A hybrid multi-objective evolutionary approach for optimal path planning of a hexapod robot

Hexapod robots are six-legged robotic systems, which have been widely investigated in the literature for various applications including exploration, rescue, and surveillance. Designing hexapod robots requires to carefully considering a number of different aspects. One of the aspects that require careful design attention is the planning of leg trajectories. In particular, given the high demand for fast motion and high-energy autonomy it is important to identify proper leg operation paths that can minimize energy consumption while maximizing the velocity of the movements. In this frame, this paper presents a preliminary study on the application of a hybrid multi-objective optimization approach for the computer-aided optimal design of a legged robot. To assess the methodology, a kinematic and dynamic model of a leg of a hexapod robot is proposed as referring to the main design parameters of a leg. Optimal criteria have been identified for minimizing the energy consumption and efficiency as well as maximizing the walking speed and the size of obstacles that a leg can overtake. We evaluate the performance of the hybrid multi-objective evolutionary approach to explore the design space and provide a designer with an optimal setting of the parameters. Our simulations demonstrate the effectiveness of the hybrid approach by obtaining improved Pareto sets of trade-off solutions as compared with a standard evolutionary algorithm. Computational costs show an acceptable increase for an off-line path planner. © Springer International Publishing Switzerland 2016

Stock Market Portfolio Management: A Walk-through

Stock market portfolio management has remained successful in drawing attention of number of researchers from the fields of computer science, finance and mathematics all around the world since years. Successfully managing stock market portfolio is the prime concern for investors and fund managers in the financial markets. This paper is aimed to provide a walk-through to the stock market portfolio management. This paper deals with questions like what is stock market portfolio, how to manage it, what are the objectives behind managing it, what are the challenges in managing it. As each coin has two sides, each portfolio has two elements – risk and return. Regarding this, Markowitz’s Modern Portfolio Theory, or Risk-Return Model, to manage portfolio is analyzed in detail along with its criticisms, efficient frontier, and suggested state-of-the-art enhancements in terms of various constraints and risk measures. This paper also discusses other models to manage stock market portfolio such as Capital Asset Pricing Model (CAPM) and Arbitrage Pricing Theory (APT) Model. DOI: 10.17762/ijritcc2321-8169.150613

Market risk management in a post-Basel II regulatory environment

We propose a novel method of Mean-Capital Requirement portfolio optimization. The optimization is performed using a parallel framework for optimization based on the Nondominated Sorting Genetic Algorithm II. Capital requirements for market risk include an additional stress component introduced by the recent Basel 2.5 regulation. Our optimization with the Basel 2.5 formula in the objective function produces superior results to those of the old (Basel II) formula in stress scenarios in which the correlations of asset returns change considerably. These improvements are achieved at the expense of reduced cardinality of Pareto-optimal portfolios. This reduced cardinality (and thus portfolio diversification) in periods of relatively low market volatility may have unintended consequences for banks’ risk exposure

Evaluation of an unsupervised learning approach for portfolio optimization

Throughout this directed research, we aim to identify opportunities for machine learning to support portfolio optimization. Based on a thorough literature review we decide to pursue an unsupervised learning approach and test its performance by conducting benchmarking against classic portfolio optimization techniques. To ensure the validity of our findings we explore the model’s robustness by conducting an array of experiments. In summary, we deem our version of the clustering algorithm to provide a suitable investment framework for return-focused investors with lower risk aversion. We suggest further research towards mitigating the algorithm’s inconsistencies and exploring additional tuning methodologies

An analysis of ensemble empirical mode decomposition applied to trend prediction on financial time series

Orientador : Luiz Eduardo S. OliveiraCoorientador : David MenottiDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 20/07/2017Inclui referências : f. 63-72Resumo: As séries temporais financeiras são notoriamente difíceis de analisar e prever dada sua natureza não estacionária e altamente oscilatória. Nesta tese, a eficácia da técnica de decomposição não-paramétrica Ensemble Empirical Mode Decomposition (EEMD) é avaliada como uma técnica de extração de característica de séries temporais provenientes de índices de mercado e taxas de câmbio, características estas usadas na classificação, juntamente com diferentes modelos de aprendizado de máquina, de tendências de curto prazo. Os resultados obtidos em dois datasets de dados financeiros distintos sugerem que os resultados promissores relatados na literatura foram obtidos com a adição, inadvertida, de lookahead bias (viés) proveniente da aplicação desta técnica como parte do pré-processamento das séries temporais. Em contraste com as conclusões encontradas na literatura, nossos resultados indicam que a aplicação do EEMD com o objetivo de gerar uma melhor representação dos dados financeiração, por si só, não é suficiente para melhorar substancialmente a precisão e retorno cumulativo obtidos por modelos preditivos em comparação aos resultados obtidos com a utilização de series temporais de mudanças percentuais. Palavras-chave: Predição de Tendencias, Aprendizado de Máquina, Séries Temporais Financeiras.Abstract: Financial time series are notoriously difficult to analyse and predict, given their nonstationary, highly oscillatory nature. In this thesis, the effectiveness of the Ensemble Empirical Mode Decomposition (EEMD) is evaluated at generating a representation for market indexes and exchange rates that improves short-term trend prediction for these financial instruments. The results obtained in two different financial datasets suggest that the promising results reported using EEMD on financial time series in other studies were obtained by inadvertently adding look-ahead bias to the testing protocol via pre-processing the entire series with EEMD, which do affect the predictive results. In contrast to conclusions found in the literature, our results indicate that the application of EEMD with the objective of generating a better representation for financial time series is not sufficient, by itself, to substantially improve the accuracy and cumulative return obtained by the same models using the raw data. Keywords: Trend Prediction, Machine Learning, Financial Time Series

Técnicas metaheurísticas de optimización multiobjetivo para resolver el problema del portafolio de inversión

189 páginas. Maestría en Optimización.El problema del portafolio de inversión consiste en la selección de un conjunto de activos de inversión. Los objetivos en general tienen que ver con la diversificación de la inversión: la minimización del riesgo y la maximización del retorno. El presente documento se organiza como se describe a continuación: el capítulo uno proporciona el marco teórico, relacionando el problema de inversión con el área de optimización multiobjetivo. En un segundo capítulo, se incluye el desarrollo de los métodos para la selección de poblaciones no dominadas, así como la descripción de las características y el modo operativo de las metaheurísticas manejadas. El tercer capítulo presenta la implementación del ajuste de parámetros, se presentan las métricas que se adoptaron. Posterior a ello, la etapa experimental es detallada y la configuración de los parámetros que se determinó. Finalmente, en el cuarto capítulo se presenta la parte experimental del proyecto, es decir, las corridas finales de los algoritmos con los parámetros ajustados, así como su análisis estadístico comparativo. Se incluye también una sección de comparación de los resultados con el estado del arte. Los apéndices están conformados por detalles de la metodología estadística usada, análisis a fondo acerca de algunos conceptos empleados en la investigación, así como información más detallada acerca de la metodología de ajuste de parámetros, así como su análisis estadístico

Complex Query Operators on Modern Parallel Architectures

Identifying interesting objects from a large data collection is a fundamental problem for multi-criteria decision making applications.In Relational Database Management Systems (RDBMS), the most popular complex query operators used to solve this type of problem are the Top-K selection operator and the Skyline operator.Top-K selection is tasked with retrieving the k-highest ranking tuples from a given relation, as determined by a user-defined aggregation function.Skyline selection retrieves those tuples with attributes offering (pareto) optimal trade-offs in a given relation.Efficient Top-K query processing entails minimizing tuple evaluations by utilizing elaborate processing schemes combined with sophisticated data structures that enable early termination.Skyline query evaluation involves supporting processing strategies which are geared towards early termination and incomparable tuple pruning.The rapid increase in memory capacity and decreasing costs have been the main drivers behind the development of main-memory database systems.Although the act of migrating query processing in-memory has created many opportunities to improve the associated query latency, attaining such improvements has been very challenging due to the growing gap between processor and main memory speeds.Addressing this limitation has been made easier by the rapid proliferation of multi-core and many-core architectures.However, their utilization in real systems has been hindered by the lack of suitable parallel algorithms that focus on algorithmic efficiency.In this thesis, we study in depth the Top-K and Skyline selection operators, in the context of emerging parallel architectures.Our ultimate goal is to provide practical guidelines for developing work-efficient algorithms suitable for parallel main memory processing.We concentrate on multi-core (CPU), many-core (GPU), and processing-in-memory architectures (PIM), developing solutions optimized for high throughout and low latency.The first part of this thesis focuses on Top-K selection, presenting the specific details of early termination algorithms that we developed specifically for parallel architectures and various types of accelerators (i.e. GPU, PIM).The second part of this thesis, concentrates on Skyline selection and the development of a massively parallel load balanced algorithm for PIM architectures.Our work consolidates performance results across different parallel architectures using synthetic and real data on variable query parameters and distributions for both of the aforementioned problems.The experimental results demonstrate several orders of magnitude better throughput and query latency, thus validating the effectiveness of our proposed solutions for the Top-K and Skyline selection operators