Three Binary Versions of Tree-Seed Algorithm for Binary Optimization

Three Binary Versions of Tree-Seed Algorithm for Binary Optimization

  IJETT-book-cover           
  
© 2024 by IJETT Journal
Volume-72 Issue-4
Year of Publication : 2024
Author : Yahye Abukar Ahmed, Mustafa Servet Kiran, Mohamed Omar Abdullahi, Abdukadir Dahir Jimale, Abdulaziz Yasin Nageye, Ali Abdi Jama
DOI : 10.14445/22315381/IJETT-V72I4P132

How to Cite?

Yahye Abukar Ahmed, Mustafa Servet Kiran, Mohamed Omar Abdullahi, Abdukadir Dahir Jimale, Abdulaziz Yasin Nageye, Ali Abdi Jama, "Three Binary Versions of Tree-Seed Algorithm for Binary Optimization," International Journal of Engineering Trends and Technology, vol. 72, no. 4, pp. 315-323, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I4P132

Abstract
This study explores the adaptation of the Tree-Seed Algorithm (TSA), a population-based optimization method, for solving binary optimization problems. Initially designed for problems featuring continuous solution spaces, TSA is adjusted to address binary-structured optimization challenges. Three distinct approaches, namely the sigmoid function, modulo function, and xor logic gate, are employed to address TSA for binary optimization problem-solving. The efficacy of these methods is evaluated through experimentation on Uncapacitated Facility Location Problems (UFLPs), representative pure binary problems from existing literature. A comprehensive analysis is conducted using a selection of well-known small, medium, and large-sized UFLP instances to assess the performances and the impact of TSA's control parameters. Comparative analysis of obtained results reveals promising outcomes achieved by the proposed algorithm.

Keywords
Sigmoid function, UFLP, Modulo function, Binary optimization, Tree-seed algorithm, x-or logic gate.

References
[1] J. Kennedy, and R. Eberhart, “Particle Swarm Optimization,” Proceedings of ICNN'95 - International Conference on Neural Networks, Perth, WA, Australia, pp. 1942-1948, 1995.
[CrossRef] [Google Scholar] [Publisher Link]
[2] D. Karaboga, and B. Basturk, “On the Performance of Artificial Bee Colony (ABC) Algorithm,” Applied Soft Computing Journal, vol. 8, no. 1, pp. 687-697, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Dervis Karaboga, and Bahriye Basturk, “A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm,” Journal of Global Optimization, vol. 39, pp. 459-471, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Xin-She Yang, “Firefly Algorithm, Stochastic Test Functions and Design Optimization,” International Journal of Bio-Inspired Computation, vol. 2, no. 2, pp. 78-84, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Xin-She Yang, “A New Metaheuristic Bat-Inspired Algorithm,” Nature Inspired Cooperative Strategies for Optimization, pp. 65-74, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[6] J. Kennedy, and R.C. Eberhart, “A Discrete Binary Version of the Particle Swarm Algorithm,” 1997 IEEE International Conference on Systems, Man, and Cybernetics, Computational Cybernetics and Simulation, Orlando, FL, USA, pp. 4104-4108, 1997.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Mehmet Sevkli, and Ali R. Guner, “A Continuous Particle Swarm Optimization Algorithm for Uncapacitated Facility Location Problem,” International Workshop on Ant Colony Optimization and Swarm Intelligence, pp. 316-323, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Nevena Lazic, Brendan J. Frey, and Parham Aarabi, “Solving the Uncapacitated Facility Location Problem Using Message Passing Algorithms,” Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 429-436, 2010.
[Google Scholar] [Publisher Link]
[9] Hazem Ahmed, and Janice Glasgow, “Swarm Intelligence: Concepts, Models and Applications,” School of Computing, Queens University, Technical Report, pp. 1-51, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Frederick Ducatelle, Gianni A. Di Caro, and Luca M. Gambardella, “Principles and Applications of Swarm Intelligence for Adaptive Routing in Telecommunications Networks,” Swarm Intelligence, vol. 4, no. 3, pp. 173-198, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[11] M. Dorigo, V. Maniezzo, and A. Colorni, “The 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] [Google Scholar] [Publisher Link]
[12] Arnab Kole, Parichay Chakrabarti, and Somnath Bhattacharyya, “An Ant Colony Optimization Algorithm for Uncapacitated Facility Location Problem,” Artificial Intelligence and Applications, vol. 1, no. 1, pp. 55-61, 2014.
[Google Scholar]
[13] Yusuke Watanabe, Mayumi Takaya, and Akihiro Yamamura, “Fitness Function in ABC Algorithm for Uncapacitated Facility Location Problem,” 3rd International Conference on Information and Communication Technology-EurAsia (ICT-EURASIA) and 9th International Conference on Research and Practical Issues of Enterprise Information Systems (CONFENIS), Daejeon, Korea, pp. 129-138, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Mauricio G.C. Resende, and Renato F. Werneck, “A Hybrid Multistart Heuristic for the Uncapacitated Facility Location Problem,” European Journal of Operational Research, vol. 174, no. 1, pp. 54-68, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[15] J.E. Beasley, “OR-Library: Distributing Test Problems by Electronic Mail,” Journal of the Operational Research Society, vol. 41, no. 11, pp. 1069-1072, 1990.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Ahamet Cevahir Çınar, “A Cuda-based Parallel Programming Approach to Tree-Seed Algorithm,” MSc Thesis, Selçuk University, pp. 1- 111, 2016.
[Google Scholar] [Publisher Link]