It is known that quantum strategies change the range of possible payoffs for the players in the prisoner's dilemma. In this paper, we examine the effect of the degree of entanglement in determining the payoffs. When both players play quantum strategies, we show that the payoff for both players is unaffected by the entanglement value and it leads to a new Nash equilibrium.

In this thesis, the problem of quantifying the Pareto optimal boundary of the achievable rate region is considered over multiple-input single-output(MISO)interference channels, where the problem boils down to solving a sequence of convex feasibility problems after certain transformations. The feasibility problem is solved by two new distributed optimal beam forming algorithms, where the first one is to parallelize the computation based on the method of alternating projections, and the second one is to localize the computation based on the method of cyclic projections. Convergence proofs are established for both algorithms.

Choice-based conjoint experiments are used when choice alternatives can be described in terms of attributes. The objective is to infer the value that respondents attach to attribute levels. This method involves the design of profiles on the basis of attributes specified at certain levels. Respondents are presented sets of profiles and asked to select the one they consider best. Choice sets with no dominating or dominated profiles are called Pareto optimal, and these Pareto optimal choice sets are provided to respondents. However, if choice sets have too many profiles, they may be difficult to implement. Therefore, we provide strategies for reducing the number of profiles in choice sets. We consider situations where only a subset of interactions is of interest, and obtain connected main effects plans with smaller choice sets for 2^n and 3^n designs that are capable of estimating subsets of interactions inclusive of one specific factor. We also provide plans for estimating all main effects and one two-way interaction for mixed level designs. Next, we examine the relationship between certain Pareto Optimal choice sets and g-designs. Finally, we obtain connected main effects plans with smaller choice sets for estimating different subsets of interactions, not inclusive of one specific factor. / Statistics

O presente trabalho propõe a utilização do método de otimização baseado no paradigma de enxame de partículas no projeto de um dispositivo eletromagnético, modelado analiticamente. A otimização baseada em enxames de partículas pertence à classe dos algoritmos evolutivos e é baseada no algoritmo de simulação do movimento de pássaros na busca por comida. O trabalho será focado na resolução de problemas de otimização multiobjetivo e apenas alguns casos de otimização mono-objetivo serão resolvidos para demonstrar a funcionalidade do método de otimização. Dois métodos de otimização multiobjetivo são propostos: um é baseado num algoritmo de otimização multiobjetivo que utiliza o paradigma de enxames em conjunto com soluções adotadas pelo algoritmo genético multiobjetivo denominado Nondominated Sorting Genetic Algorithm-II (NSGA-II) e o outro é baseado também no paradigma de enxames utilizando elementos do algoritmo de otimização multiobjetivo intitulado Strength Pareto Evolutionary Algorithm (SPEA). Ambos algoritmos são validados em um problema de otimização baseado no projeto de um motor de corrente contínua sem escovas, um benchmark de otimização. / This work proposes the use of the optimization method based on the particle swarm paradigm in the design of electromagnetic device, analytically modeled. Particle swarm optimization belongs to the class of evolutionary algorithms and is based on the movement simulation of birds searching for food. This work will be focused on solving multi-criteria optimization problems and some cases of single-objective optimization problems will be solved only to demonstrate the functionality of optimization method. Two multi-criteria optimization methods are proposed: one based on an optimization algorithm that uses the multiobjective particle swarm paradigm and some concepts extracted from the multiobjective genetic algorithm called Nondominated Sorting Genetic Algorithm-II (NSGA-II) and the other is based on the particle swarm paradigm by using some elements of another multiobjective optimization algorithm entitled Strength Pareto Evolutionary Algorithm (SPEA). Both methods are applied to an optimization problem related to the design of a brushless direct current motor.

O presente trabalho propõe a utilização do método de otimização baseado no paradigma de enxame de partículas no projeto de um dispositivo eletromagnético, modelado analiticamente. A otimização baseada em enxames de partículas pertence à classe dos algoritmos evolutivos e é baseada no algoritmo de simulação do movimento de pássaros na busca por comida. O trabalho será focado na resolução de problemas de otimização multiobjetivo e apenas alguns casos de otimização mono-objetivo serão resolvidos para demonstrar a funcionalidade do método de otimização. Dois métodos de otimização multiobjetivo são propostos: um é baseado num algoritmo de otimização multiobjetivo que utiliza o paradigma de enxames em conjunto com soluções adotadas pelo algoritmo genético multiobjetivo denominado Nondominated Sorting Genetic Algorithm-II (NSGA-II) e o outro é baseado também no paradigma de enxames utilizando elementos do algoritmo de otimização multiobjetivo intitulado Strength Pareto Evolutionary Algorithm (SPEA). Ambos algoritmos são validados em um problema de otimização baseado no projeto de um motor de corrente contínua sem escovas, um benchmark de otimização. / This work proposes the use of the optimization method based on the particle swarm paradigm in the design of electromagnetic device, analytically modeled. Particle swarm optimization belongs to the class of evolutionary algorithms and is based on the movement simulation of birds searching for food. This work will be focused on solving multi-criteria optimization problems and some cases of single-objective optimization problems will be solved only to demonstrate the functionality of optimization method. Two multi-criteria optimization methods are proposed: one based on an optimization algorithm that uses the multiobjective particle swarm paradigm and some concepts extracted from the multiobjective genetic algorithm called Nondominated Sorting Genetic Algorithm-II (NSGA-II) and the other is based on the particle swarm paradigm by using some elements of another multiobjective optimization algorithm entitled Strength Pareto Evolutionary Algorithm (SPEA). Both methods are applied to an optimization problem related to the design of a brushless direct current motor.

Mobile cloud computing is a way of delegating complex algorithms from a mobile device to the cloud to complete the tasks quickly and save energy on the mobile device. However, the cloud may not be available or suitable for helping all the time. For example, in a battlefield scenario, the cloud may not be reachable. This work considers neighbouring devices as alternatives to the cloud for offloading computation and presents three key contributions, namely a comprehensive investigation of the trade-off between computation and communication, Multi-Objective Optimisation based approach to offloading, and Queuing Theory based algorithms that present the benefits of offloading to neighbours. Initially, the states of neighbouring devices are considered to be known and the decision of computation offloading is proposed as a multi-objective optimisation problem. Novel Pareto optimal solutions are proposed. The results on a simulated dataset show up to 30% increment in performance even when cloud computing is not available. However, information about the environment is seldom known completely. In Chapter 5, a realistic environment is considered such as delayed node state information and partially connected sensors. The network of sensors is modelled as a network of queues (Open Jackson network). The offloading problem is posed as minimum cost problem and solved using Linear solvers. In addition to the simulated dataset, the proposed solution is tested on a real computer vision dataset. The experiments on the random waypoint dataset showed up to 33% boost on performance whereas in the real dataset, exploiting the temporal and spatial distribution of the targets, a significantly higher increment in performance is achieved.

Due to an increasing awareness of the importance of sustainable energy use, multi-objective optimization problems for upper-level energy systems are continually being developed and improved. This paper focuses on the modeling and optimization of the Helsinki district heating system and establishing the basis for modeling the Finnish power network. The optimization of the district heating system is conducted for a twenty four hour winter demand period. Partial load behavior of the generators is included by introducing non-linear functions for costs, emissions, and the exergetic efficiency. A fuel cost sensitivity analysis is conducted on the system by considering ten combinations of fuel costs based on high, medium, and low prices for each fuel. The solution sets, called Pareto fronts, are evaluated by post-processing techniques in order to determine the best solution from the optimal set. Because units between some of objective functions are non-commensurable, objective values are normalized and weighted. The results indicate that for today\'s fuel prices the best solution includes a dominating usage of natural gas technologies, while if the price of natural gas is higher than other fuels, natural gas technologies are often not included in the best solution. All of the necessary costs, emissions, and operating information is provided for the the Finnish power network in order to employ a multi-objective optimization on the system. / Master of Science

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Discrete choice experiments are used when choice alternatives can be described in terms of attributes. The objective is to infer the value that respondents attach to attribute levels. Respondents are presented sets of profiles based on attributes specified at certain levels and asked to select the profile they consider best. When the number of attributes or attribute levels becomes large, the profiles in a single choice set may be too numerous for respondents to make precise decisions. One strategy for reducing the size of choice sets is the sub-setting of attributes. However, the optimality of these reduced size choice sets has not been examined in the literature. We examine the optimality of reduced size choice sets for 2^n experiments using information per profile (IPP) as the optimality criteria. We propose a new approach for calculating the IPP of designs obtained by dividing attributes into two or more subsets with one, two, and in general, r overlapping attributes, and compare the IPP of the reduced size designs with the original full designs. Next we examine the IPP of choice designs based on 3^n factorial experiments. We calculate the IPP of reduced size designs obtained by sub-setting attributes in 3^n plans and compare them to the original full designs. / Statistics

Choice-based conjoint experiments are used when choice alternatives can be described in terms of attributes. The objective is to infer the value that respondents attach to attribute levels. This method involves the design of profiles on the basis of attributes specified at certain levels. Respondents are presented sets of profiles called choice sets, and asked to select the one they consider best. Sets with no dominating or dominated profiles are called Pareto Optimal sets. Information Per Profile (IPP) is used as an optimality criteria to compare designs with different numbers of profiles. For a 2^n experiment, the optimality of connected main effects plans based on two consecutive choice sets, Sl and Sl+1, has been examined in the literature. In this thesis we examine the IPP of both consecutive and non-consecutive choice sets and show that IPP can be maximized under certain conditions. We show that non-consecutive choice sets have higher IPP than consecutive choice sets for n ≥ 4. We also examine the optimality of connected first-order-interaction designs based on three choice sets and show that non-consecutive choice sets have higher IPP than consecutive choice sets under certain conditions. Further, we examine the D-, A- and E-optimality of consecutive and non-consecutive PO choice sets with maximum IPP. Finally, we consider 3^n choice experiments. We look for the optimal PO choice sets and examine their IPP, D-, A- and E-optimality, as well as comparing consecutive and non-consecutive choice sets. / Statistics