Thesis (M.Sc.)(Computer Science)--University of Pretoria, 2006. / Includes summary. Includes bibliographical references (leaves 164-174). Available on the Internet via the World Wide Web.

We investigate the use of self-assembly in a robotic system as a means of responding to dierent environmental contingencies. Self-assembly is the mechanism through which agents in a multi-robot system autonomously form connections with one another to create larger composite robotic entities. Initially, we consider a simple response mechanism that uses stochastic self-assembly without any explicit control over the resulting morphology | the robots self-assemble into a larger, randomly shaped composite entity if the task they encounter is beyond the physical capabilities of a single robot. We present distributed behavioural control that enables a group of robots to make this collective decision about when and if to self-assemble in the context of a hill crossing task. In a series of real-world experiments, we analyse the eect of dierent distributed timing and decision strategies on system performance. Outside of a task execution context, we present fully decentralised behavioural control capable of creating periodically repeating global morphologies. We then show how arbitrary morphologies can be generated by abstracting our behavioural control into a morphology control language and adding symbolic communication between connected agents. Finally, we integrate our earlier distributed response mechanism into the morphology control language. We run simulated and real-world experiments to demonstrate a self-assembling robotic system that can respond to varying environmental contingencies by forming dierent appropriate morphologies.

In swarm robotics, the control of a group of robots is often fully distributed and does not rely on any leader. In this thesis, we are interested in understanding how to design collective decision making processes in such groups. Our approach consists in taking inspiration from nature, and especially from self organization in social insects, in order to produce effective collective behaviors in robot swarms. We have devised four robotics experiments that allow us to study multiple facets of collective decision making. The problems on which we focus include cooperative transport of objects, robot localization, resource selection, and resource discrimination. We study how information is transferred inside the groups, how collective decisions arise, and through which particular interactions. Important properties of the groups such as scalability, robustness, and adaptivity are also investigated. We show that collective decisions in robot swarms can effectively arise thanks to simple mechanisms of imitation and amplification. We experimentally demonstrate their implementation with direct or indirect information transfer, and with robots that can distinguish the available options partially or not at all.

With the rapid growth of data and computational needs, distributed systems and computational Grids are gaining more and more attention. Grids are playing an important and growing role in today networks. The huge amount of computations a Grid can fulfill in a specificc time cannot be done by the best super computers. However, Grid performance can still be improved by making sure all the resources available in the Grid are utilized by a good load balancing algorithm. The purpose of such algorithms is to make sure all nodes are equally involved in Grid computations. This research proposes two new distributed swarm intelligence inspired load balancing algorithms. One is based on ant colony optimization and is called AntZ, the other one is based on particle swarm optimization and is called ParticleZ. Distributed load balancing does not incorporate a single point of failure in the system. In the AntZ algorithm, an ant is invoked in response to submitting a job to the Grid and this ant surfs the network to find the best resource to deliver the job to. In the ParticleZ algorithm, each node plays a role as a particle and moves toward other particles by sharing its workload among them. We will be simulating our proposed approaches using a Grid simulation toolkit (GridSim) dedicated to Grid simulations. The performance of the algorithms will be evaluated using several performance criteria (e.g. makespan and load balancing level). A comparison of our proposed approaches with a classical approach called State Broadcast Algorithm and two random approaches will also be provided. Experimental results show the proposed algorithms (AntZ and ParticleZ) can perform very well in a Grid environment. In particular, the use of particle swarm optimization, which has not been addressed in the literature, can yield better performance results in many scenarios than the ant colony approach.

With the rapid growth of data and computational needs, distributed systems and computational Grids are gaining more and more attention. Grids are playing an important and growing role in today networks. The huge amount of computations a Grid can fulfill in a specificc time cannot be done by the best super computers. However, Grid performance can still be improved by making sure all the resources available in the Grid are utilized by a good load balancing algorithm. The purpose of such algorithms is to make sure all nodes are equally involved in Grid computations. This research proposes two new distributed swarm intelligence inspired load balancing algorithms. One is based on ant colony optimization and is called AntZ, the other one is based on particle swarm optimization and is called ParticleZ. Distributed load balancing does not incorporate a single point of failure in the system. In the AntZ algorithm, an ant is invoked in response to submitting a job to the Grid and this ant surfs the network to find the best resource to deliver the job to. In the ParticleZ algorithm, each node plays a role as a particle and moves toward other particles by sharing its workload among them. We will be simulating our proposed approaches using a Grid simulation toolkit (GridSim) dedicated to Grid simulations. The performance of the algorithms will be evaluated using several performance criteria (e.g. makespan and load balancing level). A comparison of our proposed approaches with a classical approach called State Broadcast Algorithm and two random approaches will also be provided. Experimental results show the proposed algorithms (AntZ and ParticleZ) can perform very well in a Grid environment. In particular, the use of particle swarm optimization, which has not been addressed in the literature, can yield better performance results in many scenarios than the ant colony approach.

Das Forschungsfeld Schwarmintelligenz, also die Anwendung des Verhaltens dezentraler selbstorganisierender Tierkollektive, im Kontext der Informatik hat eine Reihe von state-of-the-art Kontroll- und Optimierungsmechanismen hervorgebracht. Die Untersuchung selbstorganisierender biologischer Systeme fördert zum einen das Design neuer robuster und adaptiver Algorithmen. Zum anderen kann sie das Verständnis der Funktionalität von selbstorganisierenden Prinzipien, welche in der Natur auftreten, unterstützen. Diese Arbeit deckt beide zuvor beschriebenen Aspekte ab. Unter Verwendung von Modellen und Simulation werden offene Fragen bezüglich der Organisation und des Verhaltens von sozialen Insekten beleuchtet. Weiter werden Abstraktionen von selbstorganisierenden Konzepten, welche man bei sozialen Insekten findet, genutzt, um neue Methoden zur Optimierung zu entwickeln. Der erste Teil dieser Arbeit untersucht allgemeine Aspekte der Arbeitsteilung sozialer Insekten. Zuerst wird die Anpassungsfähigkeit von unterschiedlich großen Kolonien, bezüglich dynamischer Veränderungen in der Umwelt untersucht. Die Ergebnisse zeigen, dass die Fähigkeit einer Kolonie, auf Veränderung in der Umwelt zu reagieren, von der Koloniegröße beeinflusst wird. Ein weiterer Aspekt der Arbeitsteilung, welcher in dieser Arbeit untersucht wird, ist, inwieweit eine räumliche Verteilung von Aufgaben und Individuen einen Einfluss auf die Arbeitsteilung hat. Die Ergebnisse deuten an, dass soziale Insekten von einer räumlichen Trennung, der zu bewerkstelligenden Aufgaben profitieren, da eine solche Trennung die Produktivität der Kolonie erhöht. Das könnte erklären, warum eine räumliche getrennte Anordnung von Aufgaben und Individuen häufig in realen Kolonien sozialer Insekten beobachtet werden kann. Der zweite Teil dieser Arbeit untersucht verschiedene Aspekte von Selbstorganisation bei Honigbienen. Zunächst wird der Einfluss der räumlichen Verteilung von Nestplätzen auf die Nestplatzsuche der europäischen Honigbiene Apis mellifera untersucht. Die Ergebnisse legen nahe, dass die Nestplatzsuche eines Schwarms aktiv durch die Anordnung der Nestplätze in der Umwelt beeinflusst wird. Eine nestplatzreiche Umgebung kann den Prozess eines Schwarms, sich für einen Nestplatz zu entscheiden, stark behindern. Das könnte erklären, warum Honigbienenarten, die geringe Anforderungen an Nestplätze haben, was die Anzahl von potenziellen Nestplätzen natürlich erhöht, eine sehr ungenaue Form der Nestplatzsuche aufweisen. Ein zweiter Aspekt der Honigbienen, welcher untersucht wird, sind die Steuerungsmechanismen, die dem kollektiven Flug eines Bienenschwarms unterliegen. Zwei mögliche Führungsmechanismen, aktive und passive Führung, werden hinsichtlich ihrer Fähigkeit verglichen, die Flugeigenschaften eines echten Honigbienenschwarms zu reproduzieren. Die Simulationsergebnisse bestätigen aktuelle empirische Befunde und zeigen, dass aktive Führung in der Lage ist, Charakteristika fliegender Schwärme widerzuspiegeln. Bei passiver Führung ist das nicht der Fall. Eine Anwendung biologischer Konzepte im Bereich der Informatik wird anhand der Nestplatzsuche demonstriert. Diese ist ein natürlicher Optimierungsprozess, basierend auf einfachen Regeln. Erzielt wird eine lokale Optimierung, die es einem Schwarm ermöglicht, Nestplätze in einer bisher unbekannten Umgebung zu finden und aus diesen den besten Nestplatz zu wählen. Das ist die Motivation, Nestplatzsuche im Bereich der Optimierung anzuwenden. Hierfür wird zuerst das Optimierungspotenzial der biologischen Nestplatzsuche mit Hilfe eines biologischen Modells untersucht. Basierend auf der Nestplatzsuche wird ein abstrahiertes algorithmisches Schema, das so genannte „Bee Nest-Site Selection Scheme“ (BNSSS) entworfen. Basierend auf dem Schema wird der erste Nestplatzsuche inspirierte Optimierungsalgorithmus „Bee-Nest\\\'\\\' für die Anwendung im Bereich von molekular Docking entwickelt. Im Vergleich zu anderen Optimierungsalgorithmen erzielt „Bee-Nest“ eine sehr gute Leistung. / The application in computer science of the behaviour found in decentralized self-organizing animal collectives -- also known as swarm intelligence -- has brought forward a number of state-of-the art control and optimization mechanisms. Further study of such self-organizing biological systems can foster the design of new robust and adaptive algorithms, as well as aid in the understanding of self-organizing processes found in nature. This thesis covers both of the aspects described above, namely the use of computational models to investigate open questions regarding the organization and behaviour of social insects, as well as using the abstraction of concepts found in social insects to generate new optimization methods. In the first part of this work, general aspects of division of labour in social insects are investigated. First the adaptiveness of different-sized colonies to dynamic changes in the environment is analysed. The findings show that a colony\\\'s ability to react to changes in the environment scales with its size. Another aspect of division of labour which is investigated is the extent to which different spatial distributions of tasks and individuals influence division of labour. The results suggest that social insects can benefit from a spatial separation of tasks within their environment, as this increases the colony\\\'s productivity. This could explain why a spatial organization of tasks and individuals is often observed in real social insect colonies. The second part of this work investigates several aspects of self-organization found in honeybees. First the influence of spatial nest-site distribution on the ability of the European honeybee Apis mellifera to select a new nest-site is studied. The results suggest that a swarm\\\'s habitat can influence its decision-making process. Nest-site rich habitats can obstruct a swarm\\\'s ability to choose a single site if all sites are of equal quality. This could explain why in nature honeybee species which have less requirements regarding a new nest-site have evolved a more imprecise form of nest-site selection than cavity-nesting species. Another aspect of honeybees which is investigated is the guidance behaviour in migrating swarms. Two potential guidance mechanisms, active and passive guidance, are compared regarding their ability to reproduce real honeybee swarm flight characteristics. The simulation results confirm previous empirical findings, as they show that active guidance is able to reflect a number of characteristics which can be observed in real moving honeybee swarms, while this is not the case for passive guidance. Nest-site selection in honeybees can be regarded as a natural optimization process. It is based on simple rules and achieves local optimization as it enables a swarm to decide between several potential nest-sites in a previously unknown dynamic environment. These factors motivate the application of the nest-site selection process to the problem domain of function optimization. First, the optimization potential of the biological nest-site selection process is studied. Then a general algorithmic scheme called ``Bee Nest-Site Selection Scheme\\\'\\\' (BNSSS) is introduced. Based on the scheme the first nest-site inspired optimization algorithm ``Bee-Nest\\\'\\\' is introduced and successfully applied to the domain of molecular docking.

The gradual changes in the world development have brought energy issues back into high profile. An ongoing challenge for countries around the world is to balance the development gains against its effects on the environment. The energy management is the key factor of any sustainable development program. All the aspects of development in agriculture, power generation, social welfare and industry in Iran are crucially related to the energy and its revenue. Forecasting end-use natural gas consumption is an important Factor for efficient system operation and a basis for planning decisions. In this thesis, particle swarm optimization (PSO) used to forecast long run natural gas consumption in Iran. Gas consumption data in Iran for the previous 34 years is used to predict the consumption for the coming years. Four linear and nonlinear models proposed and six factors such as Gross Domestic Product (GDP), Population, National Income (NI), Temperature, Consumer Price Index (CPI) and yearly Natural Gas (NG) demand investigated.

D. Tech. Electrical Engineering. / Particle Swarm Optimisation (PSO) is based on a metaphor of social interaction such as birds flocking or fish schooling to search a space by adjusting the trajectories of individual vectors, called "particles" conceptualized as moving points in a multidimensional space. This thesis presents several algorithms/techniques to improve the PSO's global search ability. Simulation and analytical results confirm the efficiency of the proposed algorithms/techniques when compared to the other state of the art algorithms.

Thesis submitted in compliance with the requirements for the Master's Degree in Technology: Electrical Engineering - Light Current, Durban University of Technology, Department of Electronic Engineering, 2008. / Linear control systems can be easily tuned using classical tuning techniques such as the Ziegler-Nichols and Cohen-Coon tuning formulae. Empirical studies have found that these conventional tuning methods result in an unsatisfactory control performance when they are used for processes experiencing the negative destabilizing effects of strong nonlinearities. It is for this reason that control practitioners often prefer to tune most nonlinear systems using trial and error tuning, or intuitive tuning. A need therefore exists for the development of a suitable tuning technique that is applicable for a wide range of control loops that do not respond satisfactorily to conventional tuning. Emerging technologies such as Swarm Intelligence (SI) have been utilized to solve many non-linear engineering problems. Particle Swarm Optimization (PSO), developed by Eberhart and Kennedy (1995), is a sub-field of SI and was inspired by swarming patterns occurring in nature such as flocking birds. It was observed that each individual exchanges previous experience, hence knowledge of the “best position” attained by an individual becomes globally known. In the study, the problem of identifying the PID controller parameters is considered as an optimization problem. An attempt has been made to determine the PID parameters employing the PSO technique. A wide range of typical process models commonly encountered in industry is used to assess the efficacy of the PSO methodology. Comparisons are made between the PSO technique and other conventional methods using simulations and real-time control.

Thesis (M.S.)--Rochester Institute of Technology, 2008. / Typescript. Includes bibliographical references (leaves 71-73).