Uncertainty-Based Design Optimization Framework Based on Improved Chicken Swarm Algorithm and Bayesian Optimization Neural Network

As the complexity and functional integration of mechanism systems continue to increase in modern practical engineering, the challenges of changing environmental conditions and extreme working conditions are becoming increasingly severe. Traditional uncertainty-based design optimization (UBDO) has ex...

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Vydáno v:Applied sciences Ročník 15; číslo 17; s. 9671
Hlavní autoři: Ji, Qiang, Li, Ran, Jing, Shi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.09.2025
Témata:
Accuracy
Algorithms
Analysis
Bayesian optimization
Behavior
BP neural network
Case studies
Collaboration
Design optimization
Efficiency
Engineering
engineering structure
Environmental engineering
improved chicken swarm optimization algorithm
Mathematical optimization
Methods
Neural networks
Optimization algorithms
uncertainty-based design optimization
ISSN:2076-3417, 2076-3417
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Shrnutí:As the complexity and functional integration of mechanism systems continue to increase in modern practical engineering, the challenges of changing environmental conditions and extreme working conditions are becoming increasingly severe. Traditional uncertainty-based design optimization (UBDO) has exposed problems of low efficiency and slow convergence when dealing with nonlinear, high-dimensional, and strongly coupled problems. In response to these issues, this paper proposes an UBDO framework that integrates an efficient intelligent optimization algorithm with an excellent surrogate model. By fusing butterfly search with Levy flight optimization, an improved chicken swarm algorithm is introduced, aiming to address the imbalance between global exploitation and local exploration capabilities in the original algorithm. Additionally, Bayesian optimization is employed to fit the limit-state evaluation function using a BP neural network, with the objective of reducing the high computational costs associated with uncertainty analysis through repeated limit-state evaluations in uncertainty-based optimization. Finally, a decoupled optimization framework is adopted to integrate uncertainty analysis with design optimization, enhancing global optimization capabilities under uncertainty and addressing challenges associated with results that lack sufficient accuracy or reliability to meet design requirements. Based on the results from engineering case studies, the proposed UBDO framework demonstrates notable effectiveness and superiority.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app15179671