In this work, we present a way to extend Ant Colony Optimization (ACO), so that it can be applied to both continuous and mixed-variable optimization problems. We demonstrate, first, how ACO may be extended to continuous domains. We describe the algorithm proposed, discuss the different design decisions made, and we position it among other metaheuristics. Following this, we present the results of numerous simulations and testing. We compare the results obtained by the proposed algorithm on typical benchmark problems with those obtained by other methods used for tackling continuous optimization problems in the literature. Finally, we investigate how our algorithm performs on a real-world problem coming from the medical field—we use our algorithm for training neural network used for pattern classification in disease recognition. Following an extensive analysis of the performance of ACO extended to continuous domains, we present how it may be further adapted to handle both continuous and discrete variables simultaneously. We thus introduce the first native mixed-variable version of an ACO algorithm. Then, we analyze and compare the performance of both continuous and mixed-variable ACO algorithms on different benchmark problems from the literature. Through the research performed, we gain some insight into the relationship between the formulation of mixed-variable problems, and the best methods to tackle them. Furthermore, we demonstrate that the performance of ACO on various real-world mixed-variable optimization problems coming from the mechanical engineering field is comparable to the state of the art.

The aim of this thesis is to conduct performance investigation of a combined system of Network Coding (NC) technique with Ant-Colony (ACO) routing protocol. This research analyses the impact of several workload characteristics, on system performance. Network coding is a significant key development of information transmission and processing. Network coding enhances the performance of multicast by employing encoding operations at intermediate nodes. Two steps should realize while using network coding in multicast communication: determining appropriate transmission paths from source to multi-receivers and using the suitable coding scheme. Intermediate nodes would combine several packets and relay them as a single packet. Although network coding can make a network achieve the maximum multicast rate, it always brings additional overheads. It is necessary to minimize unneeded overhead by using an optimization technique. On other hand, Ant Colony Optimization can be transformed into useful technique that seeks imitate the ant’s behaviour in finding the shortest path to its destination using quantities of pheromone that is left by former ants as guidance, so by using the same concept of the communication network environment, shorter paths can be formulated. The simulation results show that the resultant system considerably improves the performance of the network, by combining Ant Colony Optimization with network coding. 25% improvement in the bandwidth consumption can be achieved in comparison with conventional routing protocols. Additionally simulation results indicate that the proposed algorithm can decrease the computation time of system by a factor of 20%.

Traditional clustering algorithms have different criteria and biases, and there is no single algorithm that can be the best solution for a wide range of data sets. This problem often presents a significant obstacle to analysts in revealing meaningful information buried among the huge amount of data. Ensemble Clustering has been proposed as a way to avoid the biases and improve the accuracy of clustering. The difficulty in developing Ensemble Clustering methods is to combine external information (provided by input clusterings) with internal information (i.e. characteristics of given data) effectively to improve the accuracy of clustering. The work presented in this thesis focuses on enhancing the clustering accuracy of Ensemble Clustering by employing heuristic optimisation techniques to achieve a robust combination of relevant information during the consensus clustering stage. Two novel heuristic optimisation-based Ensemble Clustering methods, Multi-Optimisation Consensus Clustering (MOCC) and K-Ants Consensus Clustering (KACC), are developed and introduced in this thesis. These methods utilise two heuristic optimisation algorithms (Simulated Annealing and Ant Colony Optimisation) for their Ensemble Clustering frameworks, and have been proved to outperform other methods in the area. The extensive experimental results, together with a detailed analysis, will be presented in this thesis.

In this thesis, I introduce two new algorithms: Ant Brood Clustering-Intelligent Ants (ABC-INTE) and Ant Brood Clustering-Polymorphic Ants (ABC-POLY) for the graph clustering problem. ABC-INTE uses techniques such as hopping ants, relaxed drop function, ants with memories, stagnation control, and addition of k-means cluster retrieval process, as an improvement of the basic ABC-KLS algorithm. ABC-POLY uses two types of ants, inspired by the division of labour between the major and minor ants in Pheidole genus, as an improvement of ABC-INTE. For comparison purpose, I also implement MMAS, an ACO clustering algorithm. When tested on the benchmark networks, ABC-POLY outperforms or achieves the same modularity values as MMAS and ABC-INTE on 7 out of 10 networks and is robust against different graphs. In practice, the speed of ABC-POLY is at least 10 times faster than MMAS, making it a scalable algorithm compared to MMAS. ABC-POLY also outputs a direct visual representation of the natural clusters on the graph that is appealing to human observation. This thesis opens an interesting research topic to apply polymorphic ants for graph clustering in the ABC-POLY algorithm. The distributive and self-organization nature of ABC-POLY makes it a candidate for analyzing clusters in more complex and dynamic graphs. / May 2016

The Ant Colony Optimization belongs into the metaheuristic methods category and it has been developing quite recently. So far it has shown its capabalities to over-perform other metaheuristic methods in quality of the solutions. This work brings analysis of the possible applications of the method on the classical optimization combinatorial problems -- traveling salesman problem, vehicle routing problem, knapsack problem, generalized assignment problem and maximal clique problem. It also deals with the practical experiments with application on several optimization problems and analysis of the time and memory complexity of such algorithms. The last part of the work is dedicated to the possibility of parallelization of the algorithm, which was result of the application of the ACO method on the traveling salesman problem. It brings analysis of the crucial operations and data synchronization issues, as well as practical example and demonstration of the parallelized version of the algorithm.

Visando contribuir com o estado-da-arte de sistemas bioinspirados em formigas na robóotica, neste trabalho é abordado o problema do controle de um grupo de robôs para a solução coletiva das tarefas de exploração do ambiente e localização de objetos. Para isso, são utilizados algoritmos inspirados em colônias de formigas. O objetivo deste trabalho, portanto, é o desenvolvimento de um sistema de controle de navegação baseado em colônia de formigas para um time de robôs, de maneira que os robôs resolvam esses problemas utilizando estratégias de controle individuais e simples. Esse sistema tem como base a utilização de marcadores ou feromônios artificiais, que podem ser depositados pelos robôs para marcar determinadas posiçôes do ambiente / Aiming to advance the state-of-the-art of ant bioinspired systems in robotic applications, in this work we study the problem of controling a group of robots for solving colective tasks on environment exploration and object localization. To this end, we used algorithms inspired in ant colonies. Therefore, the objective of this work is to develop a navigation control system based on ant colony can solve the problems using simple control strategies. This system uses marks or artificial pheromones that can be released by the robots to mark specific positions in the environment

As estratégias de mapeamento utilizando múltiplos robôs móveis possuem uma série de vantagens quando comparadas àquelas estratégias baseadas em um único robô. As principais vantagens que podem ser elucidadas são: flexibilidade, ganho de informação e redução do tempo de construção do mapa do ambiente. No presente trabalho, um método de integração de mapas locais é proposto baseado em observações inter-robôs, considerando uma nova abordagem para a exploração do ambiente. Tal abordagem é conhecida como Sistema de Vigilância baseado na Modificação do Sistema Colônias de Formigas, ou IAS-SS. A estratégia IAS-SS é inspirada em mecanismos biológicos que definem a organização social de sistemas de enxames. Especificamente, esta estratégia é baseada em uma modificação do tradicional algoritmo de otimização por colônias de formiga. A principal contribuição do presente trabalho é a adaptação de um modelo de compartilhamento de informações utilizado em redes de sensores móveis, adaptando o mesmo para tarefas de mapeamento. Outra importante contribuição é a colaboração entre o método proposto de integração de mapas e a estratégia de coordenação de múltiplos robôs baseada na teoria de colônias de formigas. Tal colaboração permite o desenvolvimento de uma abordagem de exploração que emprega um mecanismo não físico para depósito e detecção de feromônios em ambientes reais por meio da elaboração do conceito de feromônios virtuais integrados. Resultados obtidos em simulação demonstram que o método de integração de mapas é eficiente, de modo que os ensaios experimentais foram realizados considerando-se um número variável de robôs móveis durante o processo de exploração de ambientes internos com diferentes formas e estruturas. Os resultados obtidos com os diversos experimentos realizados confirmam que o processo de integração é efetivo e adequado para executar o mapeamento do ambiente durante tarefas de exploração e vigilância do mesmo / The multiple robot map building strategies have several advantages when compared to strategies based on a single robot, in terms of flexibility, gain of information and reduction of map building time. In this work, a local map integration method is proposed based on the inter-robot observations, considering a recent approach for the environment exploration. This approach is based on the Inverse Ant System-Based Surveillance System strategy, called IASSS. The IAS-SS strategy is inspired on biological mechanisms that define the social organization of swarm systems. Specifically, it is based on a modified version of the known ant colony algorithm. The main contribution of this work is the fit of an information sharing model used in an mobile sensor network, adapting the method for mapping tasks. Another important contribution is the collaboration between the local map integration method and the multiple robot coordination strategy based on ant colony theory. Through this collaboration it is possible to develop an approach that uses a mechanism for controlling the access to pheromones in real environments. Such mechanism is based on the integrated virtual pheromones concept. Simulation results show that the map integration method is efficient, the trials are performed considering a variable number of robots and environments with different structures. Results obtained from several experiments confirm that the integration process is effective and suitable to execute mapping during the exploration task

As redes de sensores sem fio estão presentes nos mais diversos setores, formando um conjunto de elementos colaborativos que realizam o transporte da informação em cenários urbanos, agricultura de precisão, saúde e automação industrial. Sua utilização enfrenta desafios como necessidade constante de adaptações a alterações de topologia, baixas taxas de comunicação e uso eficiente de energia. Dessa forma, as aplicações dessas redes precisam suportar estas características dinamicamente. Esta tese propõe, então, um novo protocolo de roteamento: o HOFACO (Hybrid Objective Function based on Ant Colony Optimization), com base no funcionamento do padrão RPL (Routing Protocol for Low Power and Lossy Networks) aplicado à eficiência energética. Este protocolo proposto preserva as interfaces já consolidadas de roteamento e faz uso de uma informação heurística baseada em swarm intelligence (inteligência de enxames), por meio do uso de agentes - colônia de formigas -, para a composição híbrida de métricas utilizadas na avaliação do rank durante a construção de topologias. Esta proposta foi validada por meio de implementações em simulações e dispositivos reais em duas plataformas distintas: o Contiki e o OpenWSN. O processo de validação utilizou parâmetros como número de mensagens de controle, alterações de configurações topológicas, latência da rede e taxa de entrega de pacotes comparando-se a proposta a soluções tradicionais. Os resultados obtidos demonstraram um funcionamento compatível com estes parâmetros e uma melhoria em torno de 7,6% no sentido de redução do tempo de estabilização da rede. Adicionalmente, foi feito um teste comparativo de consumo de energia, evidenciando uma melhoria em termos de eficiência enérgica de até 20,9% da proposta com relação a protocolos tradicionais. / The wireless sensor networks are present in the most diverse sectors, forming a set of collaborative elements that perform the transport of information in urban scenarios, precision agriculture, health and industrial automation. Its use faces challenges as a constant need for adaptations to topology changes, low communication rates and efficient energy use. In this way, the applications of these networks must support these characteristics dynamically. This thesis proposes, therefore, a new routing protocol: the HOFACO (Hybrid Objective Function based on Ant Colony Optimization), based on the RPL (Routing Protocol for Low Power and Lossy Networks) applied to energy efficiency. This proposed protocol preserves the already consolidated routing interfaces and makes use of heuristic information based on swarm intelligence, using ant colony agents for the hybrid composition of metrics used in rank evaluation during the construction of topologies. This proposal was validated through implementations in simulations and real devices in two different platforms: Contiki and OpenWSN. The validation process used parameters such as number of control messages, changes of topological configurations, network latency and packet delivery rate, comparing the proposal to traditional solutions. The results showed a functioning compatible with these parameters and an improvement around 7.6% in the sense of reducing the time of stabilization of the network. In addition, a comparative test of energy consumption was made, evidencing an improvement in terms of energetic efficiency up to 20.9% of the proposal with respect to traditional protocols.

This research identifies and investigates major issues in inducing accurate and comprehensible fuzzy rules from datasets. A review of the current literature on fuzzy rulebase induction uncovers two significant issues: A. There is a tradeoff between inducing accurate fuzzy rules and inducing comprehensible fuzzy rules; and, B. A common strategy for the induction of fuzzy rulebases, that of iterative rule learning where the rules are generated one by one and independently of each other, may not be an optimal one. FRANTIC, a system that provides a framework for exploring the claims above is developed. At the core lies a mechanism for creating individual fuzzy rules. This is based on a significantly modified social insect-inspired heuristic for combinatorial optimisation -- Ant Colony Optimisation. The rule discovery mechanism is utilised in two very different strategies for the induction of a complete fuzzy rulebase: 1. The first follows the common iterative rule learning approach for the induction of crisp and fuzzy rules; 2. The second has been designed during this research explicitly for the induction of a fuzzy rulebase, and generates all rules in parallel. Both strategies have been tested on a number of classification problems, including medical diagnosis and industrial plant fault detection, and compared against other crisp or fuzzy induction algorithms that use more well-established approaches. The results challenge statement A above, by presenting evidence to show that one criterion need not be met at the expense of the other. This research also uncovers the cost that is paid -- that of computational expenditure -- and makes concrete suggestions on how this may be resolved. With regards to statement B, until now little or no evidence has been put forward to support or disprove the claim. The results of this research indicate that definite advantages are offered by the second simultaneous strategy, that are not offered by the iterative one. These benefits include improved accuracy over a wide range of values for several key system parameters. However, both approaches also fare well when compared to other learning algorithms. This latter fact is due to the rule discovery mechanism itself -- the adapted Ant Colony Optimisation algorithm -- which affords several additional advantages. These include a simple mechanism within the rule construction process that enables it to cope with datasets that have an imbalanced distribution between the classes, and another for controlling the amount of fit to the training data. In addition, several system parameters have been designed to be semi-autonomous so as to avoid unnecessary user intervention, and in future work the social insect metaphor may be exploited and extended further to enable it to deal with industrial-strength data mining issues involving large volumes of data, and distributed and/or heterogeneous databases.

In this thesis, a novel framework for dynamic congestion control has been proposed. The study is about the congestion control in broadband communication networks. Congestion results when demand temporarily exceeds capacity and leads to severe degradation of Quality of Service (QoS) and possibly loss of traffic. Since traffic is stochastic in nature, high demand may arise anywhere in a network and possibly causing congestion. There are different ways to mitigate the effects of congestion, by rerouting, by aggregation to take advantage of statistical multiplexing, and by discarding too demanding traffic, which is known as admission control. This thesis will try to accommodate as much traffic as possible, and study the effect of routing and aggregation on a rather general mix of traffic types. Software Defined Networking (SDN) and Network Function Virtualization (NFV) are concepts that allow for dynamic configuration of network resources by decoupling control from payload data and allocation of network functions to the most suitable physical node. This allows implementation of a centralised control that takes the state of the entire network into account and configures nodes dynamically to avoid congestion. Assumes that node controls can be expressed in commands supported by OpenFlow v1.3. Due to state dependencies in space and time, the network dynamics are very complex, and resort to a simulation approach. The load in the network depends on many factors, such as traffic characteristics and the traffic matrix, topology and node capacities. To be able to study the impact of control functions, some parts of the environment is fixed, such as the topology and the node capacities, and statistically average the traffic distribution in the network by randomly generated traffic matrices. The traffic consists of approximately equal intensity of smooth, bursty and long memory traffic. By designing an algorithm that route traffic and configure queue resources so that delay is minimised, this thesis chooses the delay to be the optimisation parameter because it is additive and real-time applications are delay sensitive. The optimisation being studied both with respect to total end-to-end delay and maximum end-to-end delay. The delay is used as link weights and paths are determined by Dijkstra's algorithm. Furthermore, nodes are configured to serve the traffic optimally which in turn depends on the routing. The proposed algorithm is a fixed-point system of equations that iteratively evaluates routing - aggregation - delay until an equilibrium point is found. Three strategies are compared: static node configuration where each queue is allocated 1/3 of the node resources and no aggregation, aggregation of real-time (taken as smooth and bursty) traffic onto the same queue, and dynamic aggregation based on the entropy of the traffic streams and their aggregates. The results of the simulation study show good results, with gains of 10-40% in the QoS parameters. By simulation, the positive effects of the proposed routing and aggregation strategy and the usefulness of the algorithm. The proposed algorithm constitutes the central control logic, and the resulting control actions are realisable through the SDN/NFV architecture.