Robust and Constrained Portfolio Optimization using Multiobjective Evolutionary Algorithms

Optimization plays an important role in many areas of science, management,economics and engineering. Many techniques in mathematics and operation research are available to solve such problems. However these techniques have many shortcomings to provide fast and accurate solution particularly when the optimization problem involves many variables and constraints. Investment portfolio optimization is one such important but complex problem in computational finance which needs effective and efficient solutions. In this problem each available asset is judiciously selected in such a way that the total profit is maximized while simultaneously minimizing the total risk. The literature survey reveals that due to non availability of suitable multi objective optimization tools, this problem is mostly being solved by viewing it as a single objective optimization problem

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Energy-Efficient Technologies for High-Performance Manufacturing Industries

Ph.DDOCTOR OF PHILOSOPH

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Computational intelligence techniques for data analysis

Ph.DDOCTOR OF PHILOSOPH

Optimal energy control of a grid connected solar-wind based electric power plant.

Doctor of Philosophy in Electrical Engineering. University of KwaZulu-Natal, Howard College 2016.In the present context of urge energy demand, renewable energy is considered as an alternative source of clean energy. In view of the increase in the price of fossil fuel due to its rarity and emissions, more integration of renewable sources is needed for better economic management of the grid. This research work has been done in two parts. The first part deals with the daily energy consumption variations for the low demand season and high demand season on weekdays and weekends. The intention is to correlate the corresponding fuel cost and estimate the operational efficiency of the hybrid system, which comprises the PV, PW, DG, battery system, for a period of 24 hours taken as control horizon. The latest published research literature has shown that a good deal of work has been done using a fixed load and uniform daily operational cost. The economic dispatch strategy, fuel cost, energy flows and energy sales are analysed in this study. The results show that a renewable energy system, which combines the PV/PW/diesel/battery models, achieves more fuel saving during both the high demand and low demand seasons than a model where the diesel generator satisfies the load on its own. The fuel cost during the low demand and high demand seasons for weekdays and weekends shows considerable fluctuations, which should not be neglected if accurate operational costs are to be obtained. The model shows the achievement of a more practical estimate of fuel costs, which reflects the fluctuation of power consumption behaviour for any given model. In the last part of the thesis model predictive control (MPC) is introduced in the management and control of power flow. The highlight in this thesis is the management of the energy flow from the hydro pump, wind, photovoltaic system and turbine when the system is subject to severe disturbances. The results demonstrated in the thesis prove the advantages of the approach and its robustness against uncertainties and external disturbances. When analysed with the open loop control system, MPC is more robust because of its stability of the system when external disturbances occur in the system. This thesis presents a practical solution to energy sale, control, optimization and management

Adaptive bio-inspired firefly and invasive weed algorithms for global optimisation with application to engineering problems

The focus of the research is to investigate and develop enhanced version of swarm intelligence firefly algorithm and ecology-based invasive weed algorithm to solve global optimisation problems and apply to practical engineering problems. The work presents two adaptive variants of firefly algorithm by introducing spread factor mechanism that exploits the fitness intensity during the search process. The spread factor mechanism is proposed to enhance the adaptive parameter terms of the firefly algorithm. The adaptive algorithms are formulated to avoid premature convergence and better optimum solution value. Two new adaptive variants of invasive weed algorithm are also developed seed spread factor mechanism introduced in the dispersal process of the algorithm. The working principles and structure of the adaptive firefly and invasive weed algorithms are described and discussed. Hybrid invasive weed-firefly algorithm and hybrid invasive weed-firefly algorithm with spread factor mechanism are also proposed. The new hybridization algorithms are developed by retaining their individual advantages to help overcome the shortcomings of the original algorithms. The performances of the proposed algorithms are investigated and assessed in single-objective, constrained and multi-objective optimisation problems. Well known benchmark functions as well as current CEC 2006 and CEC 2014 test functions are used in this research. A selection of performance measurement tools is also used to evaluate performances of the algorithms. The algorithms are further tested with practical engineering design problems and in modelling and control of dynamic systems. The systems considered comprise a twin rotor system, a single-link flexible manipulator system and assistive exoskeletons for upper and lower extremities. The performance results are evaluated in comparison to the original firefly and invasive weed algorithms. It is demonstrated that the proposed approaches are superior over the individual algorithms in terms of efficiency, convergence speed and quality of the optimal solution achieved

Modelo de decisão integrado para a priorização multiestágio de projetos de distribuição considerando a qualidade da energia elétrica

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Elétrica, Florianópolis, 2013.O presente trabalho aborda o problema de priorização dos projetos de melhoria e expansão do sistema de distribuição de energia elétrica, cujo foco é a maximização do valor do portfólio corporativo. Esse problema combinatório multiobjetivo é estruturado na forma de um modelo de decisão, formulado por meio de programação matemática binária, cuja solução envolve uma técnica de otimização bioinspirada, combinada a um conjunto de métodos de análise multicritério que buscam subsidiar o processo decisório da empresa. A primeira etapa faz uso do algoritmo genético multiobjetivo NSGA-II para obter um conjunto de portfólios Pareto-Ótimos, onde os projetos são selecionados e alocados em um horizonte de planejamento multiestágio, de acordo com os objetivos e restrições do problema. Os objetivos estão associados aos critérios de valor dos portfólios, os quais consideram os impactos financeiros potenciais dos projetos, o número de consumidores atendidos, as condições operacionais das instalações elétricas e a qualidade da energia elétrica no sistema de distribuição. As restrições envolvem a disponibilidade orçamentária e as relações de condicionamento e excludência entre os projetos. Na segunda etapa, os métodos de análise multicritério SMART e TOPSIS são utilizados para determinar os ranques das atratividades dos portfólios, incorporando o perfil das preferências dos decisores por meio dos pesos dos critérios, os quais são obtidos pelos métodos ROC e AHP. Os estudos de caso demonstram o comportamento da prioridade dos projetos nos portfólios não somente quando a qualidade da energia e o desempenho operacional são incluídos na análise, mas também em função da variação dos pesos dos critérios de planejamento. A metodologia proposta permite auxiliar na prospecção de investimentos estratégicos e contribuir para um melhor planejamento do sistema de distribuição Abstract : This work tackles the problem of project selection for the improvement of power distribution systems, which is part of the utilities planning task. The developed model is composed by a multi-objective optimization module and a multi-criteria decision support module. The first finds the Pareto-optimal portfolios, by using the multi-objective genetic algorithm NSGA-II to select and allocate the projects in a multistage planning horizon, according to the problem objectives and constraints. The latter, based on both TOPSIS and SMART multicriteria techniques, searches for the most appropriate project portfolio for the utility, considering the decision maker profile embedded into the model by the ROC and AHP weights. The optimization and decision making models take into account aspects of power quality, operational performance, number of consumers, and potential financial impacts of the projects. The presented case studies show the choice of priority projects not only when power quality and operational performance are included in the analysis, but also when the weights of planning criteria are changed. The proposed method helps in raising strategic investments and allows for better distribution system planning

Estimating Dependences and Risk between Gold Prices and S&P500: New Evidences from ARCH,GARCH, Copula and ES-VaR models

This thesis examines the correlations and linkages between the stock and commodity in order to quantify the risk present for investors in financial market (stock and commodity) using the Value at Risk measure. The risk assessed in this thesis is losses on investments in stock (S&P500) and commodity (gold prices). The structure of this thesis is based on three empirical chapters. We emphasise the focus by acknowledging the risk factor which is the non-stop fluctuation in the prices of commodity and stock prices. The thesis starts by measuring volatility, then dependence which is the correlation and lastly measure the expected shortfalls and Value at risk (VaR). The research focuses on mitigating the risk using VaR measures and assessing the use of the volatility measures such as ARCH and GARCH and basic VaR calculations, we also measured the correlation using the Copula method. Since, the measures of volatility methods have limitations that they can measure single security at a time, the second empirical chapter measures the interdependence of stock and commodity (S&P500 and Gold Price Index) by investigating the risk transmission involved in investing in any of them and whether the ups and downs in the prices of one effect the prices of the other using the Time Varying copula method. Lastly, the third empirical chapter which is the last chapter, investigates the expected shortfalls and Value at Risk (VaR) between the S&P500 and Gold prices Index using the ES-VaR method proposed by Patton, Ziegel and Chen (2018). Volatility is considered to be the most popular and traditional measure of risk. For which we have used ARCH and GARCH model in our first empirical chapter. However, the problem with volatility is that it does not take into account the direction of an investments’ movement: volatility of stocks is that they suddenly jump higher and investors are not distressed with gains. When we talk about investors for them the risk is about the odds of losing money, after my research and findings VaR is based on the common-sense fact. Hence, investors care about the odds of big losses, VaR answers the question, what is my worst-case scenario? Or simply how much I could lose in a really bad month? The results of the thesis demonstrated that measuring volatility (ARCH GARCH) alone was not sufficient in measuring the risk involved in an investment therefore methodologies such as correlation and VAR demonstrates better results. In terms of measuring the interdependence, the Time Varying Copula is used since the dynamic structure of the de- pendence between the data can be modelled by allowing either the copula function or the dependence parameter to be time varying. Lastly, hybrid model further demonstrates the average return on a risky asset for which Expected Shortfall (ES) along with some quantile dependence and VaR (Value at risk) is utilised. Basel III Accord which is applied in coming years till 2019 focuses more on ES unlike VaR, hence there is little existing work on modelling ES. The thesis focused on the results from the model of Patton, Ziegel and Chen (2018) which is based on the statistical decision theory. Patton, Ziegel and Chen (2018), overcame the problem of elicitability for ES by using ES and VaR jointly and propose the new dynamic model of risk measure. This research adds to the contribution of knowledge that measuring risk by using volatility is not enough for measuring risk, interdependence helps in measuring the dependency of one variable over the other and estimations and inference methods proposed by Patton, Ziegel and Chen (2018) using simulations proposed in ES-VaR model further concludes that ARCH and GARCH or other rolling window models are not enough for determining the risk forecasts. The results suggest, in first empirical chapter we see volatility between Gold prices and S&P500. The second empirical chapter results suggest conditional dependence of the two indexes is strongly time varying. The correlation between the stock is high before 2008. The results further displayed slight stronger bivariate upper tail, which signifies that the conditional dependence of the indexes is influence by positive shocks. The last empirical chapter findings proposed that measuring forecasts using ES-Var model proposed by Patton, Ziegel and Chen (2018) does outer perform forecasts based on univariate GARCH model. Investors want to 10 protect themselves from high losses and ES-VaR model discussed in last chapter would certainly help them to manage their funds properly