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Journal articles on the topic 'Improved harmony search'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Ouyang, Hai-bin, Li-qun Gao, Steven Li, Xiang-yong Kong, Qing Wang, and De-xuan Zou. "Improved Harmony Search Algorithm: LHS." Applied Soft Computing 53 (April 2017): 133–67. http://dx.doi.org/10.1016/j.asoc.2016.12.042.

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2

Li, Jian Ping, Ai Ping Lu, Hao Chang Wang, Xin Li, and Pan Chi Li. "An Improved Harmony Search Algorithm with Application." Applied Mechanics and Materials 687-691 (November 2014): 1367–72. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1367.

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In classical harmony search algorithm, only one harmony vector is obtained in each of iteration, which affects its search ability. We propose an improve harmony search algorithm in this paper. In our approach, the number of harmony vectors obtained in each of iteration is equivalent to the population size, and all newly generated harmony vectors are put into the harmony memory array. Then, all harmony vectors are sorted by descending order of the fitness, and the first half individuals are served as the next generation of populations. Experimental results show that our approach is obviously su

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3

Kang, Di-Wen, Li-Ping Mo, and Kai-Qing Zhou. "An Improved Harmony Search Algorithm with Segmented Search." IOP Conference Series: Materials Science and Engineering 864 (July 10, 2020): 012063. http://dx.doi.org/10.1088/1757-899x/864/1/012063.

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4

Kong, Zhi, Guo Dong Zhang, and Li Fu Wang. "An Improved Novel Global Harmony Search Algorithm." Applied Mechanics and Materials 644-650 (September 2014): 2169–72. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2169.

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This paper develops an improved novel global harmony search (INGHS) algorithm for solving optimization problems. INGHS employs a novel method for generating new solution vectors that enhances accuracy and convergence rate of novel global harmony search (NGHS) algorithm. Simulations for five benchmark test functions show that INGHS possesses better ability to find the global optimum than that of harmony search (HS) algorithm. Compared with NGHS and HS, INGHS is better in terms of robustness and efficiency.

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5

Ajay Kumar Beesetti, Ajay Kumar Beesetti. "Automatic Clustering Using Improved Harmony Search." IOSR Journal of Computer Engineering 9, no. 5 (2013): 65–68. http://dx.doi.org/10.9790/0661-0956568.

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6

Zhang, Ping, Mei Ling Li, Qian Han, Yi Ning Zhang, and Guo Jun Li. "Improved Global Harmony Search Algorithm for Numerical Optimization." Applied Mechanics and Materials 587-589 (July 2014): 2295–98. http://dx.doi.org/10.4028/www.scientific.net/amm.587-589.2295.

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To intend to improve the optimization performance of harmony search (HS) algorithm, an improved global harmony search (IGHS) algorithm was presented in this paper. In this algorithm, inspired by swarm intelligence, the global best harmony are borrowed to enhance the optimization accuracy of HS; and mutation and crossover operation instead of pitch adjustment operation to improved the algorithm convergence rate. The key parameters are adjusted to balance the local and global search. Several benchmark experiment simulations, the IGHS has demonstrated stronger convergence and stability than origi

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7

Xia, Hong Gang, and Qing Liang Wang. "Improved Harmony Search Algorithm with Crossover Operation." Applied Mechanics and Materials 415 (September 2013): 353–56. http://dx.doi.org/10.4028/www.scientific.net/amm.415.353.

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Harmony search (HS) algorithm is a good meta-heuristic intelligent optimization method and it does depend on imitating the music improvisation process to generate a perfect state of harmony. However, intelligent optimization methods is easily trapped into local optimal, HS is no exception. In order to improve the performance of HS, a new variant of harmony search algorithm is proposed in this paper. The variant introduce a new crossover operation into HS, and design a strategy to adjust parameter pitch adjusting rate (PAR) and bandwidth (BW). Several standard benchmarks carried out to be teste

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8

Xia, Hong Gang, Qing Zhou Wang, and Li Qun Gao. "Opposition-Based Improved Harmony Search Algorithm Solve Unconstrained Optimization Problems." Applied Mechanics and Materials 365-366 (August 2013): 170–73. http://dx.doi.org/10.4028/www.scientific.net/amm.365-366.170.

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This paper develops an opposition-based improved harmony search algorithm (OIHS) for solving global continuous optimization problems. The proposed method is different from the classical harmony search (HS) in three aspects. Firstly, the candidate harmony is randomly chosen from the harmony memory or opposition harmony memory was generated by opposition-based learning, which enlarged the algorithm search space. Secondly, two key control parameters, pitch adjustment rate (PAR) and bandwidth distance (bw), are adjusted dynamically with respect to the evolution of the search process. Numerical res

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9

Li, Hui, Po-Chou Shih, Xizhao Zhou, Chunming Ye, and Li Huang. "An Improved Novel Global Harmony Search Algorithm Based on Selective Acceptance." Applied Sciences 10, no. 6 (2020): 1910. http://dx.doi.org/10.3390/app10061910.

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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 wors

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10

Wang, Ling, Ruixin Yang, Yin Xu, Qun Niu, Panos M. Pardalos, and Minrui Fei. "An improved adaptive binary Harmony Search algorithm." Information Sciences 232 (May 2013): 58–87. http://dx.doi.org/10.1016/j.ins.2012.12.043.

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11

El-Abd, Mohammed. "An improved global-best harmony search algorithm." Applied Mathematics and Computation 222 (October 2013): 94–106. http://dx.doi.org/10.1016/j.amc.2013.07.020.

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12

Wang, Li Fu, Zhi Kong, and Xin Gang Wang. "Harmony Search Optimization Algorithm Based on Normal Cloud." Advanced Materials Research 760-762 (September 2013): 1825–30. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1825.

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The pitch adjusting rate (PAR) is an important parameter in harmony search algorithm, which indicates that the algorithm will choose a neighboring value with a probability. The traditional harmony search algorithm uses fixed value for PAR. However, PAR should be increased when all objective values are centralized and decreased when the function values are scattered in the solution space. In this paper, a new cloud harmony search algorithm (CHS) is proposed. We introduce cloud model theory to adjust the value PAR in the harmony search to improve the global search ability and make faster converg

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13

Zhang, Ping, Mei Ling Li, Qian Han, and Guo Jun Li. "A Self-Study Harmony Search Algorithm for Optimization Problems." Advanced Materials Research 1006-1007 (August 2014): 1017–20. http://dx.doi.org/10.4028/www.scientific.net/amr.1006-1007.1017.

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A self-study harmony search (SSHS) algorithm for solving unconstrained optimization problems has presented in this paper . SSHS employs a novel self-study strategy to generate new solution vectors which can enhance accuracy and convergence rate of harmony search (HS) algorithm. SSHS algorithm as proposed, the harmony memory consideration rate (HMCR) is dynamically adapted to the changing of objective function value in the current harmony memory. a large number of experiments improved that SSHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and i

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14

Bao, Xiaoan, Shuhan Liu, Na Zhang, and Meng Dong. "Combinatorial Test Generation using Improved Harmony Search Algorithm." International Journal of Hybrid Information Technology 8, no. 9 (2015): 121–30. http://dx.doi.org/10.14257/ijhit.2015.8.9.13.

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15

Wang, Li Fu, Zhi Kong, and Xin Gang Wang. "Harmony Search Algorithm Based on Cloud Theory." Advanced Materials Research 756-759 (September 2013): 3378–83. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3378.

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The parameter bw is a distance bandwidth between 0 and 1 in harmony search algorithm, which helps the algorithm in finding globally and locally improved solution. In this paper, a new harmony search algorithm is proposed in order to improve the convergence speed and the global search algorithm of the harmony search algorithm. The parameter bw is adjusted based on cloud theory in this new algorithm. The new algorithm is tested on some benchmark functions and the results are compared with the result of the traditional harmony search. Experimental results indicate that the new harmony search algo

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16

Wang, Yi Wen, and Min Yao. "Dynamic Adaptive Harmony Search Algorithm for Optimization." Key Engineering Materials 474-476 (April 2011): 1666–71. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.1666.

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A new meta-heuristic optimization algorithm–harmony search is conceptualized using the musical improvisation process of searching for a perfect state of harmony. Although several variants and an increasing number of applications have appeared, one of its main difficulties is how to select suitable parameter values. In this paper, we proposed a novel algorithm to dynamically adapt the harmony memory consideration rate (HMCR) and pitch adjustment rate (PAR) and distance bandwidth (BW). The experimental results revealed the superiority of the proposed method to the original HS, improved harmony s

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17

Erwin, Erwin, Saparudin Saparudin, and Wulandari Saputri. "Hybrid Multilevel Thresholding and Improved Harmony Search Algorithm for Segmentation." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2018): 4593–602. https://doi.org/10.11591/ijece.v8i6.pp4593-4602.

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This paper proposes a new method for image segmentation is hybrid multilevel thresholding and improved harmony search algorithm. Improved harmony search algorithm which is a method for finding vector solutions by increasing its accuracy. The proposed method looks for a random candidate solution, then its quality is evaluated through the Otsu objective function. Furthermore, the operator continues to evolve the solution candidate circuit until the optimal solution is found. The dataset used in this study is the retina dataset, tongue, lenna, baboon, and cameraman. The experimental results show

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18

Liu, Liqun, Jiuyuan Huo, Fei Xue, and Yongqiang Dai. "Harmony Search Method with Global Sharing Factor Based on Natural Number Coding for Vehicle Routing Problem." Information 11, no. 2 (2020): 86. http://dx.doi.org/10.3390/info11020086.

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This paper proposes an improved Harmony Search algorithm, and gives the definition of the Global Sharing Factor of the Harmony Search (HS) algorithm. In the definition, the number of creations of the HS algorithm is applied to the sharing factor and calculated. In this algorithm, the natural harmony encoding method is used to encode the initial harmony, and the total path length of all vehicles is taken as the optimization objective function. A new harmony generation strategy is proposed as follows: each tone component in an evolution is calculated separately using the new learning strategy an

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19

Im, Sung Soo, Do Guen Yoo, and Joong Hoon Kim. "Smallest-Small-World Cellular Harmony Search for Optimization of Unconstrained Benchmark Problems." Journal of Applied Mathematics 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/635608.

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We presented a new hybrid method that combines cellular harmony search algorithms with the Smallest-Small-World theory. A harmony search (HS) algorithm is based on musical performance processes that occur when a musician searches for a better state of harmony. Harmony search has successfully been applied to a wide variety of practical optimization problems. Most of the previous researches have sought to improve the performance of the HS algorithm by changing the pitch adjusting rate and harmony memory considering rate. However, there has been a lack of studies to improve the performance of the

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20

Xia, Hong Gang, and Qing Zhou Wang. "Hybrid Differential Evolution Harmony Search Algorithm for Power Economic Dispatch Problems." Applied Mechanics and Materials 415 (September 2013): 345–48. http://dx.doi.org/10.4028/www.scientific.net/amm.415.345.

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In this paper, a hybrid differential evolution harmony search (HDEHS) algorithm was presented for solving power economic dispatch problems. In this algorithm, mutation and crossover operation instead of harmony memory consideration and pitch adjustment operation, this improved the algorithm convergence rate. Moreover, dynamically adjust the key parameter (e.g. mutagenic factor F, crossover rate CR) to balance the local and global search. Based on a 13 units power system experiment simulations, the HDEHS has demonstrated stronger convergence and stability than original harmony search (HS) algor

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21

Fourie, Jaco, Steven Mills, and Richard Green. "Harmony filter: A robust visual tracking system using the improved harmony search algorithm." Image and Vision Computing 28, no. 12 (2010): 1702–16. http://dx.doi.org/10.1016/j.imavis.2010.05.006.

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22

Tuo, Shouheng, Longquan Yong, and Tao Zhou. "An Improved Harmony Search Based on Teaching-Learning Strategy for Unconstrained Optimization Problems." Mathematical Problems in Engineering 2013 (2013): 1–29. http://dx.doi.org/10.1155/2013/413565.

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Harmony search (HS) algorithm is an emerging population-based metaheuristic algorithm, which is inspired by the music improvisation process. The HS method has been developed rapidly and applied widely during the past decade. In this paper, an improved global harmony search algorithm, named harmony search based on teaching-learning (HSTL), is presented for high dimension complex optimization problems. In HSTL algorithm, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to maintain the proper balance between conver

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23

Xia, Hong Gang, and Qing Liang Wang. "Modified Harmony Search Algorithm for 0-1 Knapsack Problems." Applied Mechanics and Materials 365-366 (August 2013): 182–85. http://dx.doi.org/10.4028/www.scientific.net/amm.365-366.182.

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In this paper, a modified harmony search (MHS) algorithm was presented for solving 0-1 knapsack problems. MHS employs position update strategy for generating new solution vectors that enhances accuracy and convergence rate of harmony search (HS) algorithm. Besides, the harmony memory consideration rate (HMCR) is dynamically adapted to the changing of objective function value in the current harmony memory, and the key parameters PAR and BW dynamically adjusted with the number of generation. Based on the experiment of solving ten classic 0-1 knapsack problems, the MHS has demonstrated stronger c

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24

Xia, Hong Gang, and Qing Zhou Wang. "A Modified Harmony Search Algorithm for Numerical Optimization Problems." Advanced Materials Research 989-994 (July 2014): 2532–35. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2532.

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This paper presents a modified harmony search (MHS) algorithm for solving numerical optimization problems. MHS employs a novel self-learning strategy for generating new solution vectors that enhances accuracy and convergence rate of harmony search (HS) algorithm. In the proposed MHS algorithm, the harmony memory consideration rate (HMCR) is dynamically adapted to the changing of objective function value in the current harmony memory. The other two key parameters PAR and bw adjust dynamically with generation number. Based on a large number of experiments, MHS has demonstrated stronger convergen

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25

Pandian, Arun Nambi, and Aravindhababu Palanivelu. "Multi-objective FACTS placement using improved harmony search optimization." Journal of Intelligent & Fuzzy Systems 39, no. 3 (2020): 3839–51. http://dx.doi.org/10.3233/jifs-192178.

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Optimal placement of FACTS devices attempts to improve power transfer, minimize active power loss, enhance voltage profile and improve voltage stability, thereby making the operation of power systems more flexible and secured. The classical methods experience difficulties in solving the FACTS placement problem (FPP) with discontinuous functions and may diverge or result oscillatory convergence. Besides the number of FACTS devices for placement should be given as an input while solving the problem. The solution methods then attempts to forcefully place all the specified number of devices in the

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26

Yanqiang GE, Xiangzheng WANG, and Qingsheng LI. "A improved Harmony Search Algorithm based on Association Rules." International Journal of Advancements in Computing Technology 3, no. 9 (2011): 122–28. http://dx.doi.org/10.4156/ijact.vol3.issue9.16.

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27

Chakraborty,, Prithwish, Gourab Ghosh Roy, Swagatam Das, Dhaval Jain, and Ajith Abraham. "An Improved Harmony Search Algorithm with Differential Mutation Operator." Fundamenta Informaticae 95, no. 4 (2009): 401–26. http://dx.doi.org/10.3233/fi-2009-157.

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28

Shafaati, Mehrnoosh, and Hamed Mojallali. "IIR filter optimization using improved chaotic harmony search algorithm." Automatika 59, no. 3-4 (2018): 331–39. http://dx.doi.org/10.1080/00051144.2018.1541643.

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29

Kallioras, Nikos A., Nikos D. Lagaros, and Matthew G. Karlaftis. "An improved harmony search algorithm for emergency inspection scheduling." Engineering Optimization 46, no. 11 (2013): 1570–92. http://dx.doi.org/10.1080/0305215x.2013.854351.

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30

Kalivarapu, J., S. Jain, and S. Bag. "An improved harmony search algorithm with dynamically varying bandwidth." Engineering Optimization 48, no. 7 (2015): 1091–108. http://dx.doi.org/10.1080/0305215x.2015.1090570.

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31

Nazari, T., M. Aghaie, A. Zolfaghari, A. Minuchehr, and A. Norouzi. "WWER core pattern enhancement using adaptive improved harmony search." Nuclear Engineering and Design 254 (January 2013): 23–32. http://dx.doi.org/10.1016/j.nucengdes.2012.08.018.

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32

Mahdavi, M., M. Fesanghary, and E. Damangir. "An improved harmony search algorithm for solving optimization problems." Applied Mathematics and Computation 188, no. 2 (2007): 1567–79. http://dx.doi.org/10.1016/j.amc.2006.11.033.

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33

Sinsuphan, Nampetch, Uthen Leeton, and Thanatchai Kulworawanichpong. "Optimal power flow solution using improved harmony search method." Applied Soft Computing 13, no. 5 (2013): 2364–74. http://dx.doi.org/10.1016/j.asoc.2013.01.024.

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34

Contreras, Jhonatan, Iván Amaya, and Rodrigo Correa. "An improved variant of the conventional Harmony Search algorithm." Applied Mathematics and Computation 227 (January 2014): 821–30. http://dx.doi.org/10.1016/j.amc.2013.11.050.

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35

Li, Wenjing, Wenhong Du, Weifeng Tang, Ying Pan, Jie Zhou, and Zhongming Lin. "Parallel algorithm of multiobjective optimization harmony search based on cloud computing." Journal of Algorithms & Computational Technology 11, no. 3 (2017): 301–13. http://dx.doi.org/10.1177/1748301817713185.

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In order to solve the problems of traditional harmony search in complex function multiobjective optimization, such as low precision, slow convergence, and easy to fall into local optimum, this article proposes a multiobjective optimization harmony search parallel algorithm based on cloud computing. First, according to the characteristics that the traditional harmony search algorithm uses a single harmony library for storing and processing the memory harmony, and it is divided into multiple harmony sublibraries according to different harmony. At the same time, the roulette selection and dynamic

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36

Karimullah, Shaik, D. Vishnuvardhan, Muhammad Arif, Vinit Kumar Gunjan, Fahimuddin Shaik, and Kazy Noor-e.-alam Siddiquee. "An Improved Harmony Search Approach for Block Placement for VLSI Design Automation." Wireless Communications and Mobile Computing 2022 (April 29, 2022): 1–10. http://dx.doi.org/10.1155/2022/3016709.

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The technology grows quickly in the area of the VLSI physical design; it is crucial to integrate the greater number of transistors and parts into a very small range. Before the placement is completed, the physical and technical positioning of the blocks in the chip area is planned, which is nothing but floor planning. In order to lessen the placement region in the physical layout, floor planning must be carried out effectively. This paper proposes a blended harmony search and particle swarm optimization (BHSPS) algorithm which is the deliberate blend of the harmony search (HS) algorithm, and t

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37

Guan, Li Ming, Qian Kai Yang, Jian Lin, and Yi Fan Wu. "2-D Image Matching Based on Global Harmony Search Optimization." Applied Mechanics and Materials 239-240 (December 2012): 1133–37. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.1133.

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A two-dimensional image matching method based on the improved Hausdorff distance and global harmony search optimization is presented in this paper. First, edges are extracted form the original images by the Canny edge detector. Then, a fitness function based on the improved Hausdorff distance is constructed. Finally the global harmony search optimization is adopted to optimize the fitness function. Experiments show that the proposed method is able to locate the object of interest globally and efficiently.

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38

Erwin, Erwin, Saparudin Saparudin, and Wulandari Saputri. "Hybrid Multilevel Thresholding and Improved Harmony Search Algorithm for Segmentation." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2018): 4593. http://dx.doi.org/10.11591/ijece.v8i6.pp4593-4602.

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This paper proposes a new method for image segmentation is hybrid multilevel thresholding and improved harmony search algorithm. Improved harmony search algorithm which is a method for finding vector solutions by increasing its accuracy. The proposed method looks for a random candidate solution, then its quality is evaluated through the Otsu objective function. Furthermore, the operator continues to evolve the solution candidate circuit until the optimal solution is found. The dataset used in this study is the retina dataset, tongue, lenna, baboon, and cameraman. The experimental results show

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39

Sun, Wei, and Xingyan Chang. "An Improved Harmony Search Algorithm for Power Distribution Network Planning." Journal of Electrical and Computer Engineering 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/753712.

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Distribution network planning because of involving many variables and constraints is a multiobjective, discrete, nonlinear, and large-scale optimization problem. Harmony search (HS) algorithm is a metaheuristic algorithm inspired by the improvisation process of music players. HS algorithm has several impressive advantages, such as easy implementation, less adjustable parameters, and quick convergence. But HS algorithm still has some defects such as premature convergence and slow convergence speed. According to the defects of the standard algorithm and characteristics of distribution network pl

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40

Abdel-Raouf, Osama, Mohamed Abdel-Baset, and Ibrahim El-henawy. "Improved Harmony Search with Chaos for Solving Linear Assignment Problems." International Journal of Intelligent Systems and Applications 6, no. 5 (2014): 55–61. http://dx.doi.org/10.5815/ijisa.2014.05.05.

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41

Ragab, Mahmoud, and Adel A. Bahaddad. "Improved Harmony Search with Optimal Deep Learning Enabled Classification Model." Computers, Materials & Continua 73, no. 1 (2022): 1783–97. http://dx.doi.org/10.32604/cmc.2022.028055.

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42

Rahimkhani, Khadije, and Fatemeh Forouzesh. "Improved Routing in Wireless Sensor Networks Using Harmony Search Algorithm." Wireless Sensor Network 09, no. 09 (2017): 333–53. http://dx.doi.org/10.4236/wsn.2017.99019.

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43

henawy, Ibrahim El, Osama Abdel Raouf, and Mohamed Abdelbaset. "Improved harmony search algorithm with chaos for solving definite integral." International Journal of Operational Research 21, no. 2 (2014): 252. http://dx.doi.org/10.1504/ijor.2014.064551.

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44

Yong, Longquan, Sanyang Liu, Shouheng Tuo, and Kai Gao. "Improved Harmony Search Algorithm with Chaos for Absolute Value Equation." TELKOMNIKA (Telecommunication Computing Electronics and Control) 11, no. 4 (2013): 835. http://dx.doi.org/10.12928/telkomnika.v11i4.1208.

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45

Li, Xinyu, Kai Qin, Bing Zeng, Liang Gao, and Jiezhi Su. "Assembly sequence planning based on an improved harmony search algorithm." International Journal of Advanced Manufacturing Technology 84, no. 9-12 (2015): 2367–80. http://dx.doi.org/10.1007/s00170-015-7873-9.

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46

Wang, Lin, Huanling Hu, Rui Liu, and Xiaojian Zhou. "An improved differential harmony search algorithm for function optimization problems." Soft Computing 23, no. 13 (2018): 4827–52. http://dx.doi.org/10.1007/s00500-018-3139-4.

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47

Xiang, Wan-li, Mei-qing An, Yin-zhen Li, Rui-chun He, and Jing-fang Zhang. "An improved global-best harmony search algorithm for faster optimization." Expert Systems with Applications 41, no. 13 (2014): 5788–803. http://dx.doi.org/10.1016/j.eswa.2014.03.016.

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48

Yang, Yang, Ming Li, Chen Wang, and QingYue Wei. "Cylindricity Error Evaluation Based on an Improved Harmony Search Algorithm." Scientific Programming 2018 (July 19, 2018): 1–13. http://dx.doi.org/10.1155/2018/2483781.

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The cylindricity error is one of the basic form errors in mechanical parts, which greatly influences the assembly accuracy and service life of relevant parts. For the minimum zone method (MZM) in international standards, there is no specific formula to calculate the cylindricity error. Therefore, the evaluation methods of the cylindricity error under the MZM have been widely concerned by international scholars. To improve the evaluation accuracy and accelerate the iteration speed of the cylindricity, an improved harmony search (IHS) algorithm is proposed and applied to compute the cylindricity

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49

Yazdi, HA, H. Saberi, H. Saberi, and F. Hatami. "Designing optimal tuned mass dampers using improved harmony search algorithm." Advances in Structural Engineering 19, no. 10 (2016): 1620–36. http://dx.doi.org/10.1177/1369433216646018.

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50

Degertekin, S. O. "Improved harmony search algorithms for sizing optimization of truss structures." Computers & Structures 92-93 (February 2012): 229–41. http://dx.doi.org/10.1016/j.compstruc.2011.10.022.

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