Power law analysis of financial index dynamics

Power law PL and fractional calculus are two faces of phenomena with long memory behavior. This paper applies PL description to analyze different periods of the business cycle. With such purpose the evolution of ten important stock market indices DAX, Dow Jones, NASDAQ, Nikkei, NYSE, S&P500, SSEC, HSI, TWII, and BSE over time is studied. An evolutionary algorithm is used for the fitting of the PL parameters. It is observed that the PL curve fitting constitutes a good tool for revealing the signal main characteristics leading to the emergence of the global financial dynamic evolution.

A fuzzified systematic adjustment of the robotic Darwinian PSO

The Darwinian Particle Swarm Optimization (DPSO) is an evolutionary algorithm that extends the Particle Swarm Optimization using natural selection to enhance the ability to escape from sub-optimal solutions. An extension of the DPSO to multi-robot applications has been recently proposed and denoted as Robotic Darwinian PSO (RDPSO), benefiting from the dynamical partitioning of the whole population of robots, hence decreasing the amount of required information exchange among robots. This paper further extends the previously proposed algorithm adapting the behavior of robots based on a set of context-based evaluation metrics. Those metrics are then used as inputs of a fuzzy system so as to systematically adjust the RDPSO parameters (i.e., outputs of the fuzzy system), thus improving its convergence rate, susceptibility to obstacles and communication constraints. The adapted RDPSO is evaluated in groups of physical robots, being further explored using larger populations of simulated mobile robots within a larger scenario.

Introducing the fractional-order Darwinian PSO

One of the most well-known bio-inspired algorithms used in optimization problems is the particle swarm optimization (PSO), which basically consists on a machinelearning technique loosely inspired by birds flocking in search of food. More specifically, it consists of a number of particles that collectively move on the search space in search of the global optimum. The Darwinian particle swarm optimization (DPSO) is an evolutionary algorithm that extends the PSO using natural selection, or survival of the fittest, to enhance the ability to escape from local optima. This paper firstly presents a survey on PSO algorithms mainly focusing on the DPSO. Afterward, a method for controlling the convergence rate of the DPSO using fractional calculus (FC) concepts is proposed. The fractional-order optimization algorithm, denoted as FO-DPSO, is tested using several well-known functions, and the relationship between the fractional-order velocity and the convergence of the algorithm is observed. Moreover, experimental results show that the FO-DPSO significantly outperforms the previously presented FO-PSO.

A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers

In this paper, we formulate the electricity retailers’ short-term decision-making problem in a liberalized retail market as a multi-objective optimization model. Retailers with light physical assets, such as generation and storage units in the distribution network, are considered. Following advances in smart grid technologies, electricity retailers are becoming able to employ incentive-based demand response (DR) programs in addition to their physical assets to effectively manage the risks of market price and load variations. In this model, the DR scheduling is performed simultaneously with the dispatch of generation and storage units. The ultimate goal is to find the optimal values of the hourly financial incentives offered to the end-users. The proposed model considers the capacity obligations imposed on retailers by the grid operator. The profit seeking retailer also has the objective to minimize the peak demand to avoid the high capacity charges in form of grid tariffs or penalties. The non-dominated sorting genetic algorithm II (NSGA-II) is used to solve the multi-objective problem. It is a fast and elitist multi-objective evolutionary algorithm. A case study is solved to illustrate the efficient performance of the proposed methodology. Simulation results show the effectiveness of the model for designing the incentive-based DR programs and indicate the efficiency of NSGA-II in solving the retailers’ multi-objective problem.

A fractional calculus perspective in the evolutionary design of combinational circuits

This paper analyses the performance of a genetic algorithm (GA) in the synthesis of digital circuits using two novel approaches. The first concept consists in improving the static fitness function by including a discontinuity evaluation. The measure of variability in the error of the Boolean table has similarities with the function continuity issue in classical calculus. The second concept extends the static fitness by introducing a fractional-order dynamical evaluation.

A Fractional Calculus Perspective in the Evolutionary Design of Combinational Circuits

This paper analyses the performance of a genetic algorithm (GA) in the synthesis of digital circuits using two novel approaches. The first concept consists in improving the static fitness function by including a discontinuity evaluation. The measure of variability in the error of the Boolean table has similarities with the function continuity issue in classical calculus. The second concept extends the static fitness by introducing a fractional-order dynamical evaluation.

Lanczos algorithm on the grassmann manifold

5th. European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2008) 8th. World Congress on Computational Mechanics (WCCM8)

Prevalência de sintomas neuro-músculo-esqueléticos nos agricultores

Objectivos: O presente estudo teve como objectivos avaliar a prevalência da sintomatologia neuro-músculo-esquelética nos agricultores, identificar os seus factores de risco e avaliar a implementação de um projecto comunitário na sintomatologia neuro-músculo-esquelética dos agricultores. Metodologia: O estudo foi dividido em Estudo A e B. A amostra do Estudo A foi constituída por 250 agricultores seleccionados por amostragem consecutiva em 5 Cooperativas Agrícolas da Região Agrária entre Douro e Minho. A amostra do Estudo B foi constituída por 10 agricultores da Freguesia de Britelo - Concelho de Ponte da Barca, que aceitaram participar nas actividades do projecto (acção de educação e programa de exercícios específicos). Os dados foram recolhidos, por entrevista, através do Questionário de Avaliação dos Agricultores e o Questionário Nórdico Músculo-Esquelético. A análise estatística foi realizada recorrendo ao programa Statistical Package for Social Sciences, versão 17.0, considerando um nível de significância de 0,05. Resultados: No Estudo A observou-se que 74,4% dos agricultores referiram sintomatologia neuro-músculo-esquelético durante as actividades agrícolas e as regiões mais afectadas, foram a lombar, o pescoço e os ombros. Encontrou-se também uma associação significativa (p<0.05) entre a presença de sintomas nos agricultores e alguns factores de risco. Relativamente ao Estudo B, verificou-se uma diminuição significativa (p<0.05) na intensidade média de dor referida na lombar em algumas actividades e um aumento significativo (p<0.05) na pontuação final dos conhecimentos sobre os factores de risco. Conclusão: A população agrícola apresenta factores de risco que levam ao surgimento de sintomas neuro-músculo-esqueléticos, os quais podem ser prevenidos com a implementação de projectos comunitários.

Reorganização dos componentes neuro-motores com influência na marcha em indivíduos com lesão do sistema nervoso central - impacto funcional

Objectivo: Avaliar o impacto qualitativo de um programa de intervenção em fisioterapia, segundo o Conceito de Bobath, nas actividades e participação de dois indivíduos com lesão do Sistema Nervoso Central. Avaliar as modificações no comportamento da actividade muscular ao longo da fase de apoio do ciclo da marcha, na força de reacção ao solo e respectiva contribuição muscular. Metodologia: A avaliação realizou-se antes e após um programa de intervenção, segundo a abordagem do Conceito de Bobath, através da Classificação Internacional de Funcionalidade, Incapacidade e Saúde, electromiografia, plataforma de forças e máquina fotográfica. Resultados: Obteve-se melhorias na restrição da participação e na limitação da actividade. Verifica-se uma tendência de modificação do comportamento muscular ao longo da fase de apoio e na componente antero-posterior (Fy), mais evidente no mecanismo de aceleração. A mudança na contribuição muscular para a este mecanismo é mais evidente. Conclusão: O programa de intervenção, segundo o Conceito de Bobath, induziu mudanças positivas quanto à funcionalidade dos indivíduos, reflectindo-se na possibilidade de reorganização dos componentes neuro-motores em indivíduos com lesão do Sistema Nervoso Central.

Distributed energy resource short-term scheduling using signaled particle swarm optimization

Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.

Genetic algorithm methodology applied to intelligent house control

In recent years the use of several new resources in power systems, such as distributed generation, demand response and more recently electric vehicles, has significantly increased. Power systems aim at lowering operational costs, requiring an adequate energy resources management. In this context, load consumption management plays an important role, being necessary to use optimization strategies to adjust the consumption to the supply profile. These optimization strategies can be integrated in demand response programs. The control of the energy consumption of an intelligent house has the objective of optimizing the load consumption. This paper presents a genetic algorithm approach to manage the consumption of a residential house making use of a SCADA system developed by the authors. Consumption management is done reducing or curtailing loads to keep the power consumption in, or below, a specified energy consumption limit. This limit is determined according to the consumer strategy and taking into account the renewable based micro generation, energy price, supplier solicitations, and consumers’ preferences. The proposed approach is compared with a mixed integer non-linear approach.

Ant colony search algorithm for the optimal power flow problem

To maintain a power system within operation limits, a level ahead planning it is necessary to apply competitive techniques to solve the optimal power flow (OPF). OPF is a non-linear and a large combinatorial problem. The Ant Colony Search (ACS) optimization algorithm is inspired by the organized natural movement of real ants and has been successfully applied to different large combinatorial optimization problems. This paper presents an implementation of Ant Colony optimization to solve the OPF in an economic dispatch context. The proposed methodology has been developed to be used for maintenance and repairing planning with 48 to 24 hours antecipation. The main advantage of this method is its low execution time that allows the use of OPF when a large set of scenarios has to be analyzed. The paper includes a case study using the IEEE 30 bus network. The results are compared with other well-known methodologies presented in the literature.

Prevalência de sintomas neuro-músculo-esqueléticos nos agricultores

Introdução: A agricultura é considerada uma actividade fisicamente árdua, acarretando riscos para a saúde dos seus trabalhadores. Objectivo: Avaliar a prevalência de sintomatologia neuro-músculo-esquelética em agricultores e identificar os seus factores de risco. Métodos: Os Questionários Avaliação dos Agricultores e o Nórdico Músculo-Esquelético foram aplicados a 250 agricultores da Região Agrária entre Douro e Minho. Resultados: 74,4% dos agricultores referiram sintomatologia, principalmente, na lombar, pescoço e ombros. A presença de sintomas estava significativamente associada a alguns factores de risco (p<0.05). Conclusão: Os agricultores constituem uma população de risco para o surgimento de sintomas neuro-músculo-esqueléticos.

Prevalência e análise de factores de risco de problemas neuro-músculo-esqueléticos em músicos

Introdução: A prevalência de problemas neuro-músculo-esqueléticos nos músicos é elevada, pois estão sujeitos a grande exigência física e psicológica. Objectivos: Analisar a prevalência de factores de risco em marimbistas e caracterizar a postura da coluna vertebral na situação de tocar. Métodos: A recolha das situações de risco foi realizada através de um questionário e a postura da coluna, numa amostra de 10 marimbistas, analisada pelo SAPO. Resultados: As posturas entre as situações sem tocar e a tocar um excerto difícil são significativamente diferentes. Conclusão: Os marimbistas têm uma grande prevalência de sintomas sendo necessários programas de educação e promoção de saúde.

Optimal dispatch with reactive power compensation by Genetic Algorithm

This paper presents a Unit Commitment model with reactive power compensation that has been solved by Genetic Algorithm (GA) optimization techniques. The GA has been developed a computational tools programmed/coded in MATLAB. The main objective is to find the best generations scheduling whose active power losses are minimal and the reactive power to be compensated, subjected to the power system technical constraints. Those are: full AC power flow equations, active and reactive power generation constraints. All constraints that have been represented in the objective function are weighted with a penalty factors. The IEEE 14-bus system has been used as test case to demonstrate the effectiveness of the proposed algorithm. Results and conclusions are dully drawn.