Nonpenalty Machine Learning Constraint Handling Using PSO-SVM for Structural Optimization
Firstly formulated to solve unconstrained optimization problems, the common way to solve constrained ones with the metaheuristic particle swarm optimization algorithm (PSO) is represented by adopting some penalty functions. In this paper, a new nonpenalty-based constraint handling approach for PSO i...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/6617750 |
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| author | Marco M. Rosso Raffaele Cucuzza Fabio Di Trapani Giuseppe C. Marano |
| author_facet | Marco M. Rosso Raffaele Cucuzza Fabio Di Trapani Giuseppe C. Marano |
| author_sort | Marco M. Rosso |
| collection | DOAJ |
| description | Firstly formulated to solve unconstrained optimization problems, the common way to solve constrained ones with the metaheuristic particle swarm optimization algorithm (PSO) is represented by adopting some penalty functions. In this paper, a new nonpenalty-based constraint handling approach for PSO is implemented, adopting a supervised classification machine learning method, the support vector machine (SVM). Because of its generality, constraint handling with SVM appears more adaptive both to nonlinear and discontinuous boundary. To preserve the feasibility of the population, a simple bisection algorithm is also implemented. To improve the search performances of the algorithm, a relaxation function of the constraints is also adopted. In the end part of this paper, two numerical literature benchmark examples and two structural examples are discussed. The first structural example refers to a homogeneous constant cross-section simply supported beam. The second one refers to the optimization of a plane simply supported Warren truss beam. The obtained results in terms of objective function demonstrate that this new approach represents a valid alternative to solve constrained optimization problems even in structural optimization field. Furthermore, as demonstrated by the Warren truss beam problem, this new algorithm provides an optimal structural design which represents also a good solution from the technical point of view with a trivial rounding-off that does not jeopardize significantly the optimization design process. |
| format | Article |
| id | doaj-art-13fb6fd3808e48ada84cb1ba1e2da8ac |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-13fb6fd3808e48ada84cb1ba1e2da8ac2025-02-03T06:06:34ZengWileyAdvances in Civil Engineering1687-80861687-80942021-01-01202110.1155/2021/66177506617750Nonpenalty Machine Learning Constraint Handling Using PSO-SVM for Structural OptimizationMarco M. Rosso0Raffaele Cucuzza1Fabio Di Trapani2Giuseppe C. Marano3Politecnico di Torino, DISEG, Dipartimento di Ingegneria Strutturale, Edile e Geotecnica, Corso Duca Degli Abruzzi, 24, Turin 10128, ItalyPolitecnico di Torino, DISEG, Dipartimento di Ingegneria Strutturale, Edile e Geotecnica, Corso Duca Degli Abruzzi, 24, Turin 10128, ItalyPolitecnico di Torino, DISEG, Dipartimento di Ingegneria Strutturale, Edile e Geotecnica, Corso Duca Degli Abruzzi, 24, Turin 10128, ItalyPolitecnico di Torino, DISEG, Dipartimento di Ingegneria Strutturale, Edile e Geotecnica, Corso Duca Degli Abruzzi, 24, Turin 10128, ItalyFirstly formulated to solve unconstrained optimization problems, the common way to solve constrained ones with the metaheuristic particle swarm optimization algorithm (PSO) is represented by adopting some penalty functions. In this paper, a new nonpenalty-based constraint handling approach for PSO is implemented, adopting a supervised classification machine learning method, the support vector machine (SVM). Because of its generality, constraint handling with SVM appears more adaptive both to nonlinear and discontinuous boundary. To preserve the feasibility of the population, a simple bisection algorithm is also implemented. To improve the search performances of the algorithm, a relaxation function of the constraints is also adopted. In the end part of this paper, two numerical literature benchmark examples and two structural examples are discussed. The first structural example refers to a homogeneous constant cross-section simply supported beam. The second one refers to the optimization of a plane simply supported Warren truss beam. The obtained results in terms of objective function demonstrate that this new approach represents a valid alternative to solve constrained optimization problems even in structural optimization field. Furthermore, as demonstrated by the Warren truss beam problem, this new algorithm provides an optimal structural design which represents also a good solution from the technical point of view with a trivial rounding-off that does not jeopardize significantly the optimization design process.http://dx.doi.org/10.1155/2021/6617750 |
| spellingShingle | Marco M. Rosso Raffaele Cucuzza Fabio Di Trapani Giuseppe C. Marano Nonpenalty Machine Learning Constraint Handling Using PSO-SVM for Structural Optimization Advances in Civil Engineering |
| title | Nonpenalty Machine Learning Constraint Handling Using PSO-SVM for Structural Optimization |
| title_full | Nonpenalty Machine Learning Constraint Handling Using PSO-SVM for Structural Optimization |
| title_fullStr | Nonpenalty Machine Learning Constraint Handling Using PSO-SVM for Structural Optimization |
| title_full_unstemmed | Nonpenalty Machine Learning Constraint Handling Using PSO-SVM for Structural Optimization |
| title_short | Nonpenalty Machine Learning Constraint Handling Using PSO-SVM for Structural Optimization |
| title_sort | nonpenalty machine learning constraint handling using pso svm for structural optimization |
| url | http://dx.doi.org/10.1155/2021/6617750 |
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