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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  2/2020 - 8

Performance Analysis of Tree Seed Algorithm for Small Dimension Optimization Functions

BESKIRLI, M. See more information about BESKIRLI, M. on SCOPUS See more information about BESKIRLI, M. on IEEExplore See more information about BESKIRLI, M. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,365 KB) | Citation | Downloads: 181 | Views: 494

Author keywords
benchmark testing, algorithms, optimization, heuristic algorithms, optimization methods

References keywords
algorithm(22), optimization(17), tree(13), seed(13), systems(9), problems(5), improved(5), kiran(4), constrained(4), chen(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2020-05-31
Volume 20, Issue 2, Year 2020, On page(s): 65 - 72
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.02008
Web of Science Accession Number: 000537943500008
SCOPUS ID: 85087439628

Abstract
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Tree-Seed Algorithm (TSA) simulates the growth of trees and seeds on a land. TSA is a method proposed to solve continuous optimization problems. Trees and seeds indicate possible solutions in the search space for optimization problems. Trees are planted in the ground at the beginning of the search and each tree produces several seeds during iterations. While the trees were selected randomly during seed formation, the tournament selection method was used and also hybridized by adding the C parameter, which is the acceleration coefficient calculated according to the size of the problem. In this study, continuous optimization problem has been solved by the hybrid method. First, the performance analyses of the five best known numerical benchmark functions have been done, in both TSA and hybrid method TSA with 2, 3, 4 and 5 dimensions, and 10-50 population numbers. After that, well-known algorithms in the literature like Particle Swarm Optimization (PSO), TSA, Artificial Bee Colony (ABC), Harmony Search (HS), as well as hybrid method TSA (HTSA) have been applied to twenty-four numerical benchmark functions and the performance analyses of algorithms have been done. Hopeful and comparable conclusions based on solution quality and robustness can be obtained with the hybrid method.


References | Cited By  «-- Click to see who has cited this paper

[1] A. Beskirli, M. Beskirli, H. Hakli and H. Uguz, "Comparing Energy Demand Estimation Using Artificial Algae Algorithm: The Case of Turkey," Journal of Clean Energy Technologies, vol. 6, no. 4, pp. 349-352, 2018,
[CrossRef]


[2] J. Kennedy and R. Eberhart, "Particle swarm optimization (PSO)," in Proc. IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942-1948, 1995,
[CrossRef] [Web of Science Times Cited 26465]


[3] M. Dorigo, V. Maniezzo, and A. Colorni, "Ant system: optimization by a colony of cooperating agents," IEEE Transactions on Systems, man, and cybernetics, Part B: Cybernetics, vol. 26, no. 1, pp. 29-41, 1996.
[CrossRef] [Web of Science Times Cited 6129] [SCOPUS Times Cited 8252]


[4] D. Karaboga and B. Basturk, "Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems," in International fuzzy systems association world congress, 2007, pp. 789-798: Springer.
[CrossRef] [Web of Science Times Cited 575] [SCOPUS Times Cited 794]


[5] Z. W. Geem, J. H. Kim, and G. V. Loganathan, "A new heuristic optimization algorithm: harmony search," Simulation, vol. 76, no. 2, pp. 60-68, 2001,
[CrossRef] [Web of Science Times Cited 3051] [SCOPUS Times Cited 3902]


[6] E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, "GSA: a gravitational search algorithm," Information sciences, vol. 179, no. 13, pp. 2232-2248, 2009.
[CrossRef] [Web of Science Times Cited 2748] [SCOPUS Times Cited 3474]


[7] S. Zhao, L. Gao, J. Tu, and D. Yu, " Zhao, S., Gao, L., Tu, J., and Yu, D. (2020). A Novel Modified Tree-Seed Algorithm for High-Dimensional Optimization Problems," Chinese Journal of Electronics, vol. 29, no. 2, pp. 337-343, 2020.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 2]


[8] M. S. Kiran, "TSA: Tree-seed algorithm for continuous optimization," Expert Systems with Applications, vol. 42, no. 19, pp. 6686-6698, 2015.
[CrossRef] [Web of Science Times Cited 90] [SCOPUS Times Cited 106]


[9] S. Gupta and K. Deep, "Improved sine cosine algorithm with crossover scheme for global optimization," Knowledge-Based Systems, vol. 165, pp. 374-406, 2019.
[CrossRef] [Web of Science Times Cited 42] [SCOPUS Times Cited 48]


[10] S. Gupta, K. Deep, A. A. Heidari, H. Moayedi, and H. Chen, "Harmonized salp chain-built optimization," Engineering with Computers, 2019/10/16 2019.
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 22]


[11] S. Gupta and K. Deep, "A hybrid self-adaptive sine cosine algorithm with opposition based learning," Expert Systems with Applications, vol. 119, pp. 210-230, 2019.
[CrossRef] [Web of Science Times Cited 53] [SCOPUS Times Cited 61]


[12] W. Chen, M. Cai, X. Tan, and B. Wei, " Chen, W., Cai, M., Tan, X., & Wei, B. (2019). Parameter Identification and State-of-Charge Estimation for Li-Ion Batteries Using an Improved Tree Seed Algorithm," IEICE TRANSACTIONS on Information and Systems, vol. 102, no. 8, pp. 1489-1497, 2019,
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 4]


[13] X. Zhang and W. Zhu, " Disruption Management for Vehicle Routing Problem Based on Consumer Value and Improved Tree-Seed Algorithm," IEEE Access, vol. 7, pp. 122019-122027, 2019,
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 3]


[14] M. S. Kiran, "An implementation of tree-seed algorithm (TSA) for constrained optimization," in Intelligent and Evolutionary Systems: Springer, 2016, pp. 189-197.
[CrossRef] [Web of Science Times Cited 15]


[15] P. S. Rao, D. Vasumathi and K. Suresh, " The Adaptive Strategies Improving Web Personalization Using the Tree Seed Algorithm," In Cognitive Science and Artificial Intelligence, pp. 23-29, 2019.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 1]


[16] J. Zhou, Y. Zheng, Y. Xu, H. Liu, and D. Chen, "A heuristic TS fuzzy model for the pumped-storage generator-motor using variable-length tree-seed algorithm-based competitive agglomeration," Energies, vol. 11, no. 4, p. 944, 2018.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 8]


[17] M. Aslan, M. Beskirli, H. Kodaz, and M. S. Kiran, "An improved tree seed algorithm for optimization problems," Int J Mach Learn Comput, vol. 8, no. 1, pp. 20-25, 2018.
[CrossRef] [SCOPUS Times Cited 11]


[18] A. Babalik, A. C. Cinar, and M. S. Kiran, "A modification of tree-seed algorithm using Deb's rules for constrained optimization," Applied Soft Computing, vol. 63, pp. 289-305, 2018.
[CrossRef] [Web of Science Times Cited 26] [SCOPUS Times Cited 28]


[19] V. Muneeswaran and M. P. Rajasekaran, "Beltrami-regularized denoising filter based on tree seed optimization algorithm: an ultrasound image application," in International Conference on Information and Communication Technology for Intelligent Systems, 2017, pp. 449-457: Springer.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 5]


[20] A. A. El-Fergany and H. M. Hasanien, "Tree-seed algorithm for solving optimal power flow problem in large-scale power systems incorporating validations and comparisons," Applied Soft Computing, vol. 64, pp. 307-316, 2018.
[CrossRef] [Web of Science Times Cited 29] [SCOPUS Times Cited 36]


[21] A. Beskirli, D. Ozdemir, and H. Temurtas, "A comparison of modified tree-seed algorithm for high-dimensional numerical functions," Neural Computing and Applications, pp. 1-35, 2019.
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 3]


[22] H. Kilic and U. Yuzgec, "Tournament selection based antlion optimization algorithm for solving quadratic assignment problem," Engineering Science and Technology, vol. 22, no. 2, pp. 673-691, 2019.
[CrossRef] [Web of Science Times Cited 12] [SCOPUS Times Cited 15]


[23] H. Kilic and U. Yuzgec, "Improved antlion optimization algorithm via tournament selection," in 2017 9th International Conference on Computational Intelligence and Communication Networks (CICN), 2017, pp. 200-205: IEEE.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 13]


[24] J. Jiang, M. Xu, X. Meng, and K. Li, " STSA: A sine Tree-Seed Algorithm for complex continuous optimization problems," Physica A: Statistical Mechanics and its Applications, vol. 537, no. 1, 2020.
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[25] X. Wang and X. Yan, "Global best harmony search algorithm with control parameters co-evolution based on PSO and its application to constrained optimal problems," Applied Mathematics and Computation, vol. 219, no. 19, pp. 10059-10072, 2013.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 11]


[26] X. Hu, "Particle swarm optimization tutorial," swarmintelligence. [Online] Available: Temporary on-line reference link removed - see the PDF document (cit. on p. 3 3), 2007.



References Weight

Web of Science® Citations for all references: 39,299 TCR
SCOPUS® Citations for all references: 16,809 TCR

Web of Science® Average Citations per reference: 1,456 ACR
SCOPUS® Average Citations per reference: 623 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-03-05 07:34 in 154 seconds.




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