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Fuzzy adaptive jellyfish search-optimized stacking machine learning for engineering planning and design
Abstract This paper presents a novel fuzzy adaptive jellyfish search-optimized stacking system (FAJS-SS) that integrates the jellyfish search (JS) optimizer, the fuzzy adaptive (FA) logic controller, and stacking ensemble machine learning. First, FA logic is incorporated into JS optimizer to construct an efficient metaheuristic algorithm for global optimization. The proposed algorithm is benchmarked against various well-known optimizers using mathematical functions. The FAJS optimizer is then used to optimize the hyperparameters of the stacking system (SS). Cases that involve construction productivity, the compressive strength of a masonry structure, the shear capacity of reinforced deep beams, the axial strength of steel tube-confined concrete, and the resilient modulus of subgrade soils were investigated. Results of analyses reveal that the FAJS-SS predicts more accurately than the other machine learning systems in the literature. Accordingly, the proposed fuzzy adaptive metaheuristic-optimized stacking system is effective for providing engineering informatics in the planning and design phase.
Highlights A novel fuzzy adaptive jellyfish search-optimized stacking system (FAJS-SS) is developed. FA logic controller is incorporated into JS to construct an enhanced metaheuristic optimization algorithm. The FAJS optimizer is benchmarked against various algorithm using mathematical functions. A stacking ensemble scheme consisting of base machine learners is proposed and finetuned by FAJS. The FAJS-SS is validated in solving engineering planning and design problems.
Fuzzy adaptive jellyfish search-optimized stacking machine learning for engineering planning and design
Abstract This paper presents a novel fuzzy adaptive jellyfish search-optimized stacking system (FAJS-SS) that integrates the jellyfish search (JS) optimizer, the fuzzy adaptive (FA) logic controller, and stacking ensemble machine learning. First, FA logic is incorporated into JS optimizer to construct an efficient metaheuristic algorithm for global optimization. The proposed algorithm is benchmarked against various well-known optimizers using mathematical functions. The FAJS optimizer is then used to optimize the hyperparameters of the stacking system (SS). Cases that involve construction productivity, the compressive strength of a masonry structure, the shear capacity of reinforced deep beams, the axial strength of steel tube-confined concrete, and the resilient modulus of subgrade soils were investigated. Results of analyses reveal that the FAJS-SS predicts more accurately than the other machine learning systems in the literature. Accordingly, the proposed fuzzy adaptive metaheuristic-optimized stacking system is effective for providing engineering informatics in the planning and design phase.
Highlights A novel fuzzy adaptive jellyfish search-optimized stacking system (FAJS-SS) is developed. FA logic controller is incorporated into JS to construct an enhanced metaheuristic optimization algorithm. The FAJS optimizer is benchmarked against various algorithm using mathematical functions. A stacking ensemble scheme consisting of base machine learners is proposed and finetuned by FAJS. The FAJS-SS is validated in solving engineering planning and design problems.
Fuzzy adaptive jellyfish search-optimized stacking machine learning for engineering planning and design
Truong, Dinh-Nhat (author) / Chou, Jui-Sheng (author)
2022-09-08
Article (Journal)
Electronic Resource
English
Civil engineering informatics , Planning and design , Enhanced metaheuristic algorithm , Jellyfish search optimizer , Fuzzy adaptive logic controller , Machine learning , Stacking ensemble , ABC , Artificial bee colony , AI , Artificial intelligence , ANFIS , Adaptive neural fuzzy inference system , ANN , Artificial neural network , CA , Cultural algorithm , CEC , IEEE congress on evolutionary computation , CFDST , Concrete-filled double-skin steel tube , CFRP , Carbon fiber-reinforced polymer , CFSST , Concrete-filled stainless steel tube , CS , Compressive strength of hollow concrete masonry prisms , DE , Differential evolution , FAJS-SS , Fuzzy adaptive jellyfish search-optimized stacking system , FATCM , Fuzzy adaptive time control mechanism , FE , Finite element , FA , Fuzzy adaptive , FFA , Firefly algorithm , FL , Fuzzy logic , GA , Genetic algorithm , GSA , Gravitational Search Algorithm , HS , Harmony search , I&D , Intensification and diversification , JS , Jellyfish search , JSR , Jellyfish swarm rate , LSSVR , Least squares support vector regression , LTPP , Long-term pavement performance , MAE , Mean absolute error , MAPE , Mean absolute percentage error , MARS , Multivariate adaptive regression spline , MFs , Membership functions , ML , MLPN , Multi-layer perceptrons network , OCR , Ocean current rate , OMC , Optimum moisture content , PSO , Particle swarm optimization , R , Correlation coefficient , RBFNN , Radial basis function neural network , RCFST , Rectangular concrete-filled steel tube , RMSE , Root mean square error , SA , Simulated annealing algorithm , SI , Synthesis index , SOS , Symbiotic organisms search , STCC , Steel tube-confined concrete , SVR-GWO , Support vector regression - grey wolf optimization algorithm , TLBO , Teaching-learning-based optimization