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OPTIMIZATION OF FRAME STRUCTURE USING GRG AND XBOOST ALGORITHM
Abstract:
This study presents an innovative approach for optimizing frame structures through the integration of Generalized Reduced Gradient (GRG) and XGBoost algorithms. Frame structures play a pivotal role in various engineering applications, and their efficient design is crucial for ensuring structural integrity and performance. Traditional optimization methods often encounter challenges in handling complex design spaces and achieving optimal solutions within reasonable computational timeframes. To address these limitations, we propose a hybrid optimization framework that leverages the strengths of both GRG and XGBoost algorithms.
The GRG algorithm is employed for its ability to handle nonlinear optimization problems efficiently, particularly in scenarios where derivative information is not readily available or expensive to compute. By utilizing GRG, the framework can effectively navigate complex design spaces and converge to high-quality solutions. Additionally, the XGBoost algorithm, renowned for its robustness and scalability in machine learning tasks, is integrated to enhance the optimization process further. XGBoost provides the capability to learn complex patterns from data and make informed decisions, thereby improving the overall efficiency and effectiveness of the optimization process.
Through a series of numerical experiments and case studies, the proposed framework is evaluated in optimizing frame structures subjected to various design constraints and objectives. Performance metrics such as structural weight, stiffness, and stress distribution are considered to assess the effectiveness of the optimization process. The results demonstrate the superior performance of the hybrid optimization framework compared to traditional methods, showcasing its capability to efficiently explore the design space and identify optimal solutions.
Overall, this research contributes to advancing the state-of-the-art in structural optimization by introducing a novel approach that combines the strengths of GRG and XGBoost algorithms. The proposed framework holds significant promise for optimizing frame structures across diverse engineering domains, offering engineers and designers a powerful tool for achieving superior structural performance while minimizing resource utilization and design time.
TABLE OF CONTENT
Introduction
1.1 Background
1.2 Objectives
1.3 Scope of the Study
1.4 Significance of Optimization in Frame Structures
Literature Review
2.1 Overview of Structural Optimization
2.2 Existing Optimization Techniques in Structural Engineering
2.3 Applications of Genetic Algorithms in Structural Optimization
2.4 XGBoost Algorithm in Structural Optimization
2.5 Integration of GRG and XGBoost in Structural Optimization
Methodology
3.1 Description of the Frame Structure
3.2 Mathematical Formulation of the Optimization Problem
3.3 Genetic Algorithm (GRG) Implementation
3.3.1 Selection
3.3.2 Crossover
3.4 XGBoost Algorithm Implementation
3.4.1 Ensemble Learning
3.4.2 Hyperparameter Tuning
3.5 Integration of GRG and XGBoost
Data Collection and Preprocessing
4.1 Structural Data Collection
4.2 Data Preprocessing for GRG
4.3 Data Preprocessing for XGBoost
Conclusion
5.1 Summary of Findings
5.2 Implications for Structural Engineering
5.3 Concluding Remarks
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