Resumo: Este trabalho apresenta uma metodologia composta de duas fases para resolver problemas de otimização com restrições usando uma estratégia multiobjetivo. Na primeira fase, o esforço concentra-se em encontrar, pelo menos, uma solução factível, descartando completamente a função objetivo. Na segunda fase, aborda-se o problema como biobjetivo, onde se busca a otimização da função objetivo original e maximizar o cumprimento das restrições. Na fase um propõe-se uma estratégia baseada na diminuição progressiva da tolerância de aceitação das restrições complexas para encontrar soluções factíveis. O desempenho do algoritmo é validado através de 11 casos testes bastantes conhecidos na literatura especializada. / Abstract: This work presents a two-phase framework for solving constrained optimization problems using a multi-objective strategy. In the first phase, the objective function is completely disregarded and entire search effort is directed toward finding a single feasible solution. In the second phase, the problem is treated as a bi-objective optimization problem, where the technique converts constrained optimization to a two-objective optimization: one is the original objective function; the other is the degree function violating the constraints. In the first phase a methodology based on progressive decrease of the tolerance of acceptance of complex constrains is proposed in order to find feasible solutions. The approach is tested on 11 well-know benchmark functions. / Orientador: Rubén Augusto Romero Lázaro / Coorientador: José Roberto Sanches Mantovani / Banca: Antonio Padilha Feltrin / Banca: Marcos Julio Rider Flores / Mestre

Den här rapporten undersöker egenskaper hos en algoritm som är baserad på Learning Backtracking Search Optimization Algorithm (LBSA) som introducerades av Chen et. al. (2017). Undersökningen genomfördes genom att tillämpa algoritmen på Sudokuproblemet och jämföra lösningsgraden och diversiteten i den sista populationen med en algoritm som är baserad på Hybrid Genetic Algorithm (HGA) som introducerades av Deng och Li (2011). Resultaten visar att implementationen av den LBSA-baserade algoritmen har en lägre lösningsgrad än den HGA-baserade algoritmen för alla genomförda experiment, men att algoritmen håller en högre diversitet i den sista populationen för tre av de fem gjorda experimenten. Slutsatsen är att den LBSA-baserade algoritmen inte är lämplig för att lösa Sudokuproblemet på grund av en låg lösningsgrad och att implementationen har en hög komplexitet. / This report examines the properties of an algorithm based on the Learning Backtracking Optimization Algorithm (LBSA) introduced by Chen et. al. (2017). The examination was performed by applying the algorithm on the Sudoku problem and then comparing the solution rate and the diversity in the final population with an algorithm based on the Hybrid Genetic Algorithm introduced by Deng and Li (2011). The results show the implementation of the LBSA based algorithm have a lower solution rate than the HGA based algorithm for all executed experiments. But the LBSA based algorithm manage to keep a higher diversity in the final population in three of the five performed experiments. The conclusion is that the LBSA based algorithm is not suitable for solving the Sudoku problem since the algorithm has a lower solution rate and the implementation have a high complexity.

Most real-world optimization problems behave stochastically. Evolutionary optimization algorithms have to cope with the uncertainty in order to not loose a substantial part of their performance. There are different types of uncertainty and this thesis studies the type that is commonly known as noise and the use of resampling techniques as countermeasure in multi-objective evolutionary optimization. Several different types of resampling techniques have been proposed in the literature. The available techniques vary in adaptiveness, type of information they base their budget decisions on and in complexity. The results of this thesis show that their performance is not necessarily increasing as soon as they are more complex and that their performance is dependent on optimization problem and environment parameters. As the sampling budget or the noise level increases the optimal resampling technique varies. One result of this thesis is that at low computing budgets or low noise strength simple techniques perform better than complex techniques but as soon as more budget is available or as soon as the algorithm faces more noise complex techniques can show their strengths. This thesis evaluates the resampling techniques on standard benchmark functions. Based on these experiences insights have been gained for the use of resampling techniques in evolutionary simulation optimization of real-world problems.

It is well known that the branching factor of a computer based board game has an effect on how long a searching AI algorithm takes to search through the game tree of the game. Something that is not as known is that the branching factor may have an additional effect for certain types of AI algorithms. The aim of this work is to evaluate if the win rate of an evolutionary AI algorithm is affected by the branching factor of the board game it is applied to. To do that, an experiment is performed where an evolutionary algorithm known as “Genetic Minimax” is evaluated for the two low branching factor board games Othello and Gomoku (Gomoku is also known as 5 in a row). The performance here is defined as how many times the algorithm manages to win against another algorithm. The results from this experiment showed both some promising data, and some data which could not be as easily interpreted. For the game Othello the hypothesis about this particular evolutionary algorithm appears to be valid, while for the game Gomoku the results were somewhat inconclusive. For the game Othello the performance of the genetic minimax algorithm was comparable to the alpha-beta algorithm it played against up to and including depth 4 in the game tree. After that however, the performance started to decline more and more the deeper the algorithms searched. The branching factor of the game may be an indirect cause of this behaviour, due to the fact that as the depth increases, the search space increases proportionally to the branching factor. This increase in the search space due to the increased depth, in combination with the settings used by the genetic minimax algorithm, may have been the cause of the performance decline after that point.

In this work, the potential of intelligent algorithms for optimizing the real power loss and enhancing the grid connection power factor in a real hydrocarbon facility electrical system is assessed. Namely, genetic algorithm (GA), improve strength Pareto evolutionary algorithm (SPEA2) and differential evolutionary algorithm (DEA) are developed and implemented. The economic impact associated with these objectives optimization is highlighted. The optimization of the subject objectives is addressed as single and multi-objective constrained nonlinear problems. Different generation modes and system injected reactive power cases are evaluated. The studied electrical system constraints and parameters are all real values. The uniqueness of this thesis is that none of the previous literature studies addressed the technical and economic impacts of optimizing the aforementioned objectives for real hydrocarbon facility electrical system. All the economic analyses in this thesis are performed based on real subsidized cost of energy for the kingdom of Saudi Arabia. The obtained results demonstrate the high potential of optimizing the studied system objectives and enhancing the economics of the utilized generation fuel via the application of intelligent algorithms.

This technical report presents the data and some results of the experimental research in the field of Human-Computer Interaction (Kansei Engineering), undertaken jointly by Technische Universität Chemnitz (Germany) and Novosibirsk State Technical University (Russia) in Feb- March 2016. In the experiment, 82 Master and Bachelor students of both universities evaluated 21 website of selected German and Russian universities per 10 emotional and 5 quality scales.

Optimisation is one of the most important and challenging part of any engineering design. In real world design problems one faces multiobjective optimisation under constraints. The optimal solution in these cases is not unique because the objectives can contradict each other. In such cases, a set of optimal solutions which forms a Pareto frontier in the objective space is considered. There are many algorithms to generate the Pareto frontier. However, only a few of them are potentially capable of providing an evenly distributed set of the solutions. Such a property is especially important in real-life design because a decision maker is usually able to analyse only a very limited quantity of solutions. This thesis consists of two main parts. At first, it develops and gives the detailed description of two different algorithms that are able to generate an evenly distributed Pareto set in a general formulation. One is a classical approach and called Directed Search Domain (DSD) and the other, the cylindrical constraint evolutionary algorithm (CCEA), is a hybrid population based method. The efficiency of the algorithms are demonstrated by a number of challenging test cases and the comparisons with the results of the other existing methods. It is shown that the proposed methods are successful in generating the Pareto solutions even when some existing methods fail. In real world design problems, deterministic approaches cannot provide a reliable solution as in the event of uncertainty, deterministic optimal solution would be infeasible in many instances. Therefore a solution less sensitive to problem perturbation is desirable. This leads to the robust solution which is the focus of the second part of the thesis. In the literature, there are some techniques tailored for robust optimisation. However, most of them are either computationally expensive or do not systematically articulate the designer preferences into a robust solution. In this thesis, by introducing a measure for robustness in multiobjective context, a tunable robust function (TRF) is presented. Including the TRF in the problem formulation, it is demonstrated that the desirable robust solution based on designer preferences can be obtained. This not only provides the robust solution but also gives a control over the robustness level. The method is efficient as it only increases the dimension of the problem by one irrespective of the dimension of the original problem.

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Thesis describes main attributes and principles of Evolutionary and Genetic algorithms. Crossover, mutation and selection are described as well as termination options. There are examples of practical use of evolutionary and genetic algorithms. Optimization of distribution routes using PHP&MySQL and Google Maps API technologies.

Tato disertační práce je zaměřena na vysokorychlostní simulace dopravy a jejich přesnou kalibraci pomocí různých typů dopravních dat. Práce se po úvodním popisu motivace pro samotný výzkum nejdříve věnuje současnému stavu poznání, a dále rozdělení simulátorů dopravy, zejména podle typu dat, se kterými se v nich pracuje. Úpravou existujícího řešení je navržen vlastní mikrosimulační model, který je založen na principu celulárního automatu. S tímto novým modelem je pak experimentováno, zpočátku z pohledu rychlosti simulace a další rozšiřitelnosti. Je navržena a popsána technika, kterou je možné navržený model významně akcelerovat a následně provádět simulace rychleji než v reálném čase i pro rozsáhlá území. Práce dále přistupuje k samotné kalibraci modelu, ke které byl využit evoluční přístup. Je představena metoda pro efektivní způsob optimalizace parametrů mikrosimulačního modelu, která nevyžaduje citlivostní analýzu a je schopná nalézt jinak obtížně nastavitelné parametry modelu. Kvalita získaných optimalizovaných modelů byla analyzována jak pomocí makroskopických, tak i mikroskopických dopravních dat, a to i s ohledem na jejich reálné vlastnosti, tj. chybějící vzorky. Dále jsou zhodnoceny výkonnostní a jiné kvalitativní parametry vlastního přístupu v porovnání s existujícími řešeními, přičemž bylo dosaženo významného zlepšení. Nakonec jsou představeny nástroje, které v rámci řešení vznikly. Na závěr je uvedeno další zaměření výzkumu, a to zejména s ohledem na využití výsledků práce v praxi.