Ant algorithms have been used for a variety of combinatorial optimization problems. One of these problems, where ant algorithms haven't been used, is the design of transition rules for cellular automata (CA). Which is a problem that this master's thesis is focused on. This work begins with an introduction into ant algorithms and a overview of its applications, followed by an introduction into CA. In the next part the author proposes a way how to encode rules of CA into a graph which is used in ant algorithms. The last part of this thesis contains an application of encoded graph on elitist ant system and MAX-MIN ant system. This is followed by experimental results of creating transition rules for CA problems by these algorithms.

Empirical studies show that similar patterns emerge from a large number of different biological systems. For example, the group size distributions of several fish species and house sparrows all follow power law distributions with an exponential truncation. Networks built by ant colonies, slime mold and those are designed by engineers resemble each other in terms of structure and transportation efficiency. Based on the investigation of experimental data, we propose a variety of simple stochastic models to unravel the underlying mechanisms which lead to the collective phenomena in different systems. All the mechanisms employed in these models are rooted in the concept of selective reinforcement. In some systems the reinforcement can build optimal solutions for biological problem solving. This thesis consists of five papers. In the first three papers, I collaborate with biologists to look into group formation in house sparrows  and the movement decisions of damsel fish.  In the last two articles, I look at how shortest paths and networks are  constructed by slime molds and pheromone laying ants, as well as studying  speed-accuracy tradeoffs in slime molds' decision making. The general goal of the study is to better understand how macro level patterns and behaviors emerges from micro level interactions in both spatial and non-spatial biological systems. With the combination of mathematical modeling and experimentation, we are able to reproduce the macro level patterns in the studied biological systems and predict behaviors of the systems using minimum number of parameters.

Orientadores: Akebo Yamakami, Tatiane Regina Bonfim / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-19T06:06:35Z (GMT). No. of bitstreams: 1 Carvalho_MarciaBragade_D.pdf: 1895321 bytes, checksum: ae40a5cf6d05e99795952c1a7c6bed79 (MD5) Previous issue date: 2011 / Resumo: Este trabalho propõe novas abordagens híbridas baseadas em técnicas da computação bio-inspirada para o problema de escalonamento do tipo Job Shop. Como o problema do tipo job shop pertence a classe NP-difícil e não existe algoritmo exato capaz de solucionar todos os tipos deste problema. Normalmente é necessária a elaboração de métodos de resolução mais sofisticados para contornar essa alta complexidade. Desta forma, nesta tese propomos abordagens híbridas baseadas em algoritmo memético e algoritmo de otimização por colônia de formigas a fim de contornar essa complexidade e ser capaz de explorar eficientemente o espaço de busca obtendo resultados de alta qualidade. Os algoritmos híbridos propostos são aplicados tanto no problema de job shop com tempo de processamento preciso, como nos problemas de job shop com tempo de processamento incerto. No caso de problema com tempo de processamento incerto, os algoritmos visam encontrar um conjunto diversificado de escalonamentos com alto grau de possibilidade de serem ótimos / Abstract: This work proposes new hybrid approaches based on techniques of bio-inspired computing for the Job Shop scheduling problem. As the job shop scheduling problem is NP-hard and there is no exact algorithm capable of solving all kinds of this problem. Usually it is necessary to elaborate more sophisticated methods of resolution to overcome this high complexity. Thus, in this work we propose hybrid approaches based on memetic algorithm and ant colony optimization algorithm in order to explore the search space in an efficient manner and obtain high quality results. The proposed hybrid algorithms are applied in both the job shop scheduling problem with precise processing time, as in job shop scheduling problems with uncertain processing time. In the case of problem with uncertain processing time, the algorithms obtain a diversified set of schedules with high possibility of being optimal / Doutorado / Automação / Doutor em Engenharia Elétrica

Orientadores: Paulo de Barros Correia, Ieda Geriberto Hidalgo / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-28T12:05:23Z (GMT). No. of bitstreams: 1 Barros_RegianeSilvade_D.pdf: 4190585 bytes, checksum: c320645bbd13fd28d572f5b9751d4ff7 (MD5) Previous issue date: 2015 / Resumo: Este trabalho propõe um modelo de despacho ótimo no horizonte diário de operação, que permite coordenar a operação entre uma usina eólica e uma usina hidrelétrica. Nessa abordagem, a usina eólica é despachada em primeira instância. Para suprir eventuais saídas forçadas que possam ocorrer na geração eólica, aloca-se um valor de reserva girante incremental na usina hidrelétrica usando o conceito de Value at Risk como métrica de risco da geração eólica. O modelo é formulado como um problema multiobjetivo que busca maximizar a geração de energia e minimizar o número de partidas e paradas da usina hidrelétrica. O acoplamento hidráulico é considerado através da meta diária de defluência da usina. O problema é solucionado em duas etapas. A primeira resolve 24 problemas estáticos, que representam o despacho horário da usina hidrelétrica, separadamente. Essa etapa emprega o Algoritmo Genético para otimizar a operação da usina em termos da geração de energia elétrica. A segunda etapa soluciona o problema dinâmico, ou seja, o despacho diário da usina. A natureza do problema dinâmico, correspondendo à obtenção de caminhos mínimos eficientes em termos de partidas e paradas, sugeriu o uso da técnica de Otimização por Colônia de Formigas. As restrições de reserva girante, meta de defluência, atendimento do contrato de demanda e limites operacionais das usinas são plenamente satisfeitas. A diferença entre os montantes de energia produzidos e contratados é liquidada no mercado de curto prazo e valorada ao preço de liquidação das diferenças. O modelo se mostrou adequado em termos de tempo computacional e em relação à qualidade das soluções obtidas / Abstract: This work proposes an optimal dispatch model in the daily horizon, which coordinates the operation of a wind farm and a hydroelectric plant. In this approach the wind farm is dispatched first. In order to provide eventual faults that may occur in the wind farm generation, an incremental spinning reserve is allocated in the hydroelectric plant using the concept of Value at Risk. The model is formulated as a multiobjective problem which seeks to maximize the energy generation and to minimize the number of start-ups and shut-downs of the hydroelectric plant. The plant¿s hydraulic coupling is considered through the daily released flow goal. The model is solved in two stages, the first one solves, separately, 24 static problems that represents the hourly dispatch of the hydroelectric plant. This stage employs Genetic Algorithm to optimize the operation of the hydroelectric plant in terms of electric energy generation. The second stage considers the dynamic problem, which is the plant¿s daily dispatch. The nature of the dynamic problem, which implies in obtaining efficient shortest paths in terms of start-ups and shut-downs, suggests the use of the Ant Colony Optimization. The spinning reserve, the released flow goal, the demand contract and the generating unit¿s operational limits are fully satisfied. The difference between the energy amounts produced and contracted are liquidated in the spot market and it is valuated with the settlement differences price. Regarding computational costs and solutions quality, the model suitability is shown / Doutorado / Planejamento de Sistemas Energeticos / Doutora em Planejamento de Sistemas Energéticos

The research presented here supports the ongoing need for automatic heart volume calculation through the identification of the left and right ventricles in MRI images. The need for automated heart volume calculation stems from the amount of time it takes to manually processes MRI images and required esoteric skill set. There are several methods for region detection such as Deep Neural Networks, Support Vector Machines and Ant Colony Optimization. In this research Ant Colony Optimization (ACO) will be the method of choice due to its efficiency and flexibility. There are many types of ACO algorithms using a variety of heuristics that provide advantages in different environments and knowledge domains. All ACO algorithms share a foundational attribute, a heuristic that acts in conjunction with pheromones. These heuristics can work in various ways, such as dictating dispersion or the interpretation of pheromones. In this research a novel heuristic to disperse and act on pheromone is presented. Further, ants are applied to more general problem than the normal objective of finding edges, highly qualified region detection. The reliable application of heuristic oriented algorithms is difficult in a diverse environment. Although the problem space here is limited to MRI images of the heart, there are significant difference among them: the topology of the heart is different by patient, the angle of the scans changes and the location of the heart is not known. A thorough experiment is conducted to support algorithm efficacy using randomized sampling with human subjects. It will be shown during the analysis the algorithm has both prediction power and robustness.