Optimum Design of Truss Structures with Atomic Orbital Search Considering Discrete Design Variables

Azizi, Mahdi; Mohamed, Ali Wagdy; Shishehgarkhaneh, Milad Baghalzadeh

doi:10.1007/978-3-031-07516-2_10

Cited by 7 publications

(1 citation statement)

References 43 publications

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“…A majority of real-world engineering problems are complicated; therefore, conventional methods are unable to solve these kinds of problems accurately. In other words, conventional optimization methods cannot find the optimum solution for time, cost, risk, and quality trade-off problems; hence, these problems have been solved by metaheuristic optimization algorithms, such as rat swarm optimizer (RSO) [10]; the wisdom of artificial crowds (WoAC) [11]; tuna swarm optimization (TSO) [12]; artificial bee colony (ABC) [13]; Material Generation Algorithm (MGA) [14,15]; and Atomic Orbital Search (AOS) [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

BIM-Based Resource Tradeoff in Project Scheduling Using Fire Hawk Optimizer (FHO)

et al. 2022

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Project managers should balance a variety of resource elements in building projects while taking into account many major concerns, including time, cost, quality, risk, and the environment. This study presents a framework for resource trade-offs in project scheduling based on the Building Information Modeling (BIM) methodology and metaheuristic algorithms. First, a new metaheuristic algorithm called Fire Hawk Optimizer (FHO) is used. Using project management software and the BIM process, a 3D model of the construction is created. In order to maximize quality while minimizing time, cost, risk, and CO2 in the project under consideration, an optimization problem is created, and the FHO’s capability for solving it is assessed. The results show that the FHO algorithm is capable of producing competitive and exceptional outcomes when it comes to the trade-off of various resource options in projects.

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