Tree-Seed Algorithm (TSA) is a population-based heuristic search algorithm recently proposed to solve continuous optimization problems [1]. In TSA, trees and seeds represents the possible solution for the optimization problem. The tree population is called as the stand and the number of trees in a stand is a control parameter for TSA (known as population size in the swarm intelligence or evolutionary computation algorithms). There are two peculiar control parameters in TSA, whose names are search tendency-ST and number of seeds which will be produced for each tree-NS.

Two update equations are used in TSA and ST controls the selection of the update equation to produce seed for the tree. NS is used to determine the number of seeds which will be produced in TSA. NS and ST have been analyzed and 0.1 is suitable for ST and NS can be between 10% and 25% of the stand [1,2] but these parameters should be tuned by depending on the characteristics of the optimization problem.

The Working Diagram of TSA


(a) is the initialization of TSA. Trees are scattered to the search space and the fitness of the trees are calculated by using objective function specific for the optimization problem.



(b) is the seed production mechanism. The number of seeds for each tree is changeable. In the diagram, five seeds are produced for each tree and the best seeds are compared with the parent tree.


(c) is the replacement procedure. If fitness of the best seed is better than the fitness of its parent tree, the parent tree is removed from the stand and its best seed is located to stand.

The termination condition is maximum number of function evaluations (Max_FEs) for TSA. Because the number of seeds is changeable for each tree, Max_FEs should be used in TSA instead of iteration time.

Implementations of TSA

Matlab Implementation of TSA

Java Implementation of TSA

Future Directions for TSA

The TSA was proposed to solve unconstrained optimization problems. I suggest some ideas on TSA for the researchers:

  1. The performance of TSA on high dimensional problems can be improved by considering different update mechanisms.
  2. The binary versions of TSA can be developed for binary optimization.
  3. The discrete versions of TSA can be developed.
  4. The TSA can be modified for constrained optimization.
  5. The TSA can be applied to solve optimization problems in different field of research.
  6. Current control parameters can be analyzed or new control parameters can be added to TSA to improve its performance.
  7. Modelling versions of the TSA by considerind different approaches can be developed for solving different problems in engineering and related fields.

I, my PhD students and my colleagues also study TSA to solve constrained optimization and discrete optimization problems. For national or international collaboration, you can communicate with us via e-mails: Mustafa Servet Kıran (, Ahmet Cevahir Çınar (, Ahmet Babalık (


Studies on TSA


Journal Papers

[1] Kiran, Mustafa Servet. “TSA: Tree-seed algorithm for continuous optimization.” Expert Systems with Applications 42.19 (2015): 6686-6698..

[2]  Zheng, Yang, et al. “Design of a multi-mode intelligent model predictive control strategy for hydroelectric generating unit.” Neurocomputing 207 (2016): 287-299.

[3] Kıran, Mustafa Servet. “An implementation of tree-seed algorithm (TSA) for constrained optimization.” Intelligent and Evolutionary Systems. Springer, Cham, 2016. 189-197.

[4] Ding, Zhenghao, et al. “Structural damage identification based on modified artificial bee colony algorithm using modal data.” Inverse Problems in Science and Engineering 26.3 (2018): 422-442.

[5] Kıran, Mustafa Servet, and Ahmet Cevahir Çınar. “Ağaç-tohum algoritmasının CUDA destekli grafik işlem birimi üzerinde paralel uygulaması.” Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 33.4 (2018): 1397-1410.

[6] Babalik, Ahmet, Ahmet Cevahir Cinar, and Mustafa Servet Kiran. “A modification of tree-seed algorithm using Deb’s rules for constrained optimization.” Applied Soft Computing 63 (2018): 289-305.

[7] El-Fergany, Attia A., and Hany M. Hasanien. “Tree-seed algorithm for solving optimal power flow problem in large-scale power systems incorporating validations and comparisons.” Applied Soft Computing 64 (2018): 307-316.

[8] Cinar, Ahmet Cevahir, and Mustafa Servet Kiran. “Similarity and logic gate-based tree-seed algorithms for binary optimization.” Computers & Industrial Engineering 115 (2018): 631-646.

[9] Cinar, Ahmet Cevahir, Hazim Iscan, and Mustafa Servet Kiran. “Tree-Seed algorithm for large-scale binary optimization.” KnE Social Sciences (2018): 48-64.

[10] Muneeswaran, V., and M. Pallikonda Rajasekaran. “Gallbladder shape estimation using tree-seed optimization tuned radial basis function network for assessment of acute cholecystitis.” Intelligent Engineering Informatics. Springer, Singapore, 2018. 229-239.

[11] Zhou, Jianzhong, et al. “A heuristic TS fuzzy model for the pumped-storage generator-motor using variable-length tree-seed algorithm-based competitive agglomeration.” Energies 11.4 (2018): 944.

[12] Horng, Shih-Cheng, and Shieh-Shing Lin. “Embedding ordinal optimization into tree–seed algorithm for solving the probabilistic constrained simulation optimization problems.” Applied Sciences 8.11 (2018): 2153.

[13] GDU, Ibrahim AYDO, Serdar CARBAS, and Ahmed PAKSOY. “Investigation The Effect Of Greedy Selection Strategies On The Performance Of The Tree Seed Algorithm.” Mathematical Studies and Applications 2018 4-6 October 2018 (2018): 67.

[14] Beşkirli, Ayşe, Durmuş Özdemir, and Hasan Temurtaş. “A comparison of modified tree–seed algorithm for high-dimensional numerical functions.” Neural Computing and Applications (2019): 1-35.

[15] Ding, Zhenghao, et al. “Structural damage identification with uncertain modelling error and measurement noise by clustering based tree seeds algorithm.” Engineering Structures 185 (2019): 301-314.

[16] Jiang, Jianhua, et al. “EST-TSA: An effective search tendency based to tree seed algorithm.” Physica A: Statistical Mechanics and its Applications 534 (2019): 122323.

[17] Li, Luo, et al. “Metaheuristic FIR filter with game theory based compression technique-A reliable medical image compression technique for online applications.” Pattern Recognition Letters 125 (2019): 7-12.

[18] Sahman, Mehmet Akif, and Ahmet Cevahir Cinar. “Binary tree-seed algorithms with S-shaped and V-shaped transfer functions.” International Journal of Intelligent Systems and Applications in Engineering 7.2 (2019): 111-117.

[19] Gungor, Imral, et al. “Integration search strategies in tree seed algorithm for high dimensional function optimization.” International Journal of Machine Learning and Cybernetics (2019): 1-19.

[20] Chen, Weijie, et al. “Parameter Identification and State-of-Charge Estimation for Li-Ion Batteries Using an Improved Tree Seed Algorithm.” IEICE TRANSACTIONS on Information and Systems 102.8 (2019): 1489-1497.

[21] Ding, Zhenghao, Yilin Zhao, and Zhongrong Lu. “Simultaneous identification of structural stiffness and mass parameters based on Bare-bones Gaussian Tree Seeds Algorithm using time-domain data.” Applied Soft Computing 83 (2019): 105602.

[22] DURMUŞ, Burhanettin. “Kaotik Harita Temelli Ağaç Tohum Algoritması.” Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 23.2 (2019): 601-610.

[23] Lai, Chyh-Ming, and Tsung-Hua Wu. “Simplified swarm optimization with initialization scheme for dynamic weapon–target assignment problem.” Applied Soft Computing 82 (2019): 105542.

[24] Beşkirli, Mehmet. “Yüksek Boyutlu Test Fonksiyonlarında Ağaç Tohum Algoritmasının Performans Analizi.” Avrupa Bilim ve Teknoloji Dergisi: 93-101.

[25] Oliva, Diego, Mohamed Abd Elaziz, and Salvador Hinojosa. “Otsu’s Between Class Variance and the Tree Seed Algorithm.” Metaheuristic Algorithms for Image Segmentation: Theory and Applications. Springer, Cham, 2019. 71-83.

[26] Üney, Mehmet Şefik, and Nurettin Cetinkaya. “New Metaheuristic Algorithms for Reactive Power Optimization.” Tehnički vjesnik 26.5 (2019): 1427-1433.

[27] Cinar, Ahmet Cevahir, Sedat Korkmaz, and Mustafa Servet Kiran. “A discrete tree-seed algorithm for solving symmetric traveling salesman problem.” Engineering Science and Technology, an International Journal (2019).

[28] Ding, Zhenghao, et al. “Nonlinear hysteretic parameter identification using an improved tree-seed algorithm.” Swarm and Evolutionary Computation 46 (2019): 69-83.

[29] Jiang, Jianhua, et al. “STSA: A sine Tree-Seed Algorithm for complex continuous optimization problems.” Physica A: Statistical Mechanics and its Applications 537 (2020): 122802.


Proceeding Papers

[1] Kıran, Mustafa Servet. “An implementation of tree-seed algorithm (TSA) for constrained optimization.” Intelligent and Evolutionary Systems. Springer, Cham, 2016. 189-197.

[2] Muneeswaran, V., and M. Pallikonda Rajasekaran. “Performance evaluation of radial basis function networks based on tree seed algorithm.” 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT). IEEE, 2016.

[3]  Cinar, A. C., and M. S. Kiran. “A parallel version of tree-seed algorithm (TSA) within CUDA platform.” Selçuk international scientific conference on applied sciences. 2016.

[4] Kiran M.S., 2016, Withering Process for Tree-Seed Algorithm, The 8th International Conference on Advances in Information Technology, 19-22 December, Macau, China.

[5] Chen W.J., Tan X.J., Cai M., 2017, Parameter Identification of Equivalent Circuit Models for Li-ion Batteries based on Tree Seeds Algorithm, Internetional Conference on Sustainable Energy Engineering, IOP Conf. Series: Earth and Environmental Sciences (73),1-8.

[6] Muneeswaran, V., and M. Pallikonda Rajasekaran. “Beltrami-regularized denoising filter based on tree seed optimization algorithm: an ultrasound image application.” International Conference on Information and Communication Technology for Intelligent Systems. Springer, Cham, 2017.

[7] Çınar, Ahmet Cevahir, and Mustafa Servet Kıran. “Boundary conditions in Tree-Seed Algorithm: Analysis of the success of search space limitation techniques in Tree-Seed Algorithm.” 2017 International Conference on Computer Science and Engineering (UBMK). IEEE, 2017.

[8] Chen, Feng, et al. “A Feature Selection Approach for Network Intrusion Detection Based on Tree-Seed Algorithm and K-Nearest Neighbor.” 2018 IEEE 4th International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS). IEEE, 2018.

[9] Muneeswaran, V., and M. Pallikonda Rajasekaran. “Local contrast regularized contrast limited adaptive histogram equalization using tree seed algorithm—an aid for mammogram images enhancement.” Smart Intelligent Computing and Applications. Springer, Singapore, 2019. 693-701.

[10] Zhao, Sida, Ning Wang, and Xiu Liu. “Artificial bee colony algorithm with tree-seed searching for modeling multivariable systems using GRNN.” 2019 Chinese Control And Decision Conference (CCDC). IEEE, 2019.

[11] KAYA, E., et al. “Performance analysis of Galactic Swarm Optimization with Tree Seed Algorithm.”

[12] OZCAN, G., A. OZKIS, and MS KIRAN. “MOTSA: A Multi-Objective Tree-Seed Algorithm.”

[13] Sahman, M. A., et al. “Tree-seed algorithm in solving real-life optimization problems.” IOP Conference Series: Materials Science and Engineering. Vol. 675. No. 1. IOP Publishing, 2019.

[14] Ding, Zhenghao, Jun Li, and Hong Hao. “Structural Damage Detection with Uncertainties Using a Modified Tree Seeds Algorithm.” International Conference on Computational & Experimental Engineering and Sciences. Springer, Cham, 2019.

[15] Kiran, M.S., ” A literature review on Tree-Seed Algorithm, 8th International Conference on Advanced Technologies 1 (1), 100-104, Sarejevo, Bosnia, 2019

[16] Yunusova P., Kiran M.S., “Tree-Seed Programming for Symbolic Regression”, 2nd International Conference on Advanced Technologies, Computer Engineering and Science, pp. 300-3003, Antalya, Turkiye, 2019


Master or PhD Dissertations

[1] Çınar, A. C. (2016). A Cuda-based Parallel Programming Approach to Tree-Seed Algorithm. (MSc Thesis), MSc Thesis in Turkish, Graduate School of Natural Sciences, Selcuk University.

[2] Yunusova P. (2019), Tree-Seed Programming for Estimation of Turkey Electricity Demand, (MSc Thesis), MSc Thesis in Turkish, Graduate School of Natural Sciences,  Selcuk University.

[3] Özcan G. (2019), MOTSA: Multiobjective Variant of Tree-Seed Algorithm, (MSc Thesis), MSc Thesis in Turkish, Graduate School of Natural Sciences, Konya Technical University.