Size, Layout, and Topology Optimization of Skeletal Structures Using Plasma Generation Optimization
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RESEARCH PAPER
Size, Layout, and Topology Optimization of Skeletal Structures Using Plasma Generation Optimization Ali Kaveh1 · Seyed Milad Hosseini1 · Ataollah Zaerreza1 Received: 15 September 2020 / Accepted: 23 October 2020 © Shiraz University 2020
Abstract In this paper, plasma generation optimization (PGO) as a newly developed physics-based metaheuristic algorithm is applied to perform the size, layout, and topology optimization problems of skeletal structures. PGO is a population-based optimizer inspired by the process of plasma generation. In this optimization method, each agent is modeled as an electron. The movement of electrons and changing their energy level are performed based on simulating the process of excitation, de-excitation, and ionization. These processes occur iteratively through the plasma generation. Evaluating the robustness and performance of the PGO is illustrated through six design examples for different types of structural optimization. The results reveal that the PGO algorithm outperforms other state-of-the-art optimization techniques considered from the literature. Keywords Structural optimization · Plasma generation optimization · Sizing optimization · Layout optimization · Topology optimization · Skeletal structures
1 Introduction Designing a structure can be a complex task, particularly when it contains a large number of elements and nodes. In the field of structural optimization, the structural designer should determine the optimum member area of each element (sizing optimization), shape/profile of the structure (layout optimization), and connectivity of structural elements (topology optimization) (Hare et al. 2013; Kaveh 2017). Although these three fields were performed independently in the previous studies, applying simultaneous optimization of sizing, layout, and topology due to providing appropriate results has been indicated in recently developed optimization methods (Kaveh and Zaerreza 2020; Li et al. 2014; Maheri et al. 2016; Panagant and Bureerat 2018). In size optimization, design variables can be considered either continuous or discrete. Each design variable represents * Ali Kaveh [email protected] Seyed Milad Hosseini [email protected] Ataollah Zaerreza [email protected] 1
School of Civil Engineering, Iran University of Science and Technology, P.O. Box 16846‑13114, Tehran, Iran
a cross section of either a member or an element group of the members. When design variables are discrete, they are selected from a list of discrete cross sections. However, when design variables are continuous, they can vary continuously in the permissible range. In the field of solely sizing optimization, many researchers have carried out a great number of studies. For instance, Degertekin (2008) developed the harmony search (HS) algorithm for the optimal design of planar steel frame structures. Kaveh and Moradveisi (2016) employed colliding bodies optimization (CBO) and its enhanced variant (ECBO) for the optimal design of double-layer barrel vaults. H
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