On the use of advanced optimization methods in mechanical design

Detalhes bibliográficos
Autor(a) principal: Oliveira, Miguel Jorge Guimarães de
Data de Publicação: 2018
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10773/26911
Resumo: Advanced optimization methods are widely applied to mechanical design, mainly for its abilities to solve complex problems that traditional optimization techniques such as gradient-based methods do not present. With its increasing popularity, the number of algorithms found in the literature is vast. In this work three algorithms are implemented, namely Particle Swarm Optimization (PSO), Differential Evolution (DE) and Teaching-Learning- Based Optimization (TLBO). Firstly, the application of these algorithms is analyzed for a composition function benchmark and three mechanical design minimization problems (the weight of a speed reducer, the volume of a three-bar truss and the area of a square plate with a cut-out hole). Furthermore, as the scope of available algorithms increases, the choice of programming tools to implement them is also vast, and generally made considering subjective criteria or difficulties in using enhancing strategies such as parallel processing. Thereby an analysis of programming tools applied to metaheuristic algorithms is carried out using four programming languages with distinct characteristics: Python, MATLAB, Java and C++. The selected algorithms and problems are coded using each programming language, which are initially compared in a sequential processing implementation. Additionally, in order to analyze potential gains in performance, parallel processing procedures are implemented using features of each programming language. The application of the algorithms to the mechanical design problems demonstrates good results in the achieved solutions. In what concerns to the computational time, sequential and processing results present considerable differences between programming languages while the implementation of parallel processing procedures demonstrates significant benefits for complex problems.
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spelling On the use of advanced optimization methods in mechanical designAdvanced optimization methodsMechanical designProgramming languagesParallel processingAdvanced optimization methods are widely applied to mechanical design, mainly for its abilities to solve complex problems that traditional optimization techniques such as gradient-based methods do not present. With its increasing popularity, the number of algorithms found in the literature is vast. In this work three algorithms are implemented, namely Particle Swarm Optimization (PSO), Differential Evolution (DE) and Teaching-Learning- Based Optimization (TLBO). Firstly, the application of these algorithms is analyzed for a composition function benchmark and three mechanical design minimization problems (the weight of a speed reducer, the volume of a three-bar truss and the area of a square plate with a cut-out hole). Furthermore, as the scope of available algorithms increases, the choice of programming tools to implement them is also vast, and generally made considering subjective criteria or difficulties in using enhancing strategies such as parallel processing. Thereby an analysis of programming tools applied to metaheuristic algorithms is carried out using four programming languages with distinct characteristics: Python, MATLAB, Java and C++. The selected algorithms and problems are coded using each programming language, which are initially compared in a sequential processing implementation. Additionally, in order to analyze potential gains in performance, parallel processing procedures are implemented using features of each programming language. The application of the algorithms to the mechanical design problems demonstrates good results in the achieved solutions. In what concerns to the computational time, sequential and processing results present considerable differences between programming languages while the implementation of parallel processing procedures demonstrates significant benefits for complex problems.Métodos avançados de otimização têm sido amplamente aplicados ao projeto mecânico, principalmente pela sua capacidade de resolver problemas complexos que técnicas tradicionais de otimização como os métodos baseados em gradiente não apresentam. Devido à sua crescente popularidade, o número de algoritmos encontrados na literatura é vasto. Neste trabalho são implementados três algoritmos distintos, Otimização por Bando de Partículas (PSO), Evolução Diferencial (DE) e Otimização Baseada no Ensino-Aprendizagem (TLBO). Inicialmente, a aplicação destes algoritmos é analisada numa função composta e em três problemas de minimização de projeto mecânico (o peso de um redutor de velocidade, o volume de uma estrutura de três barras e a área de uma placa quadrada com um furo circular). Além disso, com o aumento do número de algoritmos existentes, a escolha de ferramentas de programação para implementá-los também é vasta e geralmente feita considerando critérios subjetivos ou dificuldades no uso de estratégias de melhoria como processamento paralelo. Deste modo, no presente trabalho é realizada uma análise de ferramentas de programação aplicadas a algoritmos metaheurísticos, utilizando linguagens de programação com distintas características: Python, MATLAB, Java e C++. Os algoritmos e problemas selecionados são programados em cada linguagem de programação, e inicialmente comparados numa implementação de processamento sequencial. Além disso, de forma a analisar possíveis ganhos de desempenho, são implementados procedimentos de processamento paralelo utilizando recursos de cada linguagem de programação. A aplicação dos algoritmos aos problemas de projeto mecânico demonstra bons resultados nas soluções obtidas. Os resultados, em termos de tempo computacional, de processamento sequencial e paralelo, apresentam diferenças consideráveis entre as linguagens de programação. A implementação de procedimentos de processamento paralelo demonstra benefícios significativos em problemas complexos.2019-11-05T11:30:58Z2018-11-28T00:00:00Z2018-11-28info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10773/26911TID:202237958engOliveira, Miguel Jorge Guimarães deinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T11:52:09Zoai:ria.ua.pt:10773/26911Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:59:49.148536Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv On the use of advanced optimization methods in mechanical design
title On the use of advanced optimization methods in mechanical design
spellingShingle On the use of advanced optimization methods in mechanical design
Oliveira, Miguel Jorge Guimarães de
Advanced optimization methods
Mechanical design
Programming languages
Parallel processing
title_short On the use of advanced optimization methods in mechanical design
title_full On the use of advanced optimization methods in mechanical design
title_fullStr On the use of advanced optimization methods in mechanical design
title_full_unstemmed On the use of advanced optimization methods in mechanical design
title_sort On the use of advanced optimization methods in mechanical design
author Oliveira, Miguel Jorge Guimarães de
author_facet Oliveira, Miguel Jorge Guimarães de
author_role author
dc.contributor.author.fl_str_mv Oliveira, Miguel Jorge Guimarães de
dc.subject.por.fl_str_mv Advanced optimization methods
Mechanical design
Programming languages
Parallel processing
topic Advanced optimization methods
Mechanical design
Programming languages
Parallel processing
description Advanced optimization methods are widely applied to mechanical design, mainly for its abilities to solve complex problems that traditional optimization techniques such as gradient-based methods do not present. With its increasing popularity, the number of algorithms found in the literature is vast. In this work three algorithms are implemented, namely Particle Swarm Optimization (PSO), Differential Evolution (DE) and Teaching-Learning- Based Optimization (TLBO). Firstly, the application of these algorithms is analyzed for a composition function benchmark and three mechanical design minimization problems (the weight of a speed reducer, the volume of a three-bar truss and the area of a square plate with a cut-out hole). Furthermore, as the scope of available algorithms increases, the choice of programming tools to implement them is also vast, and generally made considering subjective criteria or difficulties in using enhancing strategies such as parallel processing. Thereby an analysis of programming tools applied to metaheuristic algorithms is carried out using four programming languages with distinct characteristics: Python, MATLAB, Java and C++. The selected algorithms and problems are coded using each programming language, which are initially compared in a sequential processing implementation. Additionally, in order to analyze potential gains in performance, parallel processing procedures are implemented using features of each programming language. The application of the algorithms to the mechanical design problems demonstrates good results in the achieved solutions. In what concerns to the computational time, sequential and processing results present considerable differences between programming languages while the implementation of parallel processing procedures demonstrates significant benefits for complex problems.
publishDate 2018
dc.date.none.fl_str_mv 2018-11-28T00:00:00Z
2018-11-28
2019-11-05T11:30:58Z
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TID:202237958
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