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Scientific approach to problem solving-inspired optimization of stacking ensemble learning for enhanced civil engineering informatics

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Publicado en:The Artificial Intelligence Review vol. 58, no. 12 (Dec 2025), p. 404
Autor principal: Truong, Dinh-Nhat
Otros Autores: Chou, Jui-Sheng
Publicado:
Springer Nature B.V.
Materias:
Problem solving
Behavior
Particle swarm optimization
Experiments
Reliability
Algorithms
Quantitative analysis
Prediction models
Models
Machine learning
Chemistry
Engineering
Function words
Cognition & reasoning
Statistical analysis
Infrastructure
Evolutionary computation
Civil engineering
Social networks
Teaching
Genetic algorithms
Neural networks
Computation
Case studies
Primates
Optimization
Learning
Cultural change
Ensemble learning
Optimization algorithms
Data collection
Swarm intelligence
Genetics
Heuristic methods
Benchmarks
Evolutionary algorithms
Physics
Radial basis function
Support vector machines
Whales & whaling
Informatics
Acceso en línea:Citation/Abstract
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Resumen:This study introduces the Scientific Approach to Problem Solving-inspired Optimization (SAPSO) algorithm, a novel metaheuristic specifically designed for applications in civil engineering informatics. SAPSO imitates the structured process of scientific inquiry—covering problem review, hypothesis formulation, data collection, and analysis—to systematically explore complex search spaces. This approach enables SAPSO to reliably identify global optima. The algorithm’s performance was extensively tested against eleven leading metaheuristic algorithms using the IEEE Congress on Evolutionary Computation benchmark suites from 2020 (CEC 2020) and 2022 (CEC 2022). The comparison included the Artificial Bee Colony, Cultural Algorithm, Genetic Algorithm, Differential Evolution, Artificial Gorilla Troops Optimizer, Grey Wolf Optimizer, Particle Swarm Optimization, Red Kite Optimization Algorithm, Symbiotic Organisms Search, Teaching–Learning-Based Optimization, and Whale Optimization Algorithm. Statistical analysis with the Wilcoxon rank-sum test confirmed SAPSO’s superior results across these benchmarks. Additionally, this study presents a stacked ensemble machine learning framework called the SAPSO-Weighted Features Stacking System (SAPSO-WFSS), which combines SAPSO with two predictive models: a Radial Basis Function Neural Network and Least Squares Support Vector Regression. SAPSO is used to optimize both feature weights and model hyperparameters. Experiments on five diverse civil engineering case studies show that SAPSO-WFSS provides high accuracy, with Mean Absolute Percentage Error values as low as 2.4%, outperforming traditional methods. These findings demonstrate SAPSO’s potential as a powerful tool for improving prediction reliability in infrastructure maintenance and solving complex optimization problems in civil engineering.
ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-025-11356-x
Fuente:ABI/INFORM Global