Abstract In industry there is a high demand for algorithms that can efficiently solve search problems. Evolutionary Computing (EC) belonging to a class of heuristics are proven to be well suited to solve search problems, especially optimization tasks. They arrived at that location because of their flexibility, scalability and robustness. However, despite their advantages and increasing popularity, there are numerous opened questions in this research area, many of them related to the design and tuning of the algorithms. A neutral technique called Pseudo Redundancy and related concepts such as Updated Objective Grid (UOG) is proposed to tackle the mentioned problem making an evolutionary approach more suitable for ''real world'' applications while increasing its robustness and efficiency. The proposed UOG technique achieves neutral search by objective function transformation(s) resulting several advantageous features. (a) Simplifies the design of an evolutionary solver by giving population sizing principles and directions to choose the right selection operator. (b) The technique of updated objective grid is adaptive without introducing additional parameters, therefore no parameter tuning required for UOG to adjust it for different environments, introducing robustness. (c) The algorithm of UOG is simple and computationally cheap. (d) It boosts the performance of an evolutionary algorithm on high dimensional (constrained and unconstrained) problems. The theoretical and experimental results from artificial test problems included in this thesis clearly show the potential of the proposed technique. In order to demonstrate the power of the introduced methods under "real" circumstances, the author additionally designed EAs and performed experiments on two industrial optimization tasks. Although, only one project is detailed in this thesis while the other is referred. As the main outcome of this thesis, the author provided an evolutionary method to compute (optimal) daily water pump schedules for the water distribution network of Sopron, Hungary. The algorithm is currently working in industry.

Due to the high energy consumption demand and restrictions in the installation of new transmission lines, using Flexible AC Transmission System (FACTS) devices is inevitable. In power system analysis, transferring high-quality power is essential. In fact, one of the important factors that has a special role in terms of efficiency and operation is maximum power transfer capability. FACTS devices are used for controlling the voltage, stability, power flow and security of transmission lines. However, it is necessary to find the optimal location for these devices in power networks. Many optimization techniques have been deployed to find the optimal location for FACTS devices in power networks. There are several varieties of FACTS devices with different characteristics that are used for different purposes. The imperialist competitive algorithm (ICA) is a recently developed optimization technique that is used widely in power systems. This study presents an approach to find the optimal location and size of FACTS devices in power networks using the imperialist competitive algorithm technique. This technique is based on human social evolution. ICA technique is a new heuristic algorithm for global optimization searches that is based on the concept of imperialistic competition. This algorithm is used for mathematical issues; it can be categorized on the same level as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques. Also, in this study, the enhancement of voltage profile, stability and loss reduction and increasing of load-ability were investigated and carried out. In this case, to apply FACTS devices in power networks, the MATLAB program was used. Indeed, in this program all power network parameters were defined and analysed. IEEE 30-bus and IEEE 68-bus with 16 machine systems are used as a case study. All the simulation results, including voltage profile improvement and convergence characteristics, have been illustrated. The results show the advantages of the imperialist competitive algorithm technique over the conventional approaches.

Um avanÃo importante na modelagem de rede hidrÃulica foi a calibraÃÃo, atravÃs desta à possÃvel conhecer o comportamento das caracterÃsticas fÃsicas da rede, sendo esta de grande importÃncia nas tomadas de decisÃo. Neste trabalho vamos utilizar o mÃtodo transiente inverso aplicado com algoritmo genÃtico, para calibraÃÃo dos fatores de atritos das tubulaÃÃes de uma rede de distribuiÃÃo de Ãgua por meio de simulaÃÃes a partir da variaÃÃo de parÃmetros como: nÃmero de cromossomos, nÃmero de geraÃÃes, cruzamento de um ponto e mutaÃÃo simples, quantidade de nÃs medidos, e diferentes tipos de manobras de vÃlvulas (brusca e suave). As simulaÃÃes foram divididas em casos, onde cada soluÃÃo encontrada pelo modelo computacional foi avaliada por uma funÃÃo objetiva, baseada na diferenÃa quadrÃtica entre resultados observados e calculados para as cargas transientes no(s) nÃ(s) monitorado(s). As anÃlises das soluÃÃes encontradas demonstram como o mÃtodo inverso, o algoritmo genÃtico e a escolha de seus parÃmetros influenciam o resultado final. Por meio dos casos, observou-se que para as redes estudadas, nÃo adianta simplesmente aumentar isoladamente os valores dos parÃmetros do algoritmo genÃtico no intuito de melhorar a eficiÃncia do mÃtodo. Como se trata de um mÃtodo inverso, uma combinaÃÃo especÃfica de fator de atrito foi gerada a partir da mÃdia das soluÃÃes obtidas, sob os mesmos parÃmetros, em dez aplicaÃÃes sucessivas do algoritmo genÃtico (processamentos). Os resultados apresentados para as cargas hidrÃulicas no(s) nÃ(s) monitorado(s) foram bem prÃximas das cargas consideradas reais (observadas), tendo em alguns trechos das tubulaÃÃes valores para a forÃa de atrito bem prÃximos dos considerados reais. A eficiÃncia dos resultados encontrados foi medida por meio da FunÃÃo Objetiva. / An important advance in the hydraulic network modeling was the calibration through this is possible to know the behavior of the physical characteristics of the network, which is of great importance in decision-making. In this work we will use the reverse transient method applied with genetic algorithm for calibration of friction factors pipes of a water distribution network through simulations from the variation of parameters as the number of chromosomes, number of generations, passing a point mutation and simple, we measured quantity, and different types of maneuvering valve (sudden and soft). The simulations were divided into instances, wherein each solution found by computational model was evaluated by an objective function based on square difference between observed and calculated results for the transient loads in (s) node (s) monitored (s). The analyzes of the solutions shown as the inverse method, the algorithm genetic and the choice of its parameters influence the final result. Through cases, it was observed that for the studied networks, no use simply alone increase values ​​of the parameters of the genetic algorithm in order to improve the efficiency of method. As this is an inverse method, a specific combination of friction factor was generated from the average of these solutions under the same parameters in ten successive applications of genetic algorithm (processing). The results reported for hydraulic loads in (s) node (s) monitored (s) were very close considered the actual loads (Observed), in some parts of pipes values ​​for the friction force and considered close to real. The efficiency of the results was measured by Function Objetiv

Modeling and simulations are useful tools to help understand knee function and injuries. As there are more muscles in the human knee joint than equations of motion, optimization protocols are required to solve a problem. The purpose of this thesis was to improve the biofidelity of these simulations by adding in vivo constraints derived from experimental intra-cortical pin data and stability-inspired objective functions within an OpenSim-Matlab forward-inverse dynamics simulation framework on lower limb muscle activation predictions. Results of this project suggest that constraining the model knee joint’s ranges of motion with pin data had a significant impact on lower limb kinematics, especially in rotational degrees of freedom. This affected muscle activation predictions and knee joint loading when compared to unconstrained kinematics. Furthermore, changing the objective will change muscle activation predictions although minimization of muscle activation as an objective remains more accurate than the stability inspired functions, at least for gait. /// La modélisation et les simulations in-silico sont des outils importants pour approfondir notre compréhension de la fonction du genou et ses blessures. Puisqu’il y a plus de muscles autour du genou humain que d’équations de mouvement, des procédures d’optimisation sont requises pour résoudre le système. Le but de cette thèse était d’explorer l’effet de changer l’objectif de cette optimisation à des fonctions inspirées par la stabilité du genou par l’entremise d’un cadre de simulation de dynamique directe et inverse utilisant MatLab et OpenSim ainsi qu'un model musculo-squelétaire contraint cinématiquement par des données expérimentales dérivées de vis intra-corticales, sur les prédictions d’activation musculaire de la jambe. Les résultats de ce projet suggèrent que les contraintes de mouvement imposées sur le genou modélisé ont démontré des effets importants sur la cinématique de la jambe et conséquemment sur les prédictions d'activation musculaire et le chargement du genou. La fonction objective de l'optimisation change aussi les prédictions d’activations musculaires, bien que la fonction minimisant la consommation énergétique soit la plus juste, du moins pour la marche.

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This study analyzes how, in a possible electrification of the car fleet through plug-in hybrid electric vehicles (PHEV), the choice of objective function, which potentially reflects different stakeholders’ interests, may influence the resulting optimal PHEV battery range, the PHEV share in the vehicle fleet, the fleet total cost of ownership (TCO) savings, and the fleet electric drive fraction under various economic conditions and policy options. The optimal battery range can differ considerably among objective functions, especially between the objectives of maximizing the number of PHEVs and maximizing driving on electricity. Increased viability of the PHEV, for instance, through lower battery costs, higher running cost savings, or PHEV-promoting subsidies, will strengthen this effect. Therefore, a high share of viable PHEVs in the vehicle fleet does not necessarily result in a high share of electric driving. When designing policies to promote PHEVs, both the short- and long-term policy objectives and their potential effects need to be considered explicitly.

In this work we present a new tool for the convergence analysis of numerical optimization methods. It is based on the concepts of the Clarke derivative and set-valued mappings. Our goal is to apply this tool to minimization problems with non-smooth and noisy objective functions. After deriving a necessary condition for minimizers of such functions, we examine two unconstrained optimization routines. First, we prove new convergence theorems for Implicit Filtering and General Pattern Search. Then we show how these results can be used in practice, by executing some numerical computations. / Master of Science

Internet of Things (IoT) has been paving the way for a plethora of potential applications, which becomes more spatial and demanding. The goal of this work is to optimise the performance within the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) in the network layer. RPL still suffers from unbalanced load traffic among the candidate parents. Consequently, the overloaded parent node drains its energy much faster than other candidate parent nodes. This may lead to an early disconnection of a part of the network topology and affect the overall network reliability. To solve this problem, a new objective function (OF) has been proposed to usher better load balancing among the bottleneck candidate parents, and keep the overloaded nodes lifetime thriving to longer survival. Moreover, several IoT applications have antagonistic requirements but pertinent, which results in a greater risk of affecting the network reliability, especially within the emergency scenarios. With the presence of this challenging issue, the current standardised RPL OFs cannot sufficiently fulfil the antagonistic needs of Low-power and Lossy Networks (LLNs) applications. In response to the above issues, a context adaptive OF has been proposed to facilitate exchanging the synergy information between the application and network layers. Thus, the impact of the antagonistic requirements based on context parameters will be mitigated via rationalizing the selection decision of the routing path towards the root node. We implemented the proposed protocol and verified all our findings through excessive measurements via simulations and a realistic deployment using a real testbed of a multi-hop LLNs motes. The results proved the superiority of our solution over the existing ones with respect to end-to-end delay, packet delivery ratio and network lifetime. Our contribution has been accepted initially to be adopted within the standard body Internet Engineering Task Force (IETF).

In this project we introduced a spatial frequency-based objective function for optimization of dose distributions used in spatially fractionated radiotherapy (also known as grid therapy). Several studies indicate that tissues can tolerate larger mean doses of radiation if the dose is delivered heterogeneously or to a partial volume of the organ. The objective function rewards heterogeneous dose distributions in the collaterally irradiated healthy tissues and is based on the concept of a maximum stem-cell migration distance. The stem-cell depletion hypothesis stipulates that damaged tissues can be repopulated by nearby surviving stem-cells within a critical volume outlined by the maximum migration distance. Proton grid therapy dose distributions were calculated to study the viability of our spatial frequency-based objective function. These were computed analytically with a proton pencil beam dose kernel. A multi-slit collimator placed flush against the surface of a water phantom defined the entrance fluence. The collimator geometry was described by two free parameters: the slit width and the number of slits within a specified field width. Organs at risk (OARs) and a planning target volume (PTV) were defined. Two dose constraints were set on the PTV and objective function values were computed for the OARs. The objective function measures the high-frequency content of a masked dose distribution, where the distinction between low- and high frequencies was made based on a characteristic distance. Out of the feasible solutions, the irradiation geometry that produced the maximum objective function value was selected as the optimal solution. With the spatial frequency-based objective function we were able to find, by brute-force search, unique optimal solutions to the constrained optimization problem. The optimal solutions were found on the boundary of the solution space. The objective function can be applied directly to arbitrarily shaped regions of interest and to dose distributions produced by multiple field angles. The next step is to implement the objective function in an optimization environment of a commercial treatment planning system (TPS).

We develop efficient coordination techniques that support inelastic traffic in large-scale distributed dynamic spectrum access DSA networks. By means of any learning algorithm, the proposed techniques enable DSA users to locate and exploit spectrum opportunities effectively, thereby increasing their achieved throughput (or "rewards" to be more general). Basically, learning algorithms allow DSA users to learn by interacting with the environment, and use their acquired knowledge to select the proper actions that maximize their own objectives, thereby "hopefully" maximizing their long-term cumulative received reward/throughput. However, when DSA users' objectives are not carefully coordinated, learning algorithms can lead to poor overall system performance, resulting in lesser per-user average achieved rewards. In this thesis, we derive efficient objective functions that DSA users an aim to maximize, and that by doing so, users' collective behavior also leads to good overall system performance, thus maximizing each user's long-term cumulative received rewards. We show that the proposed techniques are: (i) efficient by enabling users to achieve high rewards, (ii) scalable by performing well in systems with a small as well as a large number of users, (iii) learnable by allowing users to reach up high rewards very quickly, and (iv) distributive by being implementable in a decentralized manner. / Graduation date: 2013