scholarly journals Optimization of Design Parameters and Cost of Geosynthetic-Reinforced Earth Walls Using Harmony Search Algorithm

2015 ◽  
Vol 1 (2) ◽  
Author(s):  
Kalehiwot Nega Manahiloh ◽  
Mohammad Motalleb Nejad ◽  
Mohammad Sadegh Momeni
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Design Parameters ◽  
Reinforced Earth ◽  
Reinforced Earth Walls ◽  
Earth Walls ◽  
Optimization Of Design Parameters

scholarly journals The Buttressed Walls Problem: An Application of a Hybrid Clustering Particle Swarm Optimization Algorithm

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 862 ◽  
Author(s):  
José García ◽  
José V. Martí ◽  
Víctor Yepes
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Particle Swarm ◽  
Retaining Walls ◽  
Harmony Search Algorithm ◽  
Swarm Optimization ◽  
Practical Applications ◽  
Reinforced Earth

The design of reinforced earth retaining walls is a combinatorial optimization problem of interest due to practical applications regarding the cost savings involved in the design and the optimization in the amount of CO 2 emissions generated in its construction. On the other hand, this problem presents important challenges in computational complexity since it involves 32 design variables; therefore we have in the order of 10 20 possible combinations. In this article, we propose a hybrid algorithm in which the particle swarm optimization method is integrated that solves optimization problems in continuous spaces with the db-scan clustering technique, with the aim of addressing the combinatorial problem of the design of reinforced earth retaining walls. This algorithm optimizes two objective functions: the carbon emissions embedded and the economic cost of reinforced concrete walls. To assess the contribution of the db-scan operator in the optimization process, a random operator was designed. The best solutions, the averages, and the interquartile ranges of the obtained distributions are compared. The db-scan algorithm was then compared with a hybrid version that uses k-means as the discretization method and with a discrete implementation of the harmony search algorithm. The results indicate that the db-scan operator significantly improves the quality of the solutions and that the proposed metaheuristic shows competitive results with respect to the harmony search algorithm.

Optimal linear cooperation spectrum sensing method based on chaos harmony search algorithm

2013 ◽  
Vol 32 (9) ◽  
pp. 2412-2417
Author(s):  
Yue-hong LI ◽  
Pin WAN ◽  
Yong-hua WANG ◽  
Jian YANG ◽  
Qin DENG
Keyword(s):  
Spectrum Sensing ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Optimal Linear

Performance Study of Harmony Search Algorithm for Multilevel Thresholding

2016 ◽  
Vol 25 (4) ◽  
pp. 473-513 ◽  
Author(s):  
Salima Ouadfel ◽  
Abdelmalik Taleb-Ahmed
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Computational Time ◽  
Multilevel Thresholding ◽  
Performance Study ◽  
Bacterial Foraging Optimization ◽  
Bee Colony ◽  
Fitness Value ◽  
Multiple Thresholds

AbstractThresholding is the easiest method for image segmentation. Bi-level thresholding is used to create binary images, while multilevel thresholding determines multiple thresholds, which divide the pixels into multiple regions. Most of the bi-level thresholding methods are easily extendable to multilevel thresholding. However, the computational time will increase with the increase in the number of thresholds. To solve this problem, many researchers have used different bio-inspired metaheuristics to handle the multilevel thresholding problem. In this paper, optimal thresholds for multilevel thresholding in an image are selected by maximizing three criteria: Between-class variance, Kapur and Tsallis entropy using harmony search (HS) algorithm. The HS algorithm is an evolutionary algorithm inspired from the individual improvisation process of the musicians in order to get a better harmony in jazz music. The proposed algorithm has been tested on a standard set of images from the Berkeley Segmentation Dataset. The results are then compared with that of genetic algorithm (GA), particle swarm optimization (PSO), bacterial foraging optimization (BFO), and artificial bee colony algorithm (ABC). Results have been analyzed both qualitatively and quantitatively using the fitness value and the two popular performance measures: SSIM and FSIM indices. Experimental results have validated the efficiency of the HS algorithm and its robustness against GA, PSO, and BFO algorithms. Comparison with the well-known metaheuristic ABC algorithm indicates the equal performance for all images when the number of thresholds M is equal to two, three, four, and five. Furthermore, ABC has shown to be the most stable when the dimension of the problem is too high.

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