Optimization of antenna lattice towers using genetic algorithm
By: Arjun, V
.
Contributor(s): Anjan, B. K.
Publisher: New York Springer 2022Edition: Vol.103(2), June.Description: 557-566p.Subject(s): Humanities and Applied SciencesOnline resources: Click here
In:
Journal of the institution of engineers (India): Series ASummary: This paper presents the design optimization of antenna lattice towers using genetic algorithm (GA). Design optimization of the antenna tower includes arriving at optimum configuration with respect to the bracing system and cross-sectional parameters of members when the weight of the tower is minimum satisfying a set of specified constraints. The variables considered for design optimization of tower are type of bracing in each panel and the cross-sectional properties of members. The proposed GA is mathematically formulated as a constrained nonlinear optimization problem. The components of the GA are described. The traditional GA is modified to handle this problem. Simple concepts such as selection of mutation probability and member grouping are used to accelerate convergence and reduce the computational effort. Numerical examples are given to validate developed algorithm and the examples with different bracing configurations.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2022-1754 |
This paper presents the design optimization of antenna lattice towers using genetic algorithm (GA). Design optimization of the antenna tower includes arriving at optimum configuration with respect to the bracing system and cross-sectional parameters of members when the weight of the tower is minimum satisfying a set of specified constraints. The variables considered for design optimization of tower are type of bracing in each panel and the cross-sectional properties of members. The proposed GA is mathematically formulated as a constrained nonlinear optimization problem. The components of the GA are described. The traditional GA is modified to handle this problem. Simple concepts such as selection of mutation probability and member grouping are used to accelerate convergence and reduce the computational effort. Numerical examples are given to validate developed algorithm and the examples with different bracing configurations.
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