Post-Processing of non gradient-based Topology Optimization with Simulated Annealing

Najafabadi, Hossein R.; Goto, Tiago G.; Falheiro, Mizael; Martins, Thiago C.; Barari, Ahmad; Tsuzuki, Marcos de Sales Guerra

doi:10.1016/j.ifacol.2021.08.184

Cited by 10 publications

(5 citation statements)

References 26 publications

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“…Due to the stochastic nature of the SA method, there may be regions with gray areas in the results. These areas may require further post-processing and refinement to be suitable for manufacturing purposes [37]. Post-processing techniques can be employed to refine the obtained designs, ensuring that they meet specific manufacturing constraints and requirements.…”

Section: Resultsmentioning

confidence: 99%

Structural Design with Self-Weight and Inertial Loading Using Simulated Annealing for Non-Gradient Topology Optimization

Rostami Najafabadi,

Martins,

Tsuzuki

et al. 2023

Machines

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This paper explores implementation of self-weight and inertial loading in topology optimization (TO) employing the Simulated Annealing (SA) algorithm as a non-gradient-based technique. This method can be applied to find optimum design of structures with no need for gradient information. To enhance the convergence of the SA algorithm, a novel approach incorporating the crystallization factor is introduced. The method is applied in a benchmark problem of a cantilever beam. The study systematically examines multiple scenarios, including cases with and without self-weight effects, as well as varying point loads. Compliance values are calculated and compared to those reported in existing literature to validate the accuracy of the optimization results. The findings highlight the versatility and effectiveness of the SA-based TO methodology in addressing complex design challenges with considerable self-weight or inertial effect. This work can contribute to structural design of systems where only the objective value is available with no gradient information to use sensitivity-based algorithms.

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Section: Resultsmentioning

confidence: 99%

Structural Design with Self-Weight and Inertial Loading Using Simulated Annealing for Non-Gradient Topology Optimization

Rostami Najafabadi,

Martins,

Tsuzuki

et al. 2023

Machines

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“…Unlike gradient methods, evolutionary optimization does not heavily rely on mathematics, and the initial starting point does not have nearly as much impact. Because of the random nature of evolutionary optimization, it is mostly less e cient than gradient-based optimization since it does not even guarantee an optimal solution [5,6]. However, the method is more aggressive and considers more solutions than gradient methods do, allowing it to nd multiple local minima points, which give it some advantages.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of the Operation Factors of Three PSO-GA-ED Meta-Heuristic Search Methods for Solving a Single-Objective Optimization Problem

Fozooni

Kamari

Pourtalebiyan

et al. 2022

Computational Intelligence and Neuroscience

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In this study, we evaluate several nongradient (evolutionary) search strategies for minimizing mathematical function expressions. We developed and tested the genetic algorithms, particle swarm optimization, and differential evolution in order to assess their general efficacy in optimization of mathematical equations. A comparison is then made between the results and the efficiency, which is determined by the number of iterations, the observed accuracy, and the overall run time. Additionally, the optimization employs 12 functions from Easom, Holder table, Michalewicz, Ackley, Rastrigin, Rosen, Rosen Brock, Shubert, Sphere, Schaffer, Himmelblau’s, and Spring Force Vanderplaats. Furthermore, the crossover rate, mutation rate, and scaling factor are evaluated to determine the effectiveness of the following algorithms. According to the results of the comparison of optimization algorithms, the DE algorithm has the lowest time complexity of the others. Furthermore, GA demonstrated the greatest degree of temporal complexity. As a result, using the PSO method produces different results when repeating the same algorithm with low reliability in terms of locating the optimal location.

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“…Neste trabalho aplicamos o filtro de densidade baseado na distância dada em Sigmund (2007), o qual suaviza bordas, aumenta as linhas finas e reduz gradiente de cinza e tabuleiro de xadrez. E aplicamos uma transformação da densidade intermediária para 0 ou 1, conforme utilizado em Najafabadi et al (2021). A aplicação de filtro de densidade como pós-processamento, melhora com a remoção de massas isoladas no domínio ou em alguns casos reforçando algumas linhas finas no domínio.…”

Section: Resultados Do Ot Multiobjetivo Aplicado a Vigasunclassified

“…Este processo de arredondamento do valor da densidade acelera o processo de convergência. Além disso, esse processo reduz o gradiente de cinza ao comparar com o resultado de Goto et al (2021a) sem a necessidade do pós-processamento utilizado em Najafabadi et al (2021). A dinâmica de gerar uma nova solução do algoritmo funciona da seguinte forma:…”

Section: Ot Com Sa E Heurística De Cristalizaçãounclassified

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Agradeço ao Professor Marcos de Sales Guerra Tsuzuki pelas orientações durante todo o desenvolvimento deste trabalho. Agradeço aos professores Thiago de Castro Martins e Ahmad Barari pelo apoio e contribuições. Aos Professores Larissa Driemeier e Rafael T. Moura pelas contribuições. Agradeço aos Colegas Hossein e Edson Ueda pela ajuda e contribuições. Agradeço aos meus colegas do Laboratório de Geometria Computacional, que me auxiliaram nesta jornada. Finalmente, agradeço à minha esposa, Pamela Jordana de Menezes pelo companheirismo e paciência e à minha família pelo apoio.

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Post-Processing of non gradient-based Topology Optimization with Simulated Annealing

Cited by 10 publications

References 26 publications

Structural Design with Self-Weight and Inertial Loading Using Simulated Annealing for Non-Gradient Topology Optimization

Structural Design with Self-Weight and Inertial Loading Using Simulated Annealing for Non-Gradient Topology Optimization

An Analysis of the Operation Factors of Three PSO-GA-ED Meta-Heuristic Search Methods for Solving a Single-Objective Optimization Problem

Propostas de heurísticas e estratégias de feedback aplicadas ao recozimento simulado.

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