principales approches hybrides développées et leurs applications. Une brève description des approches utilisant le concept de restriction d'espace est aussi présentée dans cette partie. La deuxième partie présente les concepts clés de ce cadre conceptuel. Il s'agit du processus d'identification des régions restreintes et des deux phases de recherche. Ces concepts sont mis en oeuvre dans un schéma hybride heuristique et méthode exacte. L'approche a été appliquée à un problème d'ordonnancement avec deux niveaux de décision, relié au contexte des pâtes et papier: "Pulp Production Scheduling Problem". La troisième partie a permit d'approfondir les concepts développés et ajuster les limitations identifiées dans la deuxième partie, en proposant une recherche itérative appliquée pour l'exploration de RS de grande taille et une structure en arbre binaire pour l'exploration de plusieurs RS. Cette structure a l'avantage d'éviter l'exploration d 'un espace déjà exploré précédemment tout en assurant une diversification naturelle à la méthode. Cette extension de la méthode a été testée sur un problème de localisation et d'allocation en utilisant un schéma d'hybridation heuristique-exact de manière itérative. La quatrième partie généralise les concepts préalablement développés et conçoit un cadre général qui est flexible, indépendant des méthodes utilisées et basé sur un échange d'informations entre les phases. Ce cadre a l'avantage d'être général et pourrait être appliqué à une large gamme de problèmes