Performance assessment of meta-heuristics for composite layup optimisation

Jalili, Shahin; Khani, Reza; Maheri, Alireza; Hosseinzadeh, Yousef

doi:10.1007/s00521-021-06519-0

Cited by 3 publications

(1 citation statement)

References 65 publications

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“…Previous studies of the reinforced concrete structures optimization with and without prestressing have demonstrated the applicability of metaheuristic optimization techniques like the harmony search (HS) methodology to reduce the structural cost and environmental impact [2][3][4][5][6]. Metaheuristic optimization techniques have been applied to a variety of problems in civil and structural engineering including the dimensioning of trusses [7][8][9][10][11][12][13], retaining walls [14][15][16][17][18][19][20][21][22], laminated composite plates [23][24][25][26][27][28][29][30], and steel plate girders [31]. On the other hand, post-tensioning in structures is an area where metaheuristic optimization techniques found relatively few applications.…”

Section: Introductionmentioning

confidence: 99%

Optimal Dimensions of Post-Tensioned Concrete Cylindrical Walls Using Harmony Search and Ensemble Learning with SHAP

et al. 2023

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The optimal design of prestressed concrete cylindrical walls is greatly beneficial for economic and environmental impact. However, the lack of the available big enough datasets for the training of robust machine learning models is one of the factors that prevents wide adoption of machine learning techniques in structural design. The current study demonstrates the application of the well-established harmony search methodology to create a large database of optimal design configurations. The unit costs of concrete and steel used in the construction, the specific weight of the stored fluid, and the height of the cylindrical wall are the input variables whereas the optimum thicknesses of the wall with and without post-tensioning are the output variables. Based on this database, some of the most efficient ensemble learning techniques like the Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Gradient Boosting (CatBoost) and Random Forest algorithms have been trained. An R2 score greater than 0.98 could be achieved by all of the ensemble learning models. Furthermore, the impacts of different input features on the predictions of different machine learning models have been analyzed using the SHapley Additive exPlanations (SHAP) methodology. The height of the cylindrical wall was found to have the greatest impact on the optimal wall thickness, followed by the specific weight of the stored fluid. Also, with the help of individual conditional expectation (ICE) plots the variations of predictive model outputs with respect to each input feature have been visualized. By using the genetic programming methodology, predictive equations have been obtained for the optimal wall thickness.

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Section: Introductionmentioning

confidence: 99%

Optimal Dimensions of Post-Tensioned Concrete Cylindrical Walls Using Harmony Search and Ensemble Learning with SHAP

et al. 2023

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Applications of Cultural Algorithms in Engineering

Jalili

2022

Engineering Optimization: Methods and Applications

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Framework of Meta-Heuristic Variable Length Searching for Feature Selection in High-Dimensional Data

2022

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Feature Selection in High Dimensional Space is a combinatory optimization problem with an NP-hard nature. Meta-heuristic searching with embedding information theory-based criteria in the fitness function for selecting the relevant features is used widely in current feature selection algorithms. However, the increase in the dimension of the solution space leads to a high computational cost and risk of convergence. In addition, sub-optimality might occur due to the assumption of a certain length of the optimal number of features. Alternatively, variable length searching enables searching within the variable length of the solution space, which leads to more optimality and less computational load. The literature contains various meta-heuristic algorithms with variable length searching. All of them enable searching in high dimensional problems. However, an uncertainty in their performance exists. In order to fill this gap, this article proposes a novel framework for comparing various variants of variable length-searching meta-heuristic algorithms in the application of feature selection. For this purpose, we implemented four types of variable length meta-heuristic searching algorithms, namely VLBHO-Fitness, VLBHO-Position, variable length particle swarm optimization (VLPSO) and genetic variable length (GAVL), and we compared them in terms of classification metrics. The evaluation showed the overall superiority of VLBHO over the other algorithms in terms of accomplishing lower fitness values when optimizing mathematical functions of the variable length type.

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Performance assessment of meta-heuristics for composite layup optimisation

Cited by 3 publications

References 65 publications

Optimal Dimensions of Post-Tensioned Concrete Cylindrical Walls Using Harmony Search and Ensemble Learning with SHAP

Optimal Dimensions of Post-Tensioned Concrete Cylindrical Walls Using Harmony Search and Ensemble Learning with SHAP

Applications of Cultural Algorithms in Engineering

Framework of Meta-Heuristic Variable Length Searching for Feature Selection in High-Dimensional Data

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