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This article highlights the absence of published paradigms hybridized by The Cuckoo Search (CS) and Stochastic Paint Optimizer (SPO) for optimizing truss structures using composite materials under natural frequency constraints. The article proposes a novel optimization algorithm called CSSPO for optimizing truss structures made of composite materials, known as fiber-reinforced polymer (FRP) composites, to address this gap. Optimization problems of truss structures under frequency constraints are recognized as challenging due to their non-linear and non-convex search spaces that contain numerous local optima. The proposed methodology produces high-quality optimal solutions with less computational effort than the original methods. The aim of this work is to compare the performance of carbon FRP (CFRP), glass FRP (GFRP), and steel using a novel hybrid algorithm to provide valuable insights and inform decision-making processes in material selection and design. Four benchmark structure trusses with natural frequency constraints were utilized to demonstrate the efficiency and robustness of the CSSPO. The numerical analysis findings indicate that the CSSPO outperforms the classical SPO and exhibits comparable or superior performance when compared to the SPO. The study highlights that implementing CFRP and GFRP composites in truss construction leads to a notable reduction in weight compared to using steel.
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The Mountain Gazelle Optimizer for truss structures optimization
Computational tools have been used in structural engineering design for numerous objectives, typically focusing on optimizing a design process. We first provide a detailed literature review for optimizing truss structures with metaheuristic algorithms. Then, we evaluate an effective solution for designing truss structures used in structural engineering through a method called the mountain gazelle optimizer, which is a nature-inspired meta-heuristic algorithm derived from the social behavior of wild mountain gazelles. We use benchmark problems for truss optimization and a penalty method for handling constraints. The performance of the proposed optimization algorithm will be evaluated by solving complex and challenging problems, which are common in structural engineering design. The problems include a high number of locally optimal solutions and a non-convex search space function, as these are considered suitable to evaluate the capabilities of optimization algorithms. This work is the first of its kind, as it examines the performance of the mountain gazelle optimizer applied to the structural engineering design field while assessing its ability to handle such design problems effectively. The results are compared to other optimization algorithms, showing that the mountain gazelle optimizer can provide optimal and efficient design solutions with the lowest possible weight.
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- Award ID(s):
- 1916342
- PAR ID:
- 10538768
- Publisher / Repository:
- Applied Computing and Intelligence
- Date Published:
- Journal Name:
- Applied Computing and Intelligence
- Volume:
- 3
- Issue:
- 2
- ISSN:
- 2771-392X
- Page Range / eLocation ID:
- 116 to 144
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3934/aci.2023007
