Structural Optimization Using Simulated Annealing

Sonmez, Fazıl O.

doi:10.5772/5567

Cited by 5 publications

(3 citation statements)

References 44 publications

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“…This algorithm belongs to the same evolutionary optimization class as genetic algorithm, which is a global stochastic optimization-seeking algorithm with selforganizing, self-adaptive, and self-learning properties. 37 The SA algorithm is derived from the principle of physical annealing of solids. When a crystalline solid is heated and smelted, the thermal motion of its internal particles intensifies, it becomes disordered and its internal energy increases.…”

Section: Simulated Annealing (Sa) Algorithmmentioning

confidence: 99%

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Research on a creep constitutive model of compacted graphite cast iron and its parameter identification method

Jing,

Ma,

et al. 2024

Fatigue Fract Eng Mat Struct

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In order to accurately predict the minimum creep rate under the actual working conditions of compacted graphite cast iron cylinder head, this paper conducted creep experiments in the temperature range of 723.15–823.15 K and the stress range of 100–300 MPa. The creep damage evolution and damage mechanisms under test conditions were also analyzed. Then the data fitting performances were analyzed and compared from three aspects: the optimization‐seeking algorithm, the form of the objective function and the construction of the creep constitutive model. Finally, the chosen optimization algorithm combined with the proposed objective function and creep constitutive model resulted in 100% of the predicted values within the 3 times error band and 26.67% of the predicted values within the 1.5 times error band. This improves the prediction accuracy while reducing the effect of data overfitting.

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Section: Simulated Annealing (Sa) Algorithmmentioning

confidence: 99%

“…This algorithm belongs to the same evolutionary optimization class as genetic algorithm, which is a global stochastic optimization‐seeking algorithm with self‐organizing, self‐adaptive, and self‐learning properties 37 . The SA algorithm is derived from the principle of physical annealing of solids.…”

Section: Multi‐objective Optimization Algorithmmentioning

confidence: 99%

Research on a creep constitutive model of compacted graphite cast iron and its parameter identification method

Jing,

Ma,

et al. 2024

Fatigue Fract Eng Mat Struct

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“…The simulated annealing algorithm is already used in the TO of structures with different objective functions and constraints [34,35]. To the best knowledge of the authors, the impact of self-weight and inertial forces as variable loads has not been considered in TO with simulated annealing.…”

Section: Introductionmentioning

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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Enhancing Architectural Plan Generation with Machine Learning and Space Syntax Analysis for Optimized Spatial Configuration

Sabsabi,

Hatipoglu

2024

Advances in Science, Technology &Amp; Innovation

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Structural Optimization Using Simulated Annealing

Cited by 5 publications

References 44 publications

Research on a creep constitutive model of compacted graphite cast iron and its parameter identification method

Research on a creep constitutive model of compacted graphite cast iron and its parameter identification method

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

Enhancing Architectural Plan Generation with Machine Learning and Space Syntax Analysis for Optimized Spatial Configuration

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