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Reliability-based multi-objective optimization of trusses with greylag goose algorithm.
Reliability-based multi-objective optimization of trusses with greylag goose algorithm.
- Source :
- Evolutionary Intelligence; Feb2025, Vol. 18 Issue 1, p1-33, 33p
- Publication Year :
- 2025
-
Abstract
- This paper introduces a multi-objective variant of the Greylag Goose Optimizer (MOGGO) to tackle complex structural optimization problems. Inspired by the cooperative behavior of geese in flight, MOGGO employs dynamic grouping to enhance problem-solving efficiency. Six truss structures undergo simultaneous topology, shape, and size optimization using MOGGO, aiming to maximize reliability while minimizing structural mass. By incorporating non-dominance sorting and archiving techniques, MOGGO extends the single-objective Greylag Goose Optimizer to effectively address trade-offs between competing objectives. Evaluation metrics and statistical tests demonstrate MOGGO's superior performance in handling large structural optimization problems, preserving more Pareto-optimal sets, and achieving greater convergence and variance in objective and decision spaces. MOGGO’s ability to manage conflicting objectives is further validated through diversity analysis, with swarm plots illustrating its superior convergence behavior across iterations. Overall, MOGGO proves to be an efficient and effective approach for addressing challenging reliability-based multi-objective structural optimization problems. Query ID="Q1" Text="Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 1 Given name: [specify authors given name] Last name [specify authors last name]. Also, kindly confirm the details in the metadata are correct." [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 18645909
- Volume :
- 18
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Evolutionary Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 182204274
- Full Text :
- https://doi.org/10.1007/s12065-024-01011-9