Sensitivity analysis of dynamic cell formation problem through meta-heuristic

In spite of many researches in literature investigating dynamic of cell formation (CF) problem, further research needs to be elaborated to assay hidden aspects of cellular manufacturing system (CMS), due to inherent complexity and uncertainty on optimizing this problem. In this paper, sensitivity analysis of modified self-adaptive differential evolution (MSDE) algorithm is proposed for basic parameters of CF problem, considering to the graphical representation supported by statistical analysis. Hence, a dynamic integer model of CF problem is first presented as the NP-hard problem. Then, the two basic test CF problems are introduced thereby the performance of MSDE algorithm assessed by diverse problems sizes through 140 runs from aspects of the average runtime of algorithm and the best local optimum objective function. Finally, statistical analysis is implemented on behavior of objective function values in order to validate our computational results graphically as well as statistically, giving some insights related to importance of CF parameters on designing CMS

Multi-Objective Optimization for Wind Estimation and Aircraft Model Identification

In this paper, a novel method for aerodynamic model identification of a micro-air vehicle is proposed. The principal contribution is a technique of wind estimation that provides information about the existing wind during flight when no air-data sensors are available. The estimation technique employs multi-objective optimization algorithms that utilize identification errors to propose the wind-speed components that best fit the dynamic behavior observed. Once the wind speed is estimated, the flight experimentation data are corrected and utilized to perform an identification of the aircraft model parameters. A multi-objective optimization algorithm is also used, but with the objective of estimating the aerodynamic stability and control derivatives. Employing data from different flights offers the possibility of obtaining sets of models that form the Pareto fronts. Deciding which model best adjusts to the experiments performed (compromise model) will be the ultimate task of the control engineer.The authors would like to thank the Spanish Ministry of Innovation and Science for providing funding through grant BES-2012-056210 and projects TIN-2011-28082 and ENE-25900. We also want to acknowledge the Generalitat Valenciana for financing this work through project PROMETEO/2012/028.Velasco Carrau, J.; García-Nieto Rodríguez, S.; Salcedo Romero De Ávila, JV.; Bishop, RH. (2015). Multi-Objective Optimization for Wind Estimation and Aircraft Model Identification. Journal of Guidance, Control, and Dynamics. 39(2):372-389. https://doi.org/10.2514/1.G001294S37238939

Optimization using evolutionary metaheuristic techniques: a brief review

Optimization is necessary for finding appropriate solutions to a range of real life problems. Evolutionary-approach-based meta-heuristics have gained prominence in recent years for solving Multi Objective Optimization Problems (MOOP). Multi Objective Evolutionary Approaches (MOEA) has substantial success across a variety of real-world engineering applications. The present paper attempts to provide a general overview of a few selected algorithms, including genetic algorithms, ant colony optimization, particle swarm optimization, and simulated annealing techniques. Additionally, the review is extended to present differential evolution and teaching-learning-based optimization. Few applications of the said algorithms are also presented. This review intends to serve as a reference for further work in this domain

Identificación de modelos dinámicos y ajuste de controladores basado en algoritmos evolutivos multiobjetivo

[ES] Identificación de las expresiones que modelan los coeficientes aerodinámicos de una aeronave no tripulada (UAV) mediante diferentes metodologías. La primera de ellas hace uso de una regresión cuadrática, mientras que la segunda emplea un algoritmo de Diferenciación Evolutiva en la optimización de diversos objetivos.[EN] Identification of the expressions that model de aerodinamic coefficients of an Unmanned Aerial Vehicle (UAV) by means of two different aproaches. The first one makes use of a quadratic regression while the second one uses a Diferential Evolution algorithm in the optimization of multiple objectivesVelasco Carrau, J. (2013). Identificación de modelos dinámicos y ajuste de controladores basado en algoritmos evolutivos multiobjetivo. http://hdl.handle.net/10251/36468Archivo delegad

Otimização multiobjetivo utilizando algoritmos evolutivos em seleção de carteiras: uma abordagem envolvendo ômega, assimetria e antifragilidade

Profitability in investments has always been the desire of any investor, whether an individual or a company. In periods of declining interest rates in world economies, as well as the existence of an unstable performance of stock exchange assets due to recurringfinancial crises, such as the recent one caused by COVID-19, the profitability of Fixed and Variable Income assets is increasingly threatened.This context causes investors to increasingly search for assets that manage toreconcile profitability and a minimum of security in the composition of their portfolios. Itmakes the selection of asset portfolios undoubtedly one of the most challenging topics inthe Finance area.Since Markowitz’s initial contribution, several researchers have sought to studymethods, techniques, and models applicable to the topic. A crucial theoretical landmarkwas the proposal in the 1960s of the CAPM model, which has severe empirical flaws,although robust and consistent. Its empirical limitation is pointed out by the fact that themarket portfolio proxy idealized by the model does not take effect in practice. In additionto this, some premises as normal distribution and the quadratic utility function make theCAPM model less and less likely to succeed when implemented in practice.Therefore, new approaches have been presented, with a recent highlight for theOCAPM model, in which the Omega measure allows us to relax these CAPM premisesand represent the investor’s preference more effectively. Thus, new attributes that notonly mean and variance become relevant in the process of building new approaches to themodel, transforming the problem into a multiobjective approach.As OCAPM does not yet have a full empirical application, this research splits intothree parts: the first works with mono-objective optimization and seeks to empirically knowif the OCAPM model performs better than the CAPM model in the studied markets. Thesecond part works with the optimization of purely convex attributes. It aims to ratify theview that the mean and variance may not be sufficient to represent the entire distributionof return on assets and, therefore, investors’ decisions. The third part, the central part ofthe research, deals with the optimization of multiobjective portfolios involving convex andnon-convex attributes through the use of evolutionary algorithms.In this experiment, there are three multiobjective portfolios: i) Global, involvingthe optimization of the omega, mean, asymmetry, kurtosis, drawdown, and antifragilityattributes; ii) Antifragile, involving drawdown and antifragility and iii) Asymmetric,involving omega, skewness, and kurtosis.The results of the research show that the antifragile portfolio brought higher averagereturns than CAPM and OCAPM models, and the American market showed better riskconditions. Valuing assets that have a low drawdown and have relative resilience in times of turbulence becomes advantageous in investment management. Losing little in crisistimes seems to be more significant than winning in periods of calm and stability. Amongthe evolutionary algorithms used, the highlight is the NSGA3, which presented the bestperformance out of the sample in the optimization of multiobjective portfolios.A rentabilidade em investimentos sempre foi desejo de qualquer investidor, seja pessoa física ou jurídica. Em períodos de quedas das taxas de juros das economias mundiais,bem como a existência de um desempenho instável dos ativos das bolsas de valores devido a recorrentes crises financeiras, como a recente ocasionada pelo COVID-19, a rentabilidade de ativos de Renda Fixa e Variável está cada vez mais ameaçada.Esse contexto suscita nos investidores uma busca cada vez maior por ativos que consigam conciliar rentabilidade e um mínimo de segurança na composição de seus portfólios.Isso faz com que a seleção de carteiras de ativos seja, sem dúvida, um dos temas mais desafiadores da área de Finanças.Desde a contribuição inicial de Markowitz, diversos pesquisadores têm busca do estudar métodos, técnicas e modelos aplicáveis ao tema. Um marco teórico importante foi a proposição nos anos 60 do modelo CAPM que, embora robusto e consistente, apresenta falhas severas empíricas. Sua limitação empírica é apontada pelo fato da proxy da carteira de mercado idealizada pelo modelo não se efetivar na prática. Aliado a isso, algumas premissas como a normalidade da distribuição e a função utilidade quadrática tornam o modelo CAPM cada vez menos propenso ao sucesso quando implementado na prática.Diante disso, novas abordagens têm sido apresentadas, com destaque recente para o modelo OCAPM, em que a medida Ômega permite relaxar essas premissas do CAPM e pode representar com maior efetividade a preferência do investidor. Novos atributos que não somente a média e variância passam a ser relevantes no processo de tomada dedecisão do investidor, transformando o problema em uma abordagem multi objetiva.Como o OCAPM ainda não tem ampla aplicação empírica, esta pesquisa se divide em três partes: a primeira, trabalha com otimização mono-objetivo e busca conhecer empiricamente se o modelo OCAPM apresenta desempenho superior ao modelo CAPMnos mercados estudados; a segunda parte trabalha com uma otimização de atributos puramente convexos e visa ratificar a visão de que a média e variância podem não ser,por si só, suficientes para representar toda a distribuição de retorno dos ativos e, por conseguinte, da decisão dos investidores. A terceira parte, a principal da pesquisa, tratada otimização de carteiras multi objetivas que envolvam atributos convexos e não-convexosatravés do emprego de algoritmos evolutivos.Neste experimento, são propostas 03 carteiras multi objetivas:i) Global, envolvendo a otimização dos atributos ômega, média, assimetria, curtose, drawdowne antifragilidade;ii) Antifrágil, envolvendo drawdowne antifragilidade e iii) Assimétrica, envolvendo ômega,assimetria e curtose.Os resultados da pesquisa mostram que a carteira Antifrágil trouxe ganhos superiores em relação à média de retornos dos demais modelos e sobretudo no mercado americano apresentou melhores condições de risco. Valorizar ativos que apresentem baixo drawdowne possuam relativa resiliência em períodos de turbulência se torna vantajoso na gestão de investimentos. Perder pouco em momentos de crise parece ser mais significativo que ganharem períodos de bonança e estabilidade. Dentre os algoritmos evolutivos empregados, o destaque fica com o NSGA3, que apresentou o melhor desempenho fora da amostra na otimização de carteiras multi objetivas

Hybrid evolutionary techniques for constrained optimisation design

This thesis a research program in which novel and generic optimisation methods were developed so that can be applied to a multitude of mathematically modelled business problems which the standard optimisation techniques often fail to deal with. The continuous and mixed discrete optimisation methods have been investigated by designing new approaches that allow users to more effectively tackle difficult optimisation problems with a mix of integer and real valued variables. The focus of this thesis presents practical suggestions towards the implementation of hybrid evolutionary approaches for solving optimisation problems with highly structured constraints. This work also introduces a derivation of the different optimisation methods that have been reported in the literature. Major theoretical properties of the new methods have been presented and implemented. Here we present detailed description of the most essential steps of the implementation. The performance of the developed methods is evaluated against real-world benchmark problems, and the numerical results of the test problems are found to be competitive compared to existing methods