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Journals / Turkish Journal of Electrical Engineering and Computer Science / 2013 / Cilt: 21 - Sayı: Sup.2

XOR-based artificial bee colony algorithm for binary optimization

Mustafa Servet KIRAN
Mesut GÜNDÜZ
Pages
2307–2328
Publication Date
2012-12-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The experimental results show that binABC is an alternative tool for solving binary optimization problems and is a competitive algorithm when compared with BPSO, DisABC, and IBPSO in terms of solution quality, robustness, and simplicity.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The artificial bee colony (ABC) algorithm, which was inspired by the foraging and dance behaviors of real honey bee colonies, was first introduced for solving numerical optimization problems.
📊
Key Findings & Data:When the solution space of the optimization problem is binary-structured, the basic ABC algorithm should be modified for solving this class of problems.
💡
Conclusion & Impact:The experimental results show that binABC is an alternative tool for solving binary optimization problems and is a competitive algorithm when compared with BPSO, DisABC, and IBPSO in terms of solution quality, robustness, and simplicity.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Swarm intelligence · artificial bee colony · binary optimization

Abstract

The artificial bee colony (ABC) algorithm, which was inspired by the foraging and dance behaviors of real honey bee colonies, was first introduced for solving numerical optimization problems. When the solution space of the optimization problem is binary-structured, the basic ABC algorithm should be modified for solving this class of problems. In this study, we propose XOR-based modification for the solution-updating equation of the ABC algorithm in order to solve binary optimization problems. The proposed method, named binary ABC (binABC), is examined on an uncapacitated facility location problem, which is a pure binary optimization problem, and the results obtained by the binABC are compared with results obtained by binary particle swarm optimization (BPSO), the discrete ABC (DisABC) algorithm, and improved BPSO (IBPSO). The experimental results show that binABC is an alternative tool for solving binary optimization problems and is a competitive algorithm when compared with BPSO, DisABC, and IBPSO in terms of solution quality, robustness, and simplicity.

Özet

The artificial bee colony (ABC) algorithm, which was inspired by the foraging and dance behaviors of real honey bee colonies, was first introduced for solving numerical optimization problems. When the solution space of the optimization problem is binary-structured, the basic ABC algorithm should be modified for solving this class of problems. In this study, we propose XOR-based modification for the solution-updating equation of the ABC algorithm in order to solve binary optimization problems. The proposed method, named binary ABC (binABC), is examined on an uncapacitated facility location problem, which is a pure binary optimization problem, and the results obtained by the binABC are compared with results obtained by binary particle swarm optimization (BPSO), the discrete ABC (DisABC) algorithm, and improved BPSO (IBPSO). The experimental results show that binABC is an alternative tool for solving binary optimization problems and is a competitive algorithm when compared with BPSO, DisABC, and IBPSO in terms of solution quality, robustness, and simplicity.

🔖

Cite This Article

KIRAN, M. S., GÜNDÜZ, M. (2012). XOR-based artificial bee colony algorithm for binary optimization. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 21 - Sayı: Sup.2, 2307–2328. https://ajindex.com/articles/188854

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References (13)

  1. D. Karaboga, “An idea based on honey bee swarm for numerical optimization”, Technical Report, Computer Engineering Department, Engineering Faculty, Erciyes University, available at http://mf.erciyes.edu.tr/abc/pub/tr06 2005.pdf, 2005.
  2. D. Karaboga, B. Basturk, “A powerful and efficient algorithm for numerical function optimization”, Journal of Global Optimization, Vol. 39, pp. 459–471, 2007.
  3. D. Karaboga, B. Basturk, “Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems”, Proceedings of the 12th International Fuzzy Systems Association World Congress on Foundations of Fuzzy Logic and Soft Computing, pp. 789–798, 2007.
  4. D. Karaboga, B. Akay, “A modified artificial bee colony (ABC) algorithm for constrained optimization problems”, Applied Soft Computing, Vol. 11, pp. 3021–3031, 2011.
  5. D. Karaboga, B. Basturk, “On the performance of artificial bee colony (ABC) algorithm”, Applied Soft Computing, Vol. 8, pp. 687–697, 2008.
  6. G. Zhu, S. Kwong, “Gbest-guided artificial bee colony algorithm for numerical function algorithm”, Applied Mathematics and Computation, Vol. 217, pp. 3166–3173, 2010. B. Alatas, “Chaotic bee colony algorithms for global numerical optimization”, Expert Systems with Applications, Vol. 37, pp. 5682–5687, 2010.
  7. M.H. Kashan, N. Nahavandi, A.H. Kashan, “DisABC: a new artificial bee colony algorithm for binary optimization”, Applied Soft Computing, Vol. 12, pp. 342–352, 2011.
  8. D. Karaboga, B. Gorkemli, “A combinatorial artificial bee colony algorithm for traveling salesman problem”, International Symposium on Innovations in Intelligent Systems and Applications, pp. 50–53, 2011.
  9. M.S. Kıran, H. ˙I¸scan, M. G¨ und¨ uz, “The analysis of discrete artificial bee colony algorithm with neighborhood operator on traveling salesman problem”, Neural Computing and Applications, Vol. 23, pp. 9–21, 2012.
  10. M.S. Kıran, M. G¨ und¨ uz, “A novel artificial bee colony-based algorithm for solving the numerical optimization problems”, International Journal of Innovative Computing, Information & Control, Vol. 8, pp. 6107–6122, 2012.
  11. B. Akay, D. Karaboga, “A modified artificial bee colony algorithm for real parameter optimization”, Information Sciences, Vol. 192, pp. 120–142, 2012.
  12. D.J. Mala, V. Mohan, M. Kamalapriya, “Automated software test suite optimisation framework – an artificial bee colony optimisation-based approach”, IET Software, Vol. 4, pp. 334–348, 2010.
  13. D. Karaboga, C. Ozturk, “Neural networks training by artificial bee co