This thesis presents a new ant-optimized biclustering technique known as SNAP biclustering, which runs faster and produces results of superior quality to previous techniques. Biclustering techniques have been designed to compensate for the weaknesses of classical clustering algorithms by allowing cluster overlap, and allowing vectors to be grouped for a subset of their defined features. These techniques have performed well in many problem domains, particularly DNA microarray analysis and collaborative filtering. A motivation for this work has been the biclustering technique known as bicACO, which was the first to use ant colony optimization. As bicACO is time intensive, much emphasis was placed on decreasing SNAP's runtime. The superior speed and biclustering results of SNAP are due to its improved initialization and solution construction procedures. In experimental studies involving the Yeast Cell Cycle DNA microarray dataset and the MovieLens collaborative filtering dataset, SNAP has run at least 22 times faster than bicACO while generating superior results. Thus, SNAP is an effective choice of technique for microarray analysis and collaborative filtering applications. / Master of Science

Tesis por compendio / [EN] The long-lasting trend in the field of computation of stress and resource distribution has found its way into computer networks via the concept of peer-to-peer (P2P) connectivity. P2P is a symmetrical model, where each network node is enabled a comparable range of capacities and resources. It stands in a stark contrast to the classical, strongly asymmetrical client-server approach. P2P, originally considered only a complimentary, server-side structure to the straightforward client-server model, has been shown to have the substantial potential on its own, with multiple, widely known benefits: good fault tolerance and recovery, satisfactory scalability and intrinsic load distribution. However, contrary to client-server, P2P networks require sophisticated solutions on all levels, ranging from network organization, to resource location and managing. In this thesis we address one of the key issues of P2P networks: performing efficient resource searches of semantic nature under realistic, dynamic conditions. There have been numerous solutions to this matter, with evolutionary, stigmergy-based, and simple computational foci, but few attempt to resolve the full range of challenges this problem entails. To name a few: real-life P2P networks are rarely static, nodes disconnect, reconnect and change their content. In addition, a trivial incorporation of semantic searches into well-known algorithms causes significant decrease in search efficiency. In our research we build a solution incrementally, starting with the classic Ant Colony System (ACS) within the Ant Colony Optimization metaheuristic (ACO). ACO is an algorithmic framework used for solving combinatorial optimization problems that fits contractually the problem very well, albeit not providing an immediate solution to any of the aforementioned problems. First, we propose an efficient ACS variant in structured (hypercube structured) P2P networks, by enabling a path-post processing algorithm, which called Tabu Route Optimization (TRO). Next, we proceed to resolve the issue of network dynamism with an ACO-compatible information diffusion approach. Consequently, we attempt to incorporate the semantic component of the searches. This initial approximation to the problem was achieved by allowing ACS to differentiate between search types with the pheromone-per-concept idea. We called the outcome of this merger Routing Concept ACS (RC-ACS). RC-ACS is a robust, static multipheromone implementation of ACS. However, we were able to conclude from it that the pheromone-per-concept approach offers only limited scalability and cannot be considered a global solution. Thus, further progress was made in this respect when we introduced to RC-ACS our novel idea: dynamic pheromone creation, which replaces the static one-to-one assignment. We called the resulting algorithm Angry Ant Framework (AAF). In AAF new pheromone levels are created as needed and during the search, rather than prior to it. The final step was to enable AAF, not only to create pheromone levels, but to reassign them to optimize the pheromone usage. The resulting algorithm is called EntropicAAF and it has been evaluated as one of the top-performing algorithms for P2P semantic searches under all conditions. / [ES] La popular tendencia de distribución de carga y recursos en el ámbito de la computación se ha transmitido a las redes computacionales a través del concepto de la conectividad peer-to-peer (P2P). P2P es un modelo simétrico, en el cual a cada nodo de la red se le otorga un rango comparable de capacidades y recursos. Se trata de un fuerte contraste con el clásico y fuertemente asimétrico enfoque cliente-servidor. P2P, originalmente considerado solo como una estructura del lado del servidor complementaria al sencillo modelo cliente-servidor, ha demostrado tener un potencial considerable por sí mismo, con múltiples beneficios ampliamente conocidos: buena tolerancia a fallos y recuperación, escalabilidad satisfactoria y distribución de carga intrínseca. Sin embargo, al contrario que el modelo cliente-servidor, las redes P2P requieren de soluciones sofisticadas a todos los niveles, desde la organización de la red hasta la gestión y localización de recursos. Esta tesis aborda uno de los problemas principales de las redes P2P: la búsqueda eficiente de recursos de naturaleza semántica bajo condiciones dinámicas y realistas. Ha habido numerosas soluciones a este problema basadas en enfoques evolucionarios, estigmérgicos y simples, pero pocas han tratado de resolver el abanico completo de desafíos. En primer lugar, las redes P2P reales son raramente estáticas: los nodos se desconectan, reconectan y cambian de contenido. Además, la incorporación trivial de búsquedas semánticas en algoritmos conocidos causa un decremento significativo de la eficiencia de la búsqueda. En esta investigación se ha construido una solución de manera incremental, comenzando por el clásico Ant Colony System (ACS) basado en la metaheurística de Ant Colony Optimization (ACO). ACO es un framework algorítmico usado para búsquedas en grafos que encaja perfectamente con las condiciones del problema, aunque no provee una solución inmediata a las cuestiones mencionadas anteriormente. En primer lugar, se propone una variante eficiente de ACS para redes P2P estructuradas (con estructura de hipercubo) permitiendo el postprocesamiento de las rutas, al que hemos denominado Tabu Route Optimization (TRO). A continuación, se ha tratado de resolver el problema del dinamismo de la red mediante la difusión de la información a través de una estrategia compatible con ACO. En consecuencia, se ha tratado de incorporar el componente semántico de las búsquedas. Esta aproximación inicial al problema ha sido lograda permitiendo al ACS diferenciar entre tipos de búsquedas através de la idea de pheromone-per-concept. El resultado de esta fusión se ha denominado Routing Concept ACS (RC-ACS). RC-ACS es una implementación multiferomona estática y robusta de ACS. Sin embargo, a partir de esta implementación se ha podido concluir que el enfoque pheromone-per-concept ofrece solo escalabilidad limitada y que no puede ser considerado una solución global. Por lo tanto, para lograr una mejora a este respecto, se ha introducido al RC-ACS una novedosa idea: la creación dinámica de feromonas, que reemplaza la asignación estática uno a uno. En el algoritmo resultante, al que hemos denominado Angry Ant Framework (AAF), los nuevos niveles de feromona se crean conforme se necesitan y durante la búsqueda, en lugar de crearse antes de la misma. La mejora final se ha obtenido al permitir al AAF no solo crear niveles de feromona, sino también reasignarlos para optimizar el uso de la misma. El algoritmo resultante se denomina EntropicAAF y ha sido evaluado como uno de los algoritmos más exitosos para las búsquedas semánticas P2P bajo todas las condiciones. / [CA] La popular tendència de distribuir càrrega i recursos en el camp de la computació s'ha estès cap a les xarxes d'ordinadors a través del concepte de connexions d'igual a igual (de l'anglès, peer to peer o P2P). P2P és un model simètric on cada node de la xarxa disposa del mateix nombre de capacitats i recursos. P2P, considerat originàriament només una estructura situada al servidor complementària al model client-servidor simple, ha provat tindre el suficient potencial per ella mateixa, amb múltiples beneficis ben coneguts: una bona tolerància a errades i recuperació, una satisfactòria escalabilitat i una intrínseca distribució de càrrega. No obstant, contràriament al client-servidor, les xarxes P2P requereixen solucions sofisticades a tots els nivells, que varien des de l'organització de la xarxa a la localització de recursos i la seua gestió. En aquesta tesi s'adreça un dels problemes clau de les xarxes P2P: ser capaç de realitzar eficientment cerques de recursos de naturalesa semàntica sota condicions realistes i dinàmiques. Existeixen nombroses solucions a aquest tema basades en la computació simple, evolutiva i també basades en l'estimèrgia (de l'anglès, stigmergy), però pocs esforços s'han realitzat per intentar resoldre l'ampli conjunt de reptes existent. En primer lloc, les xarxes P2P reals són rarament estàtiques: els nodes es connecten, desconnecten i canvien els seus continguts. A més a més, la incorporació trivial de cerques semàntiques als algorismes existents causa una disminució significant de l'eficiència de la cerca. En aquesta recerca s'ha construït una solució incremental, començant pel sistema clàssic de colònia de formigues (de l'anglés, Ant Colony System o ACS) dins de la metaheurística d'optimització de colònies de formigues (de l'anglès, Ant Colony Optimization o ACO). ACO és un entorn algorísmic utilitzat per cercar en grafs i que aborda el problema de forma satisfactòria, tot i que no proveeix d'una solució immediata a cap dels problemes anteriorment mencionats. Primer, s'ha proposat una variant eficient d'ACS en xarxes P2P estructurades (en forma d'hipercub) a través d'un algorisme de processament post-camí el qual s'ha anomenat en anglès Tabu Route Optimization (TRO). A continuació, s'ha procedit a resoldre el problema del dinamisme de les xarxes amb un enfocament de difusió d'informació compatible amb ACO. Com a conseqüència, s'ha intentat incorporar la component semàntica de les cerques. Aquest enfocament inicial al problema s'ha realitzat permetent a ACS diferenciar entre tipus de cerques amb la idea de ''feromona per concepte'', i s'ha anomenat a aquest producte Routing Concept ACS o RC-ACS. RC-ACS és una implementació multi-feromona robusta i estàtica d'ACS. No obstant, s'ha pogut concloure que l'enfocament de feromona per concepte ofereix només una escalabilitat limitada i no pot ser considerada una solució global. En aquest respecte s'ha realitzat progrés posteriorment introduint una nova idea a RC-ACS: la creació dinàmica de feromones, la qual reemplaça a l'assignació un a un de les mateixes. A l'algorisme resultant se l'ha anomenat en anglès Angry Ant Framework (AAF). En AAF es creen nous nivells de feromones a mesura que es necessiten durant la cerca, i no abans d'aquesta. El progrés final s'ha aconseguit quan s'ha permès a AAF, no sols crear nivells de feromones, sinó reassignar-los per optimitzar la utilització de feromones. L'algorisme resultant s'ha anomenat EntropicAAF i ha sigut avaluat com un dels algorismes per a cerques semàntiques P2P amb millors prestacions. / Krynicki, KK. (2016). Ant Colony Algorithms for the Resolution of Semantic Searches in P2P Networks [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/61293 / Premios Extraordinarios de tesis doctorales / Compendio

Roteamento por colônia de formigas é um método de roteamento em redes de comunicação, e diversos algoritmos foram propostos nos últimos anos baseado nessa estrutura. Todos esses algoritmos produzem excelentes resultados, provando a sua eficiência e eficácia. Este trabalho apresenta os resultados de desempenho dos principais algoritmos encontrados na literatura, e com base nesses resultados, propõe um novo algoritmo com desempenho equivalente e com uma complexidade computacional menor. O trabalho é focalizado em redes tipo datagrama com topologia irregular, descrevendo suas propriedades e características e realizando uma análise e comparação de seus desempenhos em um ambiente de simulação. / Ant Colony Routing is an adaptive method for routing in communication networks, and several algorithms have been proposed in the last years based on this framework. All these algorithms show excellent results, proving their efficiency and efficacy. This work presents the results of the performance of the main algorithms found in the literature, and based on these results, it proposes a novel algorithm that has a similar performance but with a lower computational complexity. The work is focused in datagram like networks with irregular topology, describing its characteristics and properties. The performances in an simulation environment are analysed and compared.

Um desafio recorrente em processos produtivos é o desenvolvimento de sistemas de monitoração e diagnóstico. Esses sistemas ajudam na detecção de mudanças inesperadas e interrupções, prevenindo perdas e mitigando riscos. Redes Neurais Artificiais (RNA) têm sido largamente utilizadas na criação de sistemas de monitoração. Normalmente as RNA utilizadas para resolver este tipo de problema são criadas levando-se em conta apenas parâmetros como o número de entradas, saídas e quantidade de neurônios nas camadas escondidas. Assim, as redes resultantes geralmente possuem uma configuração onde há uma total conexão entre os neurônios de uma camada e os da camada seguinte, sem que haja melhorias em sua topologia. Este trabalho utiliza o algoritmo de Otimização por Colônia de Formigas (OCF) para criar redes neurais otimizadas. O algoritmo de busca OCF utiliza a técnica de retropropagação de erros para otimizar a topologia da rede neural sugerindo as melhores conexões entre os neurônios. A RNA resultante foi aplicada para monitorar variáveis do reator de pesquisas IEA-R1 do IPEN. Os resultados obtidos mostram que o algoritmo desenvolvido é capaz de melhorar o desempenho do modelo que estima o valor de variáveis do reator. Em testes com diferentes números de neurônios na camada escondida, utilizando como comparativos o erro quadrático médio, o erro absoluto médio e o coeficiente de correlação, o desempenho da RNA otimizada foi igual ou superior ao da tradicional. / A recurring challenge in production processes is the development of monitoring and diagnosis systems. Those systems help on detecting unexpected changes and interruptions, preventing losses and mitigating risks. Artificial Neural Networks (ANN) have been extensively used in creating monitoring systems. Usually the ANN used to solve this kind of problem are created by taking into account only parameters as the number of inputs, outputs, and number of neurons in the hidden layers. This way, the result networks are generally fully connected and have no improvements in its topology. This work uses an Ant Colony Optimization (ACO) algorithm to create a tuned neural networks. The ACO search algorithm uses Back Error Propagation (BP) to optimize the network topology by suggesting the best neuron connections. The outcome ANN was applied to monitoring the IEA-R1 research reactor at IPEN. The results show that the algorithm is able to improve the performance of the model which estimates the values of the reactor variables. In tests with different numbers of neurons in the hidden layer, using as comparison the mean squared error, the mean absolute error, and the correlation coefficient, the performance of the optimized ANN proved equal or better than the equivalent traditional neural networks.

We present two Swarm Intelligence based approaches for data clustering. The first algorithm, Fuzzy Ants, presented in this thesis clusters data without the initial knowledge of the number of clusters. It is a two stage algorithm. In the first stage the ants cluster data to initially create raw clusters which are refined using the Fuzzy C Means algorithm. Initially, the ants move the individual objects to form heaps. The centroids of these heaps are redefined by the Fuzzy C Means algorithm. In the second stage the objects obtained from the Fuzzy C Means algorithm are hardened according to the maximum membership criteria to form new heaps. These new heaps are then moved by the ants. The final clusters formed are refined by using the Fuzzy C Means algorithm. Results from experiments with 13 datasets show that the partitions produced are competitive with those from FCM. The second algorithm, Fuzzy ant clustering with centroids, is also a two stage algorithm, it requires an initial knowledge of the number of clusters in the data. In the first stage of the algorithm ants move the cluster centers in feature space. The cluster centers found by the ants are evaluated using a reformulated Fuzzy C Means criterion. In the second stage the best cluster centers found are used as the initial cluster centers for the Fuzzy C Means algorithm. Results on 18 datasets show that the partitions found by FCM using the ant initialization are better than those from randomly initialized FCM. Hard C Means was also used in the second stage and the partitions from the ant algorithm are better than from randomly initialized Hard C Means. The Fuzzy Ants algorithm is a novel method to find the number of clusters in the data and also provides good initializations for the FCM and HCM algorithms. We performed sensitivity analysis on the controlling parameters and found the Fuzzy Ants algorithm to be very sensitive to the Tcreateforheap parameter. The FCM and HCM algorithms, with random initializations can get stuck in a bad extrema, the Fuzzy ant clustering with centroids algorithm successfully avoids these bad extremas.

Opposition-based learning (OBL) was recently proposed to extend di erent machine learning algorithms. The main idea of OBL is to consider opposite estimates, actions or states as an attempt to increase the coverage of the solution space and to reduce exploration time. OBL has already been applied to reinforcement learning, neural networks and genetic algorithms. This thesis explores the application of OBL to ant algorithms. Ant algorithms are based on the trail laying and following behaviour of ants. They have been successfully applied to many complex optimization problems. However, like any other technique, they can benefit from performance improvements. Thus, this work was motivated by the idea of developing more complex pheromone and path selection behaviour for the algorithm using the concept of opposition. This work proposes opposition-based extensions to the construction and update phases of the ant algorithm. The modifications that focus on the solution construction include three direct and two indirect methods. The three direct methods work by pairing the ants and synchronizing their path selection. The two other approaches modify the decisions of the ants by using opposite-pheromone content. The extension of the update phase lead to an approach that performs additional pheromone updates using opposite decisions. Experimental validation was done using two versions of the ant algorithm: the Ant System and the Ant Colony System. The di erent OBL extensions were applied to the Travelling Salesman Problem (TSP) and to the Grid World Problem (GWP). Results demonstrate that the concept of opposition is not easily applied to the ant algorithm. One pheromone-based method showed performance improvements that were statistically significant for the TSP. The quality of the solutions increased and more optimal solutions were found. The extension to the update phase showed some improvements for the TSP and led to accuracy improvements and a significant speed-up for the GWP. The other extensions showed no clear improvement. The proposed methods for applying opposition to the ant algorithm have potential, but more investigations are required before ant colony optimization can fully benefit from opposition. Most importantly, fundamental theoretical work with graphs, specifically, clearly defining opposite paths or opposite path components, is needed. Overall, the results indicate that OBL ideas can be beneficial for ant algorithms.

Opposition-based learning (OBL) was recently proposed to extend di erent machine learning algorithms. The main idea of OBL is to consider opposite estimates, actions or states as an attempt to increase the coverage of the solution space and to reduce exploration time. OBL has already been applied to reinforcement learning, neural networks and genetic algorithms. This thesis explores the application of OBL to ant algorithms. Ant algorithms are based on the trail laying and following behaviour of ants. They have been successfully applied to many complex optimization problems. However, like any other technique, they can benefit from performance improvements. Thus, this work was motivated by the idea of developing more complex pheromone and path selection behaviour for the algorithm using the concept of opposition. This work proposes opposition-based extensions to the construction and update phases of the ant algorithm. The modifications that focus on the solution construction include three direct and two indirect methods. The three direct methods work by pairing the ants and synchronizing their path selection. The two other approaches modify the decisions of the ants by using opposite-pheromone content. The extension of the update phase lead to an approach that performs additional pheromone updates using opposite decisions. Experimental validation was done using two versions of the ant algorithm: the Ant System and the Ant Colony System. The di erent OBL extensions were applied to the Travelling Salesman Problem (TSP) and to the Grid World Problem (GWP). Results demonstrate that the concept of opposition is not easily applied to the ant algorithm. One pheromone-based method showed performance improvements that were statistically significant for the TSP. The quality of the solutions increased and more optimal solutions were found. The extension to the update phase showed some improvements for the TSP and led to accuracy improvements and a significant speed-up for the GWP. The other extensions showed no clear improvement. The proposed methods for applying opposition to the ant algorithm have potential, but more investigations are required before ant colony optimization can fully benefit from opposition. Most importantly, fundamental theoretical work with graphs, specifically, clearly defining opposite paths or opposite path components, is needed. Overall, the results indicate that OBL ideas can be beneficial for ant algorithms.

In this dissertation, we consider the clustering problem in data sets with unknown number of clusters having arbitrary shapes, intracluster and intercluster density variations. We introduce a clustering methodology which is composed of three methods that ensures extraction of local density and connectivity properties, data set reduction, and clustering. The first method constructs a unique neighborhood for each data point using the connectivity and density relations among the points based upon the graph theoretical concepts, mainly Gabriel Graphs. Neighborhoods subsequently connected form subclusters (closures) which constitute the skeleton of the clusters. In the second method, the external shape concept in computational geometry is adapted for data set reduction and cluster visualization. This method extracts the external shape of a non-convex n-dimensional data set using Delaunay triangulation. In the third method, we inquire the applicability of Swarm Intelligence to clustering using Ant Colony Optimization (ACO). Ants explore the data set so that the clusters are detected using density break-offs, connectivity and distance information. The proposed ACO-based algorithm uses the outputs of the neighborhood construction (NC) and the external shape formation. In addition, we propose a three-phase clustering algorithm that consists of NC, outlier detection and merging phases. We test the strengths and the weaknesses of the proposed approaches by extensive experimentation with data sets borrowed from literature and generated in a controlled manner. NC is found to be effective for arbitrary shaped clusters, intracluster and intercluster density variations. The external shape formation algorithm achieves significant reductions for convex clusters. The ACO-based and the three-phase clustering algorithms have promising results for the data sets having well-separated clusters.

In einer von Vernetzung und Globalisierung geprägten Umwelt wächst der Wettbewerbsdruck auf die Unternehmen am Markt stetig. Die effektive Nutzung der Ressourcen einerseits und die enge Zusammenarbeit mit Lieferanten und Kunden andererseits führen für nicht wenige Unternehmen des industriellen Sektors zu entscheidenden Wettbewerbsvorteilen, die das Fortbestehen jener Unternehmen am Markt sichern. Viele Unternehmen verstehen sich aus diesem Grund als Bestandteil so genannter Supply Chains. Die unternehmensübergreifende Steuerung und Optimierung des Wertschöpfungsprozesses stellt ein charakteristisches Problem des Supply Chain Managements dar und besitzt zur Erzielung von Wettbewerbsvorteilen hohes Potential. Produktionsnetzwerke sind ein wesentlicher Forschungsschwerpunkt der Professur für Produktionswirtschaft und Industriebetriebslehre an der TU Chemnitz. Das Extended Value Chain Management (EVCM) stellt ein kompetenzorientiertes Konzept für die Bildung und zum Betrieb hierarchieloser temporärer regionaler Produktionsnetzwerke im Sinne virtueller Unternehmen dar. Gegenstand dieser Arbeit ist ein diskretes Optimierungsproblem, dass einen mehrstufigen Entscheidungsprozesses unter Berücksichtigung mehrerer Ziele abbildet, der sich bei der Auswahl möglicher Partner in einem Produktionsnetzwerk nach dem Betreiberkonzept des EVCM ergibt. Da mehrere Zielstellungen bestehen, werden grundlegende Methoden der mehrkriteriellen Optimierung und Entscheidung erörtert. Neben der Vorstellung des Problems sollen mehrzielorientierte Ansätze im Sinne einer Pareto-Optimierung auf Basis des Dynamic Programmings als Verfahren zur Bestimmung von Optimallösungen sowie Ant Colony Optimization zur näherungsweisen Lösung vorgestellt werden. Darauf aufbauend werden verschiedene Möglichkeiten der Hybridisierung beider Methoden diskutiert. Die entwickelten Ansätze werden auf ihre Eignung im Rahmen der informationstechnischen Umsetzung des EVCM-Konzepts untersucht und einer Evaluierung unterzogen. Hierzu werden verschiedene Kennzahlen zur Beurteilung der Verfahren entwickelt. Die modellierten Algorithmen und entwickelten Konzepte beschränken sich nicht ausschließlich auf das betrachtete Problem, sondern können leicht auf Probleme mit ähnlichen Eigenschaften übertragen werden. Insbesondere das NP-vollständige mehrkriterielle Kürzeste-Wege-Problem stellt einen Spezialfall des behandelten Optimierungsproblems dar.

In the last decades there has been a lot of interest in computational models and metaheuristics algorithms capable to solve combinatorial optimization problems. The recent trend is to define these algorithms taking inspiration by the observation of natural systems. In this thesis the Ant Colony System (ACS) is presented which has been inspired by the observation of real ant colonies. ACS is initially proposed to solve the symmetric and asymmetric travelling salesman problems where it is shown to be competitive with other metaheuristics. Although this is an interesting and promising result, it was immediately clear that ACS, as well as other metaheuristics, in many cases cannot compete with specialized local search methods. An interesting trend is therefore to couple metaheuristics with a local optimizer, giving birth to so-called hybrid methods. Along this line, the thesis investigates MACS-VRPTW (Multiple ACS for the Vehicle Routing Problem with Time Windows) and HAS-SOP: Hybrid Ant System for the Sequential Ordering Problem (SOP). In the second part the thesis introduces some modifications of the original ACS algorithm. These modifications are able to speed up the method and to make it more competitive in case of large problem instances. The resulting framework, called Enhanced Ant Colony System is tested for the SOP. Finally the thesis presents the application of ACS to solve real-life vehicle routing problems where additional constraints and stochastic information are included. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished