Harmony Search Algorithm for Structural Engineering Problems

2021 ◽  
pp. 13-47
Author(s):  
Aylin Ece Kayabekir ◽  
Gebrail Bekdaş ◽  
Melda Yücel ◽  
Sinan Melih Nigdeli ◽  
Zong Woo Geem
Keyword(s):  
Structural Engineering ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Engineering Problems

scholarly journals An Improved Novel Global Harmony Search Algorithm Based on Selective Acceptance

2020 ◽  
Vol 10 (6) ◽  
pp. 1910 ◽  
Author(s):  
Hui Li ◽  
Po-Chou Shih ◽  
Xizhao Zhou ◽  
Chunming Ye ◽  
Li Huang
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Continuous Optimization ◽  
Harmony Search Algorithm ◽  
Experimental Results ◽  
Metaheuristic Algorithms ◽  
Engineering Problems ◽  
New Harmony ◽  
Continuous Optimization Problems

The novel global harmony search (NGHS) algorithm is proposed in 2010, and it is an improved harmony search (HS) algorithm which combines the particle swarm optimization (PSO) and the genetic algorithm (GA). One of the main differences between the HS and NGHS algorithms is that of using different mechanisms to renew the harmony memory (HM). In the HS algorithm, in each iteration, the new harmony is accepted and replaced the worst harmony in the HM while the fitness of the new harmony is better than the worst harmony in the HM. Conversely, in the NGHS algorithm, the new harmony replaces the worst harmony in the HM without any precondition. However, in addition to these two mechanisms, there is one old mechanism, the selective acceptance mechanism, which is used in the simulated annealing (SA) algorithm. Therefore, in this paper, we proposed the selective acceptance novel global harmony search (SANGHS) algorithm which combines the NGHS algorithm with a selective acceptance mechanism. The advantage of the SANGHS algorithm is that it balances the global exploration and local exploitation ability. Moreover, to verify the search ability of the SANGHS algorithm, we used the SANGHS algorithm in ten well-known benchmark continuous optimization problems and two engineering problems and compared the experimental results with other metaheuristic algorithms. The experimental results show that the SANGHS algorithm has better search ability than the other four harmony search algorithms in ten continuous optimization problems. In addition, in two engineering problems, the SANGHS algorithm also provided a competition solution compared with other state-of-the-art metaheuristic algorithms.

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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