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This thesis contains analysis of currently available Objective-C run-time libraries (GCC, Apple and Étoilé run-times), their prerequisites and dependencies on the particular platform and operating system. The result of the analysis is a design of a modular run-time library that allows dynamic configuration of each component for the particular need (e.g. disabling run-time locks in a single-threaded environment). The resulting design can also be easily ported to other atypical platforms (e.g. kernel, or an experimental OS) and extended feature-wise (e.g. adding support for Objective-C categories, or associated objects). A prototype implementation of such a modular run-time for Objective-C also is included.

Jag fick ett flaskskepp i present när jag föddes. Det är ett rangligt skepp som ser ut att kunna falla sönder när som helst fast det är skyddat från yttre påverkan genom den tjocka flaskan. Flaskskeppen byggdes ofta av sjömän ute på långa resor på den tiden då en resa tog månader och inte timmar. De symboliserar en värld då tid sågs med andra ögon och där den manifesterades i objekt byggda av en yrkeskår som inte hade någon särskild koppling till konst. De hade framför allt tiden att bygga dessa båtar och kanske blev miniatyrerna de skapade objekt för dagdrömmar och resor som utspelade sig på ett mentalt hav samtidigt som de befann sig på ett fysiskt? / Thesis

Optimization methodologies are particularly relevant nowadays due to the ever-increasing power of computers and the enhancement of mathematical models to better capture reality. These computational methods are used in many different fields and some of them, such as metaheuristics, have often been found helpful and efficient for the resolution of practical applications where finding optimal solutions is not straightforward. Many practical applications are multi-objective optimization problems: there is more than one objective to optimize and the solutions found represent trade-offs between the competing objectives. In the last couple of decades, several metaheuristics approaches have been developed and applied to practical problems and multi-objective versions of the main single-objective approaches were created. The Alliance Algorithm (AA) is a recently developed single-objective optimization algorithm based on the metaphorical idea that several tribes, with certain skills and resource needs, try to conquer an environment for their survival and try to ally together to improve the likelihood of conquest. The AA method has yielded reasonable results in several fields to which it has been applied, thus the development in this thesis of a multi-objective variant to handle a wider range of problems is a natural extension. The first challenge in the development of the Multi-objective Alliance Algorithm (MOAA) was acquiring an understanding of the modifications needed for this generalization. The initial version was followed by other versions with the aim of improving MOAA performance to enable its use in solving real-world problems: the most relevant variations, which led to the final version of the approach, have been presented. The second major contribution in this research was the development and combination of features or the appropriate modification of methodologies from the literature to fit within the MOAA and enhance its potential and performance. An analysis of the features in the final version of the algorithm was performed to better understand and verify their behavior and relevance within the algorithm. The third contribution was the testing of the algorithm on a test-bed of problems. The results were compared with those obtained using well-known baseline algorithms. Moreover, the last version of the MOAA was also applied to a number of real-world problems and the results, compared against those given by baseline approaches, are discussed. Overall, the results have shown that the MOAA is a competitive approach which can be used `out-of-the-box' on problems with different mathematical characteristics and in a wide range of applications. Finally, a summary of the objectives achieved, the current status of the research and the work that can be done in future to further improve the performance of the algorithm is provided.

The work reviews a article published by N. T. Feather about five approaches which relate to the analysis of behavior in a choice situation where a decision is made between alternatives having different subjective probabilities of attainment. The relationship between choice potential and success probability is affect by the type of situation in which the choice is made.

Loudness growth evaluation is important to comprehend the theoretical implication of loudness in both normal hearing and hearing impaired people, as well as applied applications in hearing-aid design. However, current psychoacoustic procedures are subjective, time consuming and require the constant attention of participants. The primary aim of the present study is to investigate the feasibility of objectively assessing the loudness growth function by using the Auditory Brainstem Response (ABR). Previous studies applied either non-frequency specific click stimuli or tone burst stimuli to evoke auditory brainstem responses. Although the advantage of a chirp stimulus in producing a more reliable response has been well documented in many studies, no one has previously used this stimulus to evaluate loudness growth functions. One octave-band chirp stimuli with center frequencies of 1000 Hz and 4000 Hz were chosen to evoke ABRs at 7 different stimulus intensities from 20 dB nHL to 80 dB nHL with 10 dB steps. In the psychoacoustic procedure, subjects were asked to rate the perceived loudness of each presented stimulus. The recorded ABR trials were averaged by a modified version of weighted averaging based on Bayesian inference. This method of averaging decreases the effects of non-stationary noise sources by calculating a number of locally-stationary noise sources based on a series of F-tests. The peak-to-trough amplitude of the most salient peak of the ABR at each intensity constituted the physiological loudness estimate. Linear and power functions relating the psychoacoustical results and the ABR measurements were compared. The obtained results were in good agreement with equal-loudness contours and estimated loudness from the loudness model for time-varying sounds of Glasberg, & Moore (2002). We concluded that loudness growth can be estimated with ABRs to frequency-specific chirp stimuli. / Thesis / Master of Science (MSc)

In many everyday tasks it is necessary to compare the performance of the individuals in a population described by two or more criteria, for example comparing products in order to decide which is the best to purchase in terms of price and quality. Other examples are the comparison of universities, countries, the infrastructure in a telecommunications network, and the candidate solutions to a multi- or many-objective problem. In all of these cases, visualising the individuals better allows a decision maker to interpret their relative performance. This thesis explores methods for understanding and visualising multi- and many-criterion populations. Since people cannot generally comprehend more than three spatial dimensions the visualisation of many-criterion populations is a non-trivial task. We address this by generating visualisations based on the dominance relation which defines a structure in the population and we introduce two novel visualisation methods. The first method explicitly illustrates the dominance relationships between individuals as a graph in which individuals are sorted into Pareto shells, and is enhanced using many-criterion ranking methods to produce a finer ordering of individuals. We extend the power index, a method for ranking according to a single criterion, into the many-criterion domain by defining individual quality in terms of tournaments. The second visualisation method uses a new dominance-based distance in conjunction with multi-dimensional scaling, and we show that dominance can be used to identify an intuitive low-dimensional mapping of individuals, placing similar individuals close together. We demonstrate that this method can visualise a population comprising a large number of criteria. Heatmaps are another common method for presenting high-dimensional data, however they suffer from a drawback of being difficult to interpret if dissimilar individuals are placed close to each other. We apply spectral seriation to produce an ordering of individuals and criteria by which the heatmap is arranged, placing similar individuals and criteria close together. A basic version, computing similarity with the Euclidean distance, is demonstrated, before rank-based alternatives are investigated. The procedure is extended to seriate both the parameter and objective spaces of a multi-objective population in two stages. Since this process describes a trade-off, favouring the ordering of individuals in one space or the other, we demonstrate methods that enhance the visualisation by using an evolutionary optimiser to tune the orderings. One way of revealing the structure of a population is by highlighting which individuals are extreme. To this end, we provide three definitions of the “edge” of a multi-criterion mutually non-dominating population. All three of the definitions are in terms of dominance, and we show that one of them can be extended to cope with many-criterion populations. Because they can be difficult to visualise, it is often difficult for a decision maker to comprehend a population consisting of a large number of criteria. We therefore consider criterion selection methods to reduce the dimensionality with a view to preserving the structure of the population as quantified by its rank order. We investigate the efficacy of greedy, hill-climber and evolutionary algorithms and cast the dimension reduction as a multi-objective problem.

An algorithm is presented that is capable of producing Pareto-optimal solutions for multi-objective infrastructure routing problems: the Multi-Objective Ant Colony Optimization (MOACO). This algorithm offers a constructive search technique to develop solutions to different types of infrastructure routing problems on an open grid framework. The algorithm proposes unique functions such as graph pruning and path straightening to enhance both speed and performance. It also possesses features to solve issues unique to infrastructure routing not found in existing MOACO algorithms, such as problems with multiple end points or multiple possible start points. A literature review covering existing MOACO algorithms and the Ant Colony algorithms they are derived from is presented. Two case studies are developed to demonstrate the performance of the algorithm under different infrastructure routing scenarios. In the first case study the algorithm is implemented into the Ice Road Planning module within the North Slope Decision Support System (NSDSS). Using this ice road planning module a case study is developed of the White Hills Ice road to test the performance of the algorithm versus an as-built road. In the second case study, the algorithm is applied to a raw water transmission routing problem in the Region C planning zone of Texas. For both case studies the algorithm produces a set of results which are similar to the preliminary designs. By successfully applying the algorithm to two separate case studies the suitability of the algorithm to different types of infrastructure routing problems is demonstrated.

Applications for mobile devices, also known as apps, are today the backbone of a huge market where the company Apple is one of the key players. Apple develops, among other things, handheld electronic devices, which are powered by the operating system iOS. Through iOS, millions of customers get access to Apple’s App Store. During a period of two months, an app in the form of a game has been developed for iOS in the programming language Objective-C. The user of the app is presented intuitive menus and interactive graphical objects that seemingly obey physical laws. The app contains quick game sessions, a score system and a dynamic environment with gradually increasing difficulty. The app also plays background music and sound effects. The open source package Cocos2D with the integrated physics engine Box2D has been used as an aid during the app development.

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