KnE Social Sciences

ISSN: 2518-668X

The latest conference proceedings on humanities, arts and social sciences.

Tree-Seed Algorithm for Large-Scale Binary Optimization

Published date: Jan 15 2018

Journal Title: KnE Social Sciences

Issue title: The 9th International Conference on Advances in Information Technology (IAIT-2017)

Pages: 48-64

DOI: 10.18502/kss.v3i1.1396

Authors:

Ahmet Cevahir Cinarahmetcevahircinar@gmail.comDept. of Computer Engineering, Faculty of Engineering, Konya

Hazim IscanDept. of Computer Engineering, Faculty of Engineering, Konya

Mustafa Servet KiranDept. of Computer Engineering, Faculty of Engineering, Konya

Abstract:

Population-based swarm or evolutionary computation algorithms in optimization are attracted the interest of the researchers due their simple structure, optimization performance, easy-adaptation. Binary optimization problems can be also solved by using these algorithms. This paper focuses on solving large scale binary optimization problems by using Tree-Seed Algorithm (TSA) proposed for solving continuous optimization problems by imitating relationship between the trees and their seeds in nature. The basic TSA is modified by using xor logic gate for solving binary optimization problems in this study. In order to investigate the performance of the proposed algorithm, the numeric benchmark problems with the different dimensions are considered and obtained results show that the proposed algorithm produces effective and comparable solutions in terms of solution quality.

Keywords: binary optimization, tree-seed algorithm, xor-gate, large-scale optimization
References:

[1] M. S. Kiran, ”TSA: Tree-seed algorithm for continuous optimization,” Expert Systems with Applications, vol. 42, pp. 6686-6698, 2015.


[2] A. Banitalebi, M. I. A. Aziz, and Z. A. Aziz, ”A self-adaptive binary differential evolution algorithm for large scale binary optimization problems,” Information Sciences, vol. 367, pp. 487-511, 2016.


[3] Z. Beheshti, S. M. Shamsuddin, and S. Hasan, ”Memetic binary particle swarm optimization for discrete optimization problems,” Information Sciences, vol. 299, pp. 58-84, 2015.


[4] H. Nezamabadi-pour, M. Rostami-Shahrbabaki, and M. Maghfoori-Farsangi, ”Binary particle swarm optimization: challenges and new solutions,” CSI J Comput Sci Eng, vol. 6, pp. 21-32, 2008.


[5] Y. Shi and R. Eberhart, ”A modified particle swarm optimizer,” in Evolutionary Com- putation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on, 1998, pp. 69-73.


[6] J. Kennedy and R. C. Eberhart, ”A discrete binary version of the particle swarm algorithm,” in Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on, 1997, pp. 4104-4108.


[7] G. Pampara, A. P. Engelbrecht, and N. Franken, ”Binary differential evolution,” in Evolutionary Computation, 2006. CEC 2006. IEEE Congress on, 2006, pp. 1873-1879.


[8] H. Nezamabadi-pour, ”A quantum-inspired gravitational search algorithm for binary encoded optimization problems,” Engineering Applications of Artificial Intelligence, vol. 40, pp. 62-75, 2015.


[9] C. Ozturk, E. Hancer, and D. Karaboga, ”A novel binary artificial bee colony algorithm based on genetic operators,” Information Sciences, vol. 297, pp. 154-170, 2015.


[10] C. Deng, B. Zhao, Y. Yang, H. Peng, and Q. Wei, ”Novel binary encoding differential evolution algorithm,” Advances in Swarm Intelligence, pp. 416-423, 2011.


[11] M. H. Kashan, A. H. Kashan, and N. Nahavandi, ”A novel differential evolution algorithm for binary optimization,” Computational Optimization and Applications, vol. 55, p. 481, 2013.


[12] M. H. Kashan, N. Nahavandi, and A. H. Kashan, ”DisABC: a new artificial bee colony algorithm for binary optimization,” Applied Soft Computing, vol. 12, pp. 342-352, 2012.


[13] P. Jaccard, ”Étude comparative de la distribution florale dans une portion des Alpes et des Jura,” Bull Soc Vaudoise Sci Nat, vol. 37, pp. 547-579, 1901.


[14] M. S. Kiran and M. GÜNDÜZ„ ”XOR-based artificial bee colony algorithm for binary optimization,” Turkish Journal of Electrical Engineering & Computer Sciences, vol. 21, pp. 2307-2328, 2013.


[15] M. Locatelli, ”A Note on the Griewank Test Function,” Journal of Global Optimization, vol. 25, pp. 169 174, February 01 2003Van der Geer J, Hanraads JAJ, Lupton RA. The art of writing a scientific article. J Sci Commun 2000;163:51-59.

Download
HTML
Cite
Share
statistics

443 Abstract Views

242 PDF Downloads