Optimum design of truss structures by Material Generation Algorithm with discrete variables

Azizi, Mahdi; Shishehgarkhaneh, Milad Baghalzadeh; Basiri, Mahla

doi:10.1016/j.dajour.2022.100043

Cited by 19 publications

(8 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Korean game is the main concept of the SGO 32 algorithm. This game is played by several players.…”

Section: Improved Squid Game Optimizer For Enhancing the Target Movem...mentioning

confidence: 99%

Mobility target tracking with meta‐heuristic aided target movement prediction scheme in WSN using adaptive distributed extended Kalman filtering

Ramadevi,

Subramanyam,

Bindu

2024

Int J Communication

View full text Add to dashboard Cite

SummaryIn wireless sensor networks (WSNs), target tracking has been prominently raised in recent days. Because of the frequent utilization of WSN, the attention on target tracking is greatly increased. The estimated optimal value derived from the earlier moment is rarely taken into consideration in traditional target‐tracking algorithms. One of the most crucial uses of WSNs is mobile target tracking, and it is especially used for spying. Precision surveillance is heavily dependent on localization or distance estimation, and extensive study has been done in this area. This research aims to develop a new target‐tracking network‐assisted target movement prediction model in WSN with reduced energy consumption. The major phases involved in the proposed target movement prediction in WSN are (a) mobility target tracking and (b) target movement prediction. Initially, the mobility target tracking is done with the help of adaptive distributed extended Kalman filtering (ADEKF). The mobility target tracking performance is improved by optimally tuning the parameters from ADEKF with the support of the improved squid game optimizer (ISGO). Then, the target movement prediction phase is executed with the help of input parameters like “Angle of Arrival (AoA) and Received Signal Strength (RSS),” and the optimal progress of the mobile node is predicted. The implementation outcome of the proposed tracking network‐assisted target movement prediction model is validated concerning various performance metrics. Overall performance analysis shows that the developed model offers 2.5% in terms of RMSE measures. The developed model shows better analysis while validating with existing approaches.

show abstract

“…The Korean game is the main concept of the SGO 32 algorithm. This game is played by several players.…”

Section: Improved Squid Game Optimizer For Enhancing the Target Movem...mentioning

confidence: 99%

Mobility target tracking with meta‐heuristic aided target movement prediction scheme in WSN using adaptive distributed extended Kalman filtering

Ramadevi,

Subramanyam,

Bindu

2024

Int J Communication

View full text Add to dashboard Cite

show abstract

“…Clustering: Clustering algorithms, such as k-means, hierarchical clustering, and DBSCAN, are used to group similar data points based on their characteristics or proximity. In civil engineering, clustering can be used for analyzing geotechnical data to identify soil profiles, grouping similar construction materials, or segmenting structural elements based on their behavior [17]. 4.…”

Section: Machine Learning Algorithms and Their Applicationsmentioning

confidence: 99%

The Role of Artificial Intelligence in Civil Engineering Applications and Programs

Alghurair,

Fahim

2023

Journal of Engineering Sciences and Information Technology

View full text Add to dashboard Cite

The integration of artificial intelligence (AI) techniques in civil engineering programs and drawing techniques has the potential to revolutionize the industry. This study explores the benefits, challenges, and strategies for the effective utilization of AI in civil engineering. The research investigates the current state of AI integration, examines its applications in structural analysis, design, documentation, and project management, and identifies the barriers hindering widespread adoption. The study highlights the importance of data-driven decision-making, enhanced design visualization, improved accuracy and efficiency, streamlined documentation and communication, and enhanced project management and control. By addressing the challenges and providing insights into AI techniques, this study aims to bridge the gap between theoretical potential and practical implementation, paving the way for more sustainable and intelligent civil engineering projects.

show abstract

“…So, the objective function to minimize the total amount of CO2 in the project can be calculated by Eq. 19.…”

Section: Initialization and Decision Variablesmentioning

confidence: 99%

“…Furthermore, SI may be developed by the collective behaviour of artificial agents like robots in foraging robots [10]; (iii) Human and animal behaviour-based algorithms: they are inspired by some specific behaviour of individuals in society or animals in nature, such as Search Algorithm (CapSA) [11], Golden Tortoise Beetle Optimizer (GTBO) [12], Battle Royale Optimization (BRO) [13], passing vehicle search (PVS) [14], Dynamic Virtual Bats Algorithm (DVBA) [15], crow search algorithm (CSA) [16], and Tribe-Charged System Search (T-CSS) [17]. Finally, (iv) Physics-based algorithms: they are inspired by physics laws like Quantum mechanics, Universe theory, Newton's gravitational law, Electromagnetism, and Electrostatics, such as Material Generation Algorithm (MGA) [18,19], cyber-physical systems (CPS) [20], Archimedes optimization algorithm (AOA) [21], Lichtenberg Algorithm (LA) [22], Thermal Exchange Optimization algorithm (TEOA) [23], Charged System Search (CSS) [24,25], and Atomic Orbital Search (AOS) [26,27].…”

Section: Introductionmentioning

confidence: 99%

Fire Hawk Optimizer for Resource Trade-off in Project Scheduling based on BIM

Shishehgarkhaneh¹,

Azizi²,

Basiri³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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. A predefined stopping condition is taken into account while doing 30 independent optimization runs to obtain the statistical metrics, such as the mean, standard deviation, and the required number of objective function evaluations. The results show that the FHO algorithm is capable of producing competitive and exceptional outcomes when it comes to trade-off various resource options in projects.

show abstract

Optimum design of truss structures by Material Generation Algorithm with discrete variables

Cited by 19 publications

References 52 publications

Mobility target tracking with meta‐heuristic aided target movement prediction scheme in WSN using adaptive distributed extended Kalman filtering

Mobility target tracking with meta‐heuristic aided target movement prediction scheme in WSN using adaptive distributed extended Kalman filtering

The Role of Artificial Intelligence in Civil Engineering Applications and Programs

Fire Hawk Optimizer for Resource Trade-off in Project Scheduling based on BIM

Contact Info

Product

Resources

About