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.

Many animal groups benefit from making decisions collectively. For example, colonies of many ant species are able to select the best possible nest to move into without every ant needing to visit each available nest site. Similarly, honey bee colonies can focus their foraging resources on the best possible food sources in their environment by sharing information with each other. In the same way, groups of human individuals are often able to make better decisions together than each individual group member can on his or her own. This phenomenon is known as "collective intelligence", or "wisdom of crowds." What unites all these examples is the fact that there is no centralized organization dictating how animal groups make their decisions. Instead, these successful decisions emerge from interactions and information transfer between individual members of the group and between individuals and their environment. In this thesis, I apply mathematical modeling techniques in order to better understand how groups of social animals make important decisions in situations where no single individual has complete information. This thesis consists of five papers, in which I collaborate with biologists and sociologists to simulate the results of their experiments on group decision-making in animals. The goal of the modeling process is to better understand the underlying mechanisms of interaction that allow animal groups to make accurate decisions that are vital to their survival. Mathematical models also allow us to make predictions about collective decisions made by animal groups that have not yet been studied experimentally or that cannot be easily studied. The combination of mathematical modeling and experimentation gives us a better insight into the benefits and drawbacks of collective decision making, and into the variety of mechanisms that are responsible for collective intelligence in animals. The models that I use in the thesis include differential equation models, agent-based models, stochastic models, and spatially explicit models. The biological systems studied included foraging honey bee colonies, house-hunting ants, and humans answering trivia questions.

University of Macau / Faculty of Science and Technology / Department of Electromechanical Engineering

This dissertation presents the development of a new concept and technology exploration methodology for aerospace architectures. The methodology is based on modeling the design space by a graph, and optimizing the graph using Ant Colony Optimization. The results show that the proposed design methodology can explore more efficiently the concept and technology space of a launch vehicle architecture than traditional optimization approaches such as Genetic Algorithm and Simulated Annealing. The purpose of the method is to introduce quantitative and simultaneous exploration of concept and technology alternatives during the early phases of conceptual design. To achieve this goal, technical challenges such as expanding the size of the design space, exploring more efficiently the design options, and simultaneously considering technologies and concepts are overcome. The total number of design alternatives grows factorially with the number of concepts in the design space. Under these circumstances, the design space is difficult to explore in its totality. Considering more alternatives has been the focus of several researchers, using Genetic Algorithms and Simulated Annealing. The large number of incompatibilities between alternatives, however, limits these optimization algorithms and reduces the number of concepts or technologies that can be considered. To address these problems, a concept and technology selection methodology is developed. The methodology proposes a way to automatically generate aerospace architectures, and to model concept and technology incompatibilities by means of a graph. In conjunction with this new modeling approach, a graph-based stochastic optimization algorithm is used to efficiently explore the design space. This design methodology is applied to the simultaneous concept and technology exploration of an expendable launch vehicle architecture. This study demonstrates that the consideration of more design alternatives can help design engineers to make more informed decisions during the concept and technology selection process. Moreover, the simultaneous exploration of concepts and technologies has the potential to identify a different set of solutions than the standard approach where the technologies are explored after the concepts have initially been selected.

Groups of starlings can form impressive shapes as they travel northward together in the springtime. This is among a group of natural phenomena based on swarm behaviour. The research field of artificial intelligence in computer science, particularly the areas of robotics and image processing, has in recent decades given increasing attention to the underlying structures. The behaviour of these intelligent swarms has opened new approaches for face detection as well. G. Beni and J. Wang coined the term “swarm intelligence” to describe this type of group behaviour. In this context, intelligence describes the ability to solve complex problems. The objective of this project is to automatically find exactly one face on a photo or video material by means of swarm intelligence. The process developed for this purpose consists of a combination of various known structures, which are then adapted to the task of face detection. To illustrate the result, a 3D hat shape is placed on top of the face using an example application program.

Thesis (Ph.D.(Computer Science))--University of Pretoria, 2009. / Abstract in English. Includes bibliographical references.

Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Ronald G. Harley; Committee Member: David G. Taylor; Committee Member: Deepakraj M. Divan; Committee Member: Ganesh Kumar Venayagamoorthy; Committee Member: Thomas G. Habetler. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Este trabalho trata do estudo de uma técnica de rastreamento de máxima potência (Maximum Power Point Tracking - MPPT) baseado no método de Otimização por Enxame de Partículas (Particle Swarm Optimization - PSO), o qual é aplicado a um sistema fotovoltaico (Photovoltaic -PV) conectado a uma rede elétrica monofásica. A curva característica corrente-tensão dos painéis/arranjos fotovoltaicos têm um comportamento não-linear, e quando estes são submetidos a condições de sombreamento parcial, pode-se ocorrer o surgimento de pontos distintos de máximos locais e global. A maioria das técnicas de MPPT tradicionais não é capaz de encontrar o ponto de máximo global para a extração da máxima potência fornecida pelo sistema PV. Por conseguinte, a fim de contornar este problema, neste trabalho o método de MPPTPSO proposto é utilizado para buscar a operação do sistema no ponto de máximo global, maximizando a extração de energia dos referidos arranjos PV. Simulações e ensaios experimentais com três topologias diferentes de conversores são apresentados para demonstrar a eficácia da proposta, quando esta é comparada com o método tradicional MPPT-PO conhecido como Perturbação e Observação Observe (P&O). / This paper consists of a study of a maximum power point tracking (MPPT) technique based on the Particle Swarm Optimization (PSO) method, which is applied to a single-phase grid-tied photovoltaic system. Since photovoltaic panels have nonlinear voltage-current characteristic curves, when they are submitted to partial shading conditions, it is possible to appear distinct local and global maximum power points. On the other hand, the most traditional MPPT methods are not able to find the maximum global point for extraction the maximum power provided by the PV array. Therefore, in order to overcome this problem, MPPT-PSO based method is used for obtaining the maximum global point, maximizing the power extraction in the photovoltaic arrangements. Numerical simulations and experimental results with three different topologies of converters are presented to demonstrate the effectiveness of the proposed MPPT-PSO technique, when it is compared with the well-known Perturb and Observe (P&O) MPPT-PO technique.

Thesis (Master of Engineering in Electrical Engineering)--Cape Peninsula University of Technology, 2018. / A smart grid is an intelligent power delivery system integrating traditional and advanced control, monitoring, and protection systems for enhanced reliability, improved efficiency, and quality of supply. To achieve a smart grid, technical challenges such as voltage instability; power loss; and unscheduled power interruptions should be mitigated. Therefore, future smart grids will require intelligent solutions at transmission and distribution levels, and optimal placement & sizing of grid components for optimal steady state and dynamic operation of the power systems. At distribution levels, feeder reconfiguration and Distributed Generation (DG) can be used to improve the distribution network performance. Feeder reconfiguration consists of readjusting the topology of the primary distribution network by remote control of the tie and sectionalizing switches under normal and abnormal conditions. Its main applications include service restoration after a power outage, load balancing by relieving overloads from some feeders to adjacent feeders, and power loss minimisation for better efficiency. On the other hand, the DG placement problem entails finding the optimal location and size of the DG for integration in a distribution network to boost the network performance. This research aims to develop Particle Swarm Optimization (PSO) algorithms to solve the distribution network feeder reconfiguration and DG placement & sizing problems. Initially, the feeder reconfiguration problem is treated as a single-objective optimisation problem (real power loss minimisation) and then converted into a multi-objective optimisation problem (real power loss minimisation and load balancing). Similarly, the DG placement problem is treated as a single-objective problem (real power loss minimisation) and then converted into a multi-objective optimisation problem (real power loss minimisation, voltage deviation minimisation, Voltage stability Index maximisation). The developed PSO algorithms are implemented and tested for the 16-bus, the 33-bus, and the 69-bus IEEE distribution systems. Additionally, a parallel computing method is developed to study the operation of a distribution network with a feeder reconfiguration scheme under dynamic loading conditions.

Agrupamento de dados é o processo que consiste em dividir um conjunto de dados em grupos de forma que dados semelhantes entre si permaneçam no mesmo grupo enquanto que dados dissimilares sejam alocados em grupos diferentes. Técnicas tradicionais de agrupamento de dados têm sido usualmente desenvolvidas de maneira centralizada dependendo assim de estruturas que devem ser acessadas e modificadas a cada passo do processo de agrupamento. Além disso, os resultados gerados por tais métodos são dependentes de informações que devem ser fornecidas a priori como por exemplo número de grupos, tamanho do grupo ou densidade mínima/máxima permitida para o grupo. O presente trabalho visa propor o bee clustering, um algoritmo distribuído inspirado principalmente em técnicas de inteligência de enxames como organização de colônias de abelhas e alocação de tarefas em insetos sociais, desenvolvido com o objetivo de resolver o problema de agrupamento de dados sem a necessidade de pistas sobre o resultado desejado ou inicialização de parâmetros complexos. O bee clustering é capaz de formar grupos de agentes de maneira distribuída, uma necessidade típica em cenários de sistemas multiagente que exijam capacidade de auto-organização sem controle centralizado. Os resultados obtidos mostram que é possível atingir resultados comparáveis as abordagens centralizadas. / Clustering can be defined as a set of techniques that separate a data set into groups of similar objects. Data items within the same group are more similar than objects of different groups. Traditional clustering methods have been usually developed in a centralized fashion. One reason for this is that this form of clustering relies on data structures that must be accessed and modified at each step of the clustering process. Another issue with classical clustering methods is that they need some hints about the target clustering. These hints include for example the number of clusters, the expected cluster size, or the minimum density of clusters. In this work we propose a clustering algorithm that is inspired by swarm intelligence techniques such as the organization of bee colonies and task allocation among social insects. Our proposed algorithm is developed in a decentralized fashion without any initial information about number of classes, number of partitions, and size of partition, and without the need of complex parameters. The bee clustering algorithm is able to form groups of agents in a distributed way, a typical necessity in multiagent scenarios that require self-organization without central control. The performance of our algorithm shows that it is possible to achieve results that are comparable to those from centralized approaches.