scholarly journals Optimal Design of Multi-Span Pitched Roof Frames with Tapered Members

2018 ◽  
Author(s):  
Ali Kaveh ◽  
Mohammad Zaman Kabir ◽  
Mahdi Bohlool
Keyword(s):  
Optimal Design ◽  
Heuristic Algorithms ◽  
Optimization Algorithms ◽  
Lateral Loads ◽  
Optimal Weight ◽  
Industrial Buildings ◽  
Optimum Weight ◽  
Apex Height ◽  
Tapered Members

Many industrial buildings require large spans and high height, and the use of a frame with inclined roofs with non-prismatic elements can reduce the usage of steel. Pitched roof frame with single spans are optimized using different meta-heuristic algorithms. In this paper, the optimal design of industrial frames with two and three spans under gravity and lateral loads is performed. Five efficient and widely accepted optimization algorithms are used to optimize each frame. The convergence histories and design results of these algorithms are compared and the most suitable algorithm is determined. In each frame, the effect of increasing the apex height is evaluated on the optimal weight and the best angle is determined for optimum weight.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

Comparison of four meta-heuristic algorithms for optimal design of double-layer barrel vaults

2018 ◽  
Vol 33 (3-4) ◽  
pp. 115-123
Author(s):  
Ali Kaveh ◽  
Majid Ilchi Ghazaan ◽  
Soroush Mahjoubi
Keyword(s):  
Optimal Design ◽  
Double Layer ◽  
Heuristic Algorithms ◽  
Size Optimization ◽  
Community Centers ◽  
Cross Sectional ◽  
Long Span ◽  
Design Variables ◽  
Rail Station ◽  
Barrel Vaults

Barrel vaults are effective semi-cylindrical forms of roof systems that are widespread for multipurpose facilities including warehouse, rail station, pools, sports center, airplane hungers, and community centers because of providing long-span and economical roof with significant amount of space underneath. In the present study, size optimization of double-layer barrel vaults with different configurations is studied. Four recently developed algorithms consisting of the CBO, ECBO, VPS, and MDVC-UVPS are employed and their performances are compared. The structures are subjected to stress, stability, and displacement limitations according to the provisions of AISC-ASD. The design variables are the cross-sectional areas of the bar elements which are selected from steel pipe sections. The numerical results indicate that the MDVC-UVPS outperforms the other algorithms in finding optimal design in all examples.

Comparative Evaluation of Algorithms for Achieving Ultrapacked Thermal Greases: Microstructural Models and Effective Behavior

2020 ◽  
Vol 142 (4) ◽  
Author(s):  
Sukshitha Achar P. L ◽  
Huanyu Liao ◽  
Ganesh Subbarayan
Keyword(s):  
Heuristic Algorithms ◽  
Volume Fraction ◽  
Augmented Lagrangian Method ◽  
Random Network ◽  
Optimization Algorithms ◽  
Particle Packing ◽  
Computational Time ◽  
Particle Volume ◽  
Volume Fractions ◽  
Number Of Particles

Abstract In this work, we develop and evaluate algorithms for generating ultrapacked microstructures of particles. Simulated microstructures reported in the literature rarely contain particle volume fractions greater than 60%. However, commercially available thermal greases appear to achieve volume fractions in the range of 60–80%. Therefore, to analyze the effectiveness of commercially available particle-filled thermal interface materials (TIM), there is a need to develop algorithms capable of generating ultrapacked microstructures. The particle packing problem is initially posed as a nonlinear programming problem, and formal optimization algorithms are applied to generate microstructures that are maximally packed. The packing efficiency in the simulated microstructure is dependent on the number of particles in the simulation cell; however, as the number of particles increases, the packing simulation is computationally expensive. Here, the computational time to generate microstructures with large number of particles is systematically evaluated first using optimization algorithms. The algorithms include the penalty function methods, best-in-class sequential quadratic programming method, matrix-less conjugate gradient method as well as the augmented Lagrangian method. Heuristic algorithms are next evaluated to achieve computationally efficient packing. The evaluated heuristic algorithms are mainly based on the drop-fall-shake (DFS) method, but modified to more effectively simulate the mixing process in commercial planetary mixers. With the developed procedures, representative volume elements (RVE) with volume fraction as high as 74% are demonstrated. The simulated microstructures are analyzed using our previously developed random network model to estimate the effective thermal and mechanical behavior given a particle arrangement.

scholarly journals Optimized Location-Allocation of Earthquake Relief Centers Using PSO and ACO, Complemented by GIS, Clustering, and TOPSIS

2018 ◽  
Vol 7 (8) ◽  
pp. 292 ◽  
Author(s):  
Bahram Saeidian ◽  
Mohammad Mesgari ◽  
Biswajeet Pradhan ◽  
Mostafa Ghodousi
Keyword(s):  
Heuristic Algorithms ◽  
Initial Conditions ◽  
Optimization Algorithms ◽  
Gis Analysis ◽  
Clustering Method ◽  
Geospatial Information ◽  
Location Allocation ◽  
Urban Disaster ◽  
Location Allocation Problem ◽  
Selection Of

After an earthquake, it is required to establish temporary relief centers in order to help the victims. Selection of proper sites for these centers has a significant effect on the processes of urban disaster management. In this paper, the location and allocation of relief centers in district 1 of Tehran are carried out using Geospatial Information System (GIS), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision model, a simple clustering method and the two meta-heuristic algorithms of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). First, using TOPSIS, the proposed clustering method and GIS analysis tools, sites satisfying initial conditions with adequate distribution in the area are chosen. Then, the selection of proper centers and the allocation of parcels to them are modelled as a location/allocation problem, which is solved using the meta-heuristic optimization algorithms. Also, in this research, PSO and ACO are compared using different criteria. The implementation results show the general adequacy of TOPSIS, the clustering method, and the optimization algorithms. This is an appropriate approach to solve such complex site selection and allocation problems. In view of the assessment results, the PSO finds better answers, converges faster, and shows higher consistency than the ACO.

scholarly journals A Survey on Meta-heuristic Algorithms for Global Optimization Problems

2020 ◽  
pp. 48-60
Author(s):  
Abdel Nasser H. Zaied ◽  
Mahmoud Ismail ◽  
Salwa El-Sayed ◽  
◽  
◽  
...  
Keyword(s):  
Global Optimization ◽  
Real World ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Traditional Methods ◽  
Complex Problems ◽  
First Choice ◽  
Important Field ◽  
Real World Problems

Optimization is a more important field of research. With increasing the complexity of real-world problems, the more efficient and reliable optimization algorithms vital. Traditional methods are unable to solve these problems so, the first choice for solving these problems becomes meta-heuristic algorithms. Meta-heuristic algorithms proved their ability to solve more complex problems and giving more satisfying results. In this paper, we introduce the more popular meta-heuristic algorithms and their applications in addition to providing the more recent references for these algorithms.

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