MARCOS ALVES - Academia.edu (original) (raw)
Papers by MARCOS ALVES
Smartphone users' satisfaction is related to several factors of interaction that represent cr... more Smartphone users' satisfaction is related to several factors of interaction that represent criteria evaluated in the choice of the operating system. This paper investigates the usage satisfaction of Android, iOS and Windows Phone mobile operating systems users' and deals with multicriteria decision making. A survey was applied and 314 answers were obtained. The evaluated criteria and the scores obtained through the survey were the initial parameters for the TOPSIS mul-ticriteria decision-making method. The method classified the three alternatives based on preferences over screen, terminology, learning and system capabilities. The iOS was the best classified in the proposed evaluations. Android got the second place in the Screen factors. In the others, Windows Phone was second and Android was third. A small variation in the weights did not change the order of classification found. Although ranking should reflect the decision makers preference, further investigations can be ca...
Applied Soft Computing, 2020
In this paper we introduce a Non-Stationary Fuzzy Time Series (NSFTS) method with time varying pa... more In this paper we introduce a Non-Stationary Fuzzy Time Series (NSFTS) method with time varying parameters adapted from the distribution of the data. In this approach, we employ Non-Stationary Fuzzy Sets, in which perturbation functions are used to adapt the membership function parameters in the knowledge base in response to statistical changes in the time series. The proposed method is capable of dynamically adapting its fuzzy sets to reflect the changes in the stochastic process based on the residual errors, without the need to retraining the model. This method can handle non-stationary and heteroskedastic data as well as scenarios with concept-drift. The proposed approach allows the model to be trained only once and remain useful long after while keeping reasonable accuracy. The flexibility of the method by means of computational experiments was tested with eight synthetic non-stationary time series data with several kinds of concept drifts, four real market indices (Dow Jones, NASDAQ, SP500 and TAIEX), three real FOREX pairs (EUR-USD, EUR-GBP, GBP-USD), and two real cryptocoins exchange rates (Bitcoin-USD and Ethereum-USD). As competitor models the Time Variant fuzzy time series and the Incremental Ensemble were used, these are two of the major approaches for handling non-stationary data sets. Non-parametric tests are employed to check the significance of the results. The proposed method shows resilience to concept drift, by adapting parameters of the model, while preserving the symbolic structure of the knowledge base.
Applied Soft Computing, 2020
Solving many-objective problems (MaOPs) is still a significant challenge in the multi-objective o... more Solving many-objective problems (MaOPs) is still a significant challenge in the multi-objective optimization (MOO) field. One way to measure algorithm performance is through the use of benchmark functions (also called test functions or test suites), which are artificial problems with a well-defined mathematical formulation, known solutions and a variety of features and difficulties. In this paper we propose a parameterized generator of scalable and customizable benchmark problems for MaOPs. It is able to generate problems that reproduce features present in other benchmarks and also problems with some new features. We propose here the concept of generative benchmarking, in which one can generate an infinite number of MOO problems, by varying parameters that control specific features that the problem should have: scalability in the number of variables and objectives, bias, deceptiveness, multimodality, robust and non-robust solutions, shape of the Pareto front, and constraints. The proposed Generalized Position-Distance (GPD) tunable benchmark generator uses the position-distance paradigm, a basic approach to building test functions, used in other benchmarks such as Deb, Thiele, Laumanns and Zitzler (DTLZ), Walking Fish Group (WFG) and others. It includes scalable problems in any number of variables and objectives and it presents Pareto fronts with different characteristics. The resulting functions are easy to understand and visualize, easy to implement, fast to compute and their Pareto optimal solutions are known.
Anais do 14º Simpósio Brasileiro de Automação Inteligente, 2019
This paper presents a method for the power dispatching problem in a smart park that uses plug-in ... more This paper presents a method for the power dispatching problem in a smart park that uses plug-in electric vehicles as storage units. The objective is to find the best energy storage planning for 10 vehicles over one day (24 hours), in order to minimize the total cost of energy bought from the grid by the smart park. An evolutionary algorithm was applied to solve the problem, considering possible alterations of arrival/departure times of vehicles. At each hour the algorithm was reinitialized and updated values for grid electricity prices, energy needs and solar energy were given by means of a fuzzy time-series model. The results presented an average cost of 21,576(20executions)andaveragetimeof6.45seconds.Theproposedapproachwasabletojoinsucessfullywithtime−seriesmodel,dynamicoptimizationandevolutionaryalgorithmstosolveaproblemofpowerdispatching.Resumo:Esteartigoapresentaumapropostaparaoproblemadedespachodeenergiaemumestacionamentointeligentequeusaveıˊculoseleˊtricoscomounidadesdearmazenamento.Oobjetivoprincipaleˊencontraromelhorplanejamentodearmazenamentodeenergiapara10veıˊculosaolongodeumdia(24horas),afimdeminimizarocustototaldaenergiacompradadaredepeloestacionamento.Umalgoritmoevolucionaˊriofoiaplicadopararesolveroproblema,levandoemconsiderac\caopossıˊveisalterac\coesnoshoraˊriosdeentrada/saıˊdadosveıˊculos.Acadahoraoalgoritmoerareinicializadoevaloresatualizadosparaosprec\cosdaenergia,necessidadedeenergiaeenergiasolareramgeradosporummodelodeseˊriestemporaisfuzzy.Osresultadosapresentaramumcustomeˊdiode21, 576 (20 executions) and average time of 6.45 seconds. The proposed approach was able to join sucessfully with time-series model, dynamic optimization and evolutionary algorithms to solve a problem of power dispatching. Resumo: Este artigo apresenta uma proposta para o problema de despacho de energia em um estacionamento inteligente que usa veículos elétricos como unidades de armazenamento. O objetivo principalé encontrar o melhor planejamento de armazenamento de energia para 10 veículos ao longo de um dia (24 horas), a fim de minimizar o custo total da energia comprada da rede pelo estacionamento. Um algoritmo evolucionário foi aplicado para resolver o problema, levando em consideração possíveis alterações nos horários de entrada/saída dos veículos. A cada hora o algoritmo era reinicializado e valores atualizados para os preços da energia, necessidade de energia e energia solar eram gerados por um modelo de séries temporais fuzzy. Os resultados apresentaram um custo médio de 21,576(20executions)andaveragetimeof6.45seconds.Theproposedapproachwasabletojoinsucessfullywithtime−seriesmodel,dynamicoptimizationandevolutionaryalgorithmstosolveaproblemofpowerdispatching.Resumo:Esteartigoapresentaumapropostaparaoproblemadedespachodeenergiaemumestacionamentointeligentequeusaveıˊculoseleˊtricoscomounidadesdearmazenamento.Oobjetivoprincipaleˊencontraromelhorplanejamentodearmazenamentodeenergiapara10veıˊculosaolongodeumdia(24horas),afimdeminimizarocustototaldaenergiacompradadaredepeloestacionamento.Umalgoritmoevolucionaˊriofoiaplicadopararesolveroproblema,levandoemconsiderac\caopossıˊveisalterac\coesnoshoraˊriosdeentrada/saıˊdadosveıˊculos.Acadahoraoalgoritmoerareinicializadoevaloresatualizadosparaosprec\cosdaenergia,necessidadedeenergiaeenergiasolareramgeradosporummodelodeseˊriestemporaisfuzzy.Osresultadosapresentaramumcustomeˊdiode21.576 (20 execuções) e tempo médio de 6.45 segundos. A abordagem proposta foi capaz de combinar eficazmente modelos de séries temporais, otimização dinâmica e algoritmos evolucionários para resolver um problema de despacho de energia.
Proceedings XIII Brazilian Congress on Computational Inteligence, 2018
Proceedings XIII Brazilian Congress on Computational Inteligence, 2018
Fuzzy Multi-criteria decision making methods have been provided to help the decision-makers in th... more Fuzzy Multi-criteria decision making methods have been provided to help the decision-makers in their complex decisions about future uncertainties. Taking into consideration uncertainties such as vagueness and future scenarios, this paper aims to apply the methods Fuzzy-MultiMoora, Fuzzy-Topsis Linear, Fuzzy-Topsis Vector, Fuzzy-Vikor and Fuzzy-Waspas in a Hydrothermal Dispatch problem. Five scenarios were evaluated by varying hydrology and energy demand parameters, from very pessimistic to very optimistic. Two decision makers made explicit their preferences weighting three criteria: Cost, Rationing and Distance. The normalized fuzzy numbers were calculated using the concept of alpha-cuts. Finally, the indexes were aggregated into a final ordering considering weights for the methods based on the Kendall tau distance. The best solutions were compared in relation to the criteria. It was observed that these solutions presented good results in all scenarios evaluated.
Smartphone users' satisfaction is related to several factors of interaction that represent cr... more Smartphone users' satisfaction is related to several factors of interaction that represent criteria evaluated in the choice of the operating system. This paper investigates the usage satisfaction of Android, iOS and Windows Phone mobile operating systems users' and deals with multicriteria decision making. A survey was applied and 314 answers were obtained. The evaluated criteria and the scores obtained through the survey were the initial parameters for the TOPSIS mul-ticriteria decision-making method. The method classified the three alternatives based on preferences over screen, terminology, learning and system capabilities. The iOS was the best classified in the proposed evaluations. Android got the second place in the Screen factors. In the others, Windows Phone was second and Android was third. A small variation in the weights did not change the order of classification found. Although ranking should reflect the decision makers preference, further investigations can be ca...
Applied Soft Computing, 2020
In this paper we introduce a Non-Stationary Fuzzy Time Series (NSFTS) method with time varying pa... more In this paper we introduce a Non-Stationary Fuzzy Time Series (NSFTS) method with time varying parameters adapted from the distribution of the data. In this approach, we employ Non-Stationary Fuzzy Sets, in which perturbation functions are used to adapt the membership function parameters in the knowledge base in response to statistical changes in the time series. The proposed method is capable of dynamically adapting its fuzzy sets to reflect the changes in the stochastic process based on the residual errors, without the need to retraining the model. This method can handle non-stationary and heteroskedastic data as well as scenarios with concept-drift. The proposed approach allows the model to be trained only once and remain useful long after while keeping reasonable accuracy. The flexibility of the method by means of computational experiments was tested with eight synthetic non-stationary time series data with several kinds of concept drifts, four real market indices (Dow Jones, NASDAQ, SP500 and TAIEX), three real FOREX pairs (EUR-USD, EUR-GBP, GBP-USD), and two real cryptocoins exchange rates (Bitcoin-USD and Ethereum-USD). As competitor models the Time Variant fuzzy time series and the Incremental Ensemble were used, these are two of the major approaches for handling non-stationary data sets. Non-parametric tests are employed to check the significance of the results. The proposed method shows resilience to concept drift, by adapting parameters of the model, while preserving the symbolic structure of the knowledge base.
Applied Soft Computing, 2020
Solving many-objective problems (MaOPs) is still a significant challenge in the multi-objective o... more Solving many-objective problems (MaOPs) is still a significant challenge in the multi-objective optimization (MOO) field. One way to measure algorithm performance is through the use of benchmark functions (also called test functions or test suites), which are artificial problems with a well-defined mathematical formulation, known solutions and a variety of features and difficulties. In this paper we propose a parameterized generator of scalable and customizable benchmark problems for MaOPs. It is able to generate problems that reproduce features present in other benchmarks and also problems with some new features. We propose here the concept of generative benchmarking, in which one can generate an infinite number of MOO problems, by varying parameters that control specific features that the problem should have: scalability in the number of variables and objectives, bias, deceptiveness, multimodality, robust and non-robust solutions, shape of the Pareto front, and constraints. The proposed Generalized Position-Distance (GPD) tunable benchmark generator uses the position-distance paradigm, a basic approach to building test functions, used in other benchmarks such as Deb, Thiele, Laumanns and Zitzler (DTLZ), Walking Fish Group (WFG) and others. It includes scalable problems in any number of variables and objectives and it presents Pareto fronts with different characteristics. The resulting functions are easy to understand and visualize, easy to implement, fast to compute and their Pareto optimal solutions are known.
Anais do 14º Simpósio Brasileiro de Automação Inteligente, 2019
This paper presents a method for the power dispatching problem in a smart park that uses plug-in ... more This paper presents a method for the power dispatching problem in a smart park that uses plug-in electric vehicles as storage units. The objective is to find the best energy storage planning for 10 vehicles over one day (24 hours), in order to minimize the total cost of energy bought from the grid by the smart park. An evolutionary algorithm was applied to solve the problem, considering possible alterations of arrival/departure times of vehicles. At each hour the algorithm was reinitialized and updated values for grid electricity prices, energy needs and solar energy were given by means of a fuzzy time-series model. The results presented an average cost of 21,576(20executions)andaveragetimeof6.45seconds.Theproposedapproachwasabletojoinsucessfullywithtime−seriesmodel,dynamicoptimizationandevolutionaryalgorithmstosolveaproblemofpowerdispatching.Resumo:Esteartigoapresentaumapropostaparaoproblemadedespachodeenergiaemumestacionamentointeligentequeusaveıˊculoseleˊtricoscomounidadesdearmazenamento.Oobjetivoprincipaleˊencontraromelhorplanejamentodearmazenamentodeenergiapara10veıˊculosaolongodeumdia(24horas),afimdeminimizarocustototaldaenergiacompradadaredepeloestacionamento.Umalgoritmoevolucionaˊriofoiaplicadopararesolveroproblema,levandoemconsiderac\caopossıˊveisalterac\coesnoshoraˊriosdeentrada/saıˊdadosveıˊculos.Acadahoraoalgoritmoerareinicializadoevaloresatualizadosparaosprec\cosdaenergia,necessidadedeenergiaeenergiasolareramgeradosporummodelodeseˊriestemporaisfuzzy.Osresultadosapresentaramumcustomeˊdiode21, 576 (20 executions) and average time of 6.45 seconds. The proposed approach was able to join sucessfully with time-series model, dynamic optimization and evolutionary algorithms to solve a problem of power dispatching. Resumo: Este artigo apresenta uma proposta para o problema de despacho de energia em um estacionamento inteligente que usa veículos elétricos como unidades de armazenamento. O objetivo principalé encontrar o melhor planejamento de armazenamento de energia para 10 veículos ao longo de um dia (24 horas), a fim de minimizar o custo total da energia comprada da rede pelo estacionamento. Um algoritmo evolucionário foi aplicado para resolver o problema, levando em consideração possíveis alterações nos horários de entrada/saída dos veículos. A cada hora o algoritmo era reinicializado e valores atualizados para os preços da energia, necessidade de energia e energia solar eram gerados por um modelo de séries temporais fuzzy. Os resultados apresentaram um custo médio de 21,576(20executions)andaveragetimeof6.45seconds.Theproposedapproachwasabletojoinsucessfullywithtime−seriesmodel,dynamicoptimizationandevolutionaryalgorithmstosolveaproblemofpowerdispatching.Resumo:Esteartigoapresentaumapropostaparaoproblemadedespachodeenergiaemumestacionamentointeligentequeusaveıˊculoseleˊtricoscomounidadesdearmazenamento.Oobjetivoprincipaleˊencontraromelhorplanejamentodearmazenamentodeenergiapara10veıˊculosaolongodeumdia(24horas),afimdeminimizarocustototaldaenergiacompradadaredepeloestacionamento.Umalgoritmoevolucionaˊriofoiaplicadopararesolveroproblema,levandoemconsiderac\caopossıˊveisalterac\coesnoshoraˊriosdeentrada/saıˊdadosveıˊculos.Acadahoraoalgoritmoerareinicializadoevaloresatualizadosparaosprec\cosdaenergia,necessidadedeenergiaeenergiasolareramgeradosporummodelodeseˊriestemporaisfuzzy.Osresultadosapresentaramumcustomeˊdiode21.576 (20 execuções) e tempo médio de 6.45 segundos. A abordagem proposta foi capaz de combinar eficazmente modelos de séries temporais, otimização dinâmica e algoritmos evolucionários para resolver um problema de despacho de energia.
Proceedings XIII Brazilian Congress on Computational Inteligence, 2018
Proceedings XIII Brazilian Congress on Computational Inteligence, 2018
Fuzzy Multi-criteria decision making methods have been provided to help the decision-makers in th... more Fuzzy Multi-criteria decision making methods have been provided to help the decision-makers in their complex decisions about future uncertainties. Taking into consideration uncertainties such as vagueness and future scenarios, this paper aims to apply the methods Fuzzy-MultiMoora, Fuzzy-Topsis Linear, Fuzzy-Topsis Vector, Fuzzy-Vikor and Fuzzy-Waspas in a Hydrothermal Dispatch problem. Five scenarios were evaluated by varying hydrology and energy demand parameters, from very pessimistic to very optimistic. Two decision makers made explicit their preferences weighting three criteria: Cost, Rationing and Distance. The normalized fuzzy numbers were calculated using the concept of alpha-cuts. Finally, the indexes were aggregated into a final ordering considering weights for the methods based on the Kendall tau distance. The best solutions were compared in relation to the criteria. It was observed that these solutions presented good results in all scenarios evaluated.