An option is a financial instrument which derives its value from an underlying asset. There are a wide range of options traded today. Some are simple and plain, like the European options, while others are very difficult to evaluate. Both buyers and sellers continue to look for efficient algorithms and faster technology to price options for profit. In this thesis, I will first map the PSO parameters to the parameters in the option pricing problem. Then, I extend this to study pricing of complex chooser option. Further, I design a parallel algorithm that avails of the inherent concurrency in PSO while searching for a optimum solution. For implementation of my algorithm I used graphics processor unit (GPU). Analyzing the characteristics of PSO and option pricing, I propose a strategy to normalize some of the PSO parameters that helps in better understanding the sensitivity of various parameters on option pricing results.

In this thesis, a Particle Swarm Optimization (PSO) method combined with a ray propagation method is presented as a means to optimally locate multiple antennas in an indoor environment. This novel approach uses Particle Swarm Optimisation combined with geometric partitioning. The PSO algorithm uses swarm intelligence to determine the optimal transmitter location within the building layout. It uses the Keenan-Motley indoor propagation model to determine the fitness of a location. If a transmitter placed at that optimum location, transmitting a maximum power is not enough to meet the coverage requirements of the entire indoor space, then the space is geometrically partitioned and the PSO initiated again independently in each partition. The method outputs the number of antennas, their effective isotropic radiated power (EIRP) and physical location required to meet the coverage requirements. An example scenario is presented for a real building at Loughborough University and is compared against a conventional planning technique used widely in practice.

Made available in DSpace on 2014-06-12T16:00:06Z (GMT). No. of bitstreams: 2 arquivo5998_1.pdf: 1180748 bytes, checksum: e5d0a95fe8d45bdddd6975b365971603 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2007 / Este trabalho propõe uma metodologia para a otimização global de redes neurais MLP. O objetivo é a otimização simultânea de arquiteturas e pesos sinápticos de redes MLP, na tentativa de proporcionar um bom desempenho de classificação para qualquer conjunto de dados. A otimização simultânea de arquiteturas e pesos de redes neurais é uma abordagem interessante para a obtenção de redes eficientes com maior poder de generalização, pois cria um compromisso entre baixa complexidade estrutural do modelo e baixos índices de erro de treinamento. Tal aplicação já foi bastante investigada com a utilização de métodos de busca metaheurística tais como algoritmos genéticos, recozimento simulado, busca tabu e combinações dos mesmos. Outra técnica de busca meta-heurística menos investigada neste contexto é a otimização por enxame de partículas (PSO) que vem recebendo cada vez mais atenção da comunidade científica devido aos bons resultados obtidos ao lidar com problemas de otimização numérica contínua. A metodologia desenvolvida neste trabalho consiste na aplicação de dois algoritmos PSOs, um para a otimização de arquiteturas e outro para o ajuste dos pesos sinápticos de cada arquitetura gerada pelo primeiro PSO. Estes dois processos são intercalados por um número específico de iterações. Este trabalho apresenta resultados da aplicação da metodologia proposta em três conhecidas bases de dados de problemas de classificação de padrões de domínio médico. Nos problemas mais difíceis de classificar, a metodologia apresentada obteve resultados satisfatórios e gerou redes com baixo erro de generalização e baixa complexidade. Tais resultados são relevantes para mostrar que a técnica meta-heurística de otimização por enxames de partículas é uma opção efetiva para o ajuste de pesos e arquiteturas de redes neurais MLP

Many scientific, engineering and economic problems involve the optimisation of a set of parameters. These problems include examples like minimising the losses in a power grid by finding the optimal configuration of the components, or training a neural network to recognise images of people's faces. Numerous optimisation algorithms have been proposed to solve these problems, with varying degrees of success. The Particle Swarm Optimiser (PSO) is a relatively new technique that has been empirically shown to perform well on many of these optimisation problems. This thesis presents a theoretical model that can be used to describe the long-term behaviour of the algorithm. An enhanced version of the Particle Swarm Optimiser is constructed and shown to have guaranteed convergence on local minima. This algorithm is extended further, resulting in an algorithm with guaranteed convergence on global minima. A model for constructing cooperative PSO algorithms is developed, resulting in the introduction of two new PSO-based algorithms. Empirical results are presented to support the theoretical properties predicted by the various models, using synthetic benchmark functions to investigate specific properties. The various PSO-based algorithms are then applied to the task of training neural networks, corroborating the results obtained on the synthetic benchmark functions. / Thesis (PhD)--University of Pretoria, 2007. / Computer Science / Unrestricted

Evolutionary algorithms and swarm intelligence techniques have been shown to successfully solve optimization problems where the goal is to find a single optimal solution. In multimodal domains where the goal is the locate multiple solutions in a single search space, these techniques fail. Niching algorithms extend existing global optimization algorithms to locate and maintain multiple solutions concurrently. In this thesis, strategies are developed that utilize the unique characteristics of the particle swarm optimization algorithm to perform niching. Shrinking topological neighborhoods and optimization with multiple subswarms are used to identify and stably maintain niches. Solving systems of equations and multimodal functions are used to demonstrate the effectiveness of the new algorithms. / Dissertation (MS)--University of Pretoria, 2005. / Computer Science / unrestricted

This research investigates how public service organisations (PSO’s) use public procurement, referred to as the acquisition of goods and services by PSOs, to analyse the processes through which a PSO acquires innovative goods and services in order to improve public services. Despite a number of success stories from the literature (Phillips et al, 2007; Uyarra, 2010; Yeow et al, 2011), PSOs struggle to procure and implement innovation (e.g. Uyarra et al, 2014a). One major reason for this lack of innovation procurement and adoption is the nature of governance of the procurement process in the public sector (Rolfstam, 2009).Drawing from the public sector and organisational governance literature, this research develops a conceptual framework to investigate how internal, managerial and external governance affects the willingness and ability of PSOs to procure innovative goods and services. External governance refers to overarching bodies of organisations and institutions situated outside the PSO which influences policy and organisational arrangements of PSOs. Managerial governance refers to organisational actors and other stakeholders brought together to form governing boards which directly control and support the PSO leader. Internal governance refers to the day-to-day operations and delivery of a public service. This research adopts a positivist approach with a deductive inquiry process. Using the English secondary education system as the PSO under investigation this research utilises a mixture of quantitative (survey to two types of secondary schools in England) and qualitative methods (four case studies). The findings of this research indicate that these three governance levels influence PSOs procuring innovation in a number of ways. External governance can determine the decision-making process and what can and cannot be procured to improve the service and how budgets are used for innovations. External governance can also act as a source of expertise and knowledge, create opportunities and incentivise PSOs by establishing conditions, mechanisms and access to large scale programmes and initiatives. Similarly, managerial governance entails actors to act as gatekeepers in the decision making process, assisting in procurements by leveraging expertise from other positions and improving the chances of procuring innovation through partnership arrangements with internal governance actors. At the internal governance level, procurement of innovation is greatly improved when ‘champions’ support innovative solutions and when staff responsible for the delivery of the service (i.e. teachers) specify requirements. This research makes three contributions. Firstly, it develops a conceptual framework for public procurement of innovation (PPI) with governance at the centre. Secondly, it adds to the growing body of literature of PPI practice and the barriers faced by PSOs. Finally, this research pays attention to education, a public service sector that has been overlooked by previous studies. Consequently, this research may help policy-makers and practitioners to better understand the governance of PPI.

Optimization forms an intrinsic part of the design and implementation of modern systems, such as industrial systems, communication networks, and the configuration of electric or electronic components. Population-based single-solution optimization algorithms such as Particle Swarm Optimization (PSO) have been shown to perform well when a number of optimal or suboptimal solutions exist. However, some problems require algorithms that locate all or most of these optimal and suboptimal solutions. Such algorithms are known as niching or speciation algorithms. Several techniques have been proposed to extend the PSO paradigm so that multiple optima can be located and maintained within a convoluted search space. A significant number of these implementations are subswarm-based, that is, portions of the swarm are optimized separately. Niches are formed to contain these subswarms, a process that often requires user-specified parameters, as the sizes and placing of the niches are unknown. This thesis presents a new niching technique that uses the vector dot product of the social and cognitive direction vectors to determine niche boundaries. Thus, a separate niche radius is calculated for each niche, a process that requires minimal knowledge of the search space. This strategy differs from other techniques using niche radii where a niche radius is either required to be set in advance, or calculated from the distances between particles. The development of the algorithm is traced and tested extensively using synthetic benchmark functions. Empirical results are reported using a variety of settings. An analysis of the algorithm is presented as well as a scalability study. The performance of the algorithm is also compared to that of two other well-known PSO niching algorithms. To conclude, the vector-based PSO is extended to locate and track multiple optima in dynamic environments. / Thesis (PhD)--University of Pretoria, 2010. / Computer Science / unrestricted

This work deals with two optimization problems, traveling salesman problem and cluster analysis. Solution of these optimization problems are applied on INVEA-TECH company needs. It shortly describes questions of optimization and some optimization techniques. Closely deals with swarm intelligence, strictly speaking particle swarm intelligence. Part of this work is recherché of variants of particle swarm optimization algorithm. The second part describes PSO algorithms solving clustering problem and traveling salesman problem and their implementation in Matlab language.

This dissertation presents an analysis of the angle modulated particle swarm optimisation (AMPSO) algorithm. AMPSO is a technique that enables one to solve binary optimisation problems with particle swarm optimisation (PSO), without any modifications to the PSO algorithm. While AMPSO has been successfully applied to a range of optimisation problems, there is little to no understanding of how and why the algorithm might fail. The work presented here includes in-depth theoretical and emprical analyses of the AMPSO algorithm in an attempt to understand it better. Where problems are identified, they are supported by theoretical and/or empirical evidence. Furthermore, suggestions are made as to how the identified issues could be overcome. In particular, the generating function is identified as the main cause for concern. The generating function in AMPSO is responsible for generating binary solutions. However, it is shown that the increasing frequency of the generating function hinders the algorithm’s ability to effectively exploit the search space. The problem is addressed by introducing methods to construct different generating functions, and to quantify the quality of arbitrary generating functions. In addition to this, a number of other problems are identified and addressed in various ways. The work concludes with an empirical analysis that aims to identify which of the various suggestions made throughout this dissertatioin hold substantial promise for further research. / Dissertation (MSc)--University of Pretoria, 2017. / Computer Science / MSc / Unrestricted

Particle swarm optimisation (PSO) is a stochastic, population-based optimisation algorithm. PSO has been applied successfully to a variety of domains. This thesis examines the behaviour of PSO when applied to high dimensional optimisation problems. Empirical experiments are used to illustrate the problems exhibited by the swarm, namely that the particles are prone to leaving the search space and never returning. This thesis does not intend to develop a new version of PSO speci cally for high dimensional problems. Instead, the thesis investigates why PSO fails in high dimensional search spaces. Four di erent types of approaches are examined. The rst is the application of velocity clamping to prevent the initial velocity explosion and to keep particles inside the search space. The second approach selects values for the acceleration coe cients and inertia weights so that particle movement is restrained or so that the swarm follows particular patterns of movement. The third introduces coupling between problem variables, thereby reducing the swarm's movement freedom and forcing the swarm to focus more on certain subspaces within the search space. The nal approach examines the importance of initialisation strategies in controlling the swarm's exploration to exploitation ratio. The thesis shows that the problems exhibited by PSO in high dimensions, particularly unwanted particle roaming, can not be fully mitigated by any of the techniques examined. The thesis provides deeper insight into the reasons for PSO's poor performance by means of extensive empirical tests and theoretical reasoning. / Dissertation (MSc)--University of Pretoria, 2017. / Computer Science / MSc / Unrestricted