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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| Published in: | Applied sciences Vol. 15; no. 17; p. 9671 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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01.09.2025
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| ISSN: | 2076-3417, 2076-3417 |
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| Abstract | 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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| AbstractList | 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. |
| Audience | Academic |
| Author | Ji, Qiang Li, Ran Jing, Shi |
| Author_xml | – sequence: 1 givenname: Qiang orcidid: 0009-0008-5800-916X surname: Ji fullname: Ji, Qiang – sequence: 2 givenname: Ran orcidid: 0009-0006-3059-6759 surname: Li fullname: Li, Ran – sequence: 3 givenname: Shi orcidid: 0009-0003-3252-7202 surname: Jing fullname: Jing, Shi |
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| SubjectTerms | 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 |
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| Title | Uncertainty-Based Design Optimization Framework Based on Improved Chicken Swarm Algorithm and Bayesian Optimization Neural Network |
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| Volume | 15 |
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