Increased Efficiency of the Artificial Bee Colony Algorithm Using the Pheromone Technique

Authors

  • A. H. Alaidi Programming Department, Computer Science and Information Technology College, Wasit University, Iraq
  • C. Soong Der College of Computing and Informatics, Universiti Tenaga Nasional, Malaysia
  • Y. Weng Leong College of Computing and Informatics, Universiti Tenaga Nasional, Malaysia
Volume: 12 | Issue: 6 | Pages: 9732-9736 | December 2022 | https://doi.org/10.48084/etasr.5305

Abstract

Artificial Bee Colony (ABC) is a powerful metaheuristic algorithm inspired by the behavior of a honey bee swarm. ABC suffers from poor exploitation and, in some cases, poor exploration. Ant Colony Optimization (ACO) is another metaheuristic algorithm that uses pheromones as a guide for an ant to find its way. This study used a pheromone technique from ACO on ABC to enhance its exploration and exploitation. The performance of the proposed method was verified through twenty instances from TSPLIB. The results were compared with the original ABC method and showed that the proposed method leverages the performance of ABC.

Keywords:

artificial bee colony, pheromone, ant colony optimization

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References

H. Wei, J. Ji, Y. Qin, Y. Wang, and C. Liu, "A Novel Artificial Bee Colony Algorithm Based on Attraction Pheromone for the Multidimensional Knapsack Problems," in Artificial Intelligence and Computational Intelligence, Taiyuan, China, 2011, pp. 1–10. DOI: https://doi.org/10.1007/978-3-642-23887-1_1

J. Ji, H. Wei, C. Liu, and B. Yin, "Artificial Bee Colony Algorithm Merged with Pheromone Communication Mechanism for the 0-1 Multidimensional Knapsack Problem," Mathematical Problems in Engineering, vol. 2013, Jul. 2013, Art. no. e676275. DOI: https://doi.org/10.1155/2013/676275

J. M. Moosa, R. Shakur, M. Kaykobad, and M. S. Rahman, "Gene selection for cancer classification with the help of bees," BMC Medical Genomics, vol. 9, no. 2, Aug. 2016, Art. no. 47. DOI: https://doi.org/10.1186/s12920-016-0204-7

A. H. Alaidi, C. S. Soong Der, and Y. Weng Leong, "Systematic Review of Enhancement of Artificial Bee Colony Algorithm Using Ant Colony Pheromone," International Journal of Interactive Mobile Technologies, vol. 15, no. 16, Aug. 2021, Art. no. 172. DOI: https://doi.org/10.3991/ijim.v15i16.24171

Ι. Marouani, A. Boudjemline, T. Guesmi, and H. H. Abdallah, "A Modified Artificial Bee Colony for the Non-Smooth Dynamic Economic/Environmental Dispatch," Engineering, Technology & Applied Science Research, vol. 8, no. 5, pp. 3321–3328, Oct. 2018. DOI: https://doi.org/10.48084/etasr.2098

S. D. Chavan and A. V. Kulkarni, "Event Based Clustering Localized Energy Efficient Ant Colony Optimization (EBC_LEE-ACO) for Performance Enhancement of Wireless Sensor Network," Engineering, Technology & Applied Science Research, vol. 8, no. 4, pp. 3177–3183, Aug. 2018. DOI: https://doi.org/10.48084/etasr.2121

H. Ehteshami, S. Javadi, and S. M. Shariatmadar, "Improving the Power Quality in Tehran Metro Line-Two Using the Ant Colony Algorithm," Engineering, Technology & Applied Science Research, vol. 7, no. 6, pp. 2256–2259, Dec. 2017. DOI: https://doi.org/10.48084/etasr.1551

N. Rahnema and F. S. Gharehchopogh, "An improved artificial bee colony algorithm based on whale optimization algorithm for data clustering," Multimedia Tools and Applications, vol. 79, no. 43–44, pp. 32169–32194, Aug. 2020. DOI: https://doi.org/10.1007/s11042-020-09639-2

D. Lei and H. Yang, "Scheduling unrelated parallel machines with preventive maintenance and setup time: Multi-sub-colony artificial bee colony," Applied Soft Computing, vol. 125, Aug. 2022, Art. no. 109154. DOI: https://doi.org/10.1016/j.asoc.2022.109154

Z. Han, M. Chen, S. Shao, and Q. Wu, "Improved artificial bee colony algorithm-based path planning of unmanned autonomous helicopter using multi-strategy evolutionary learning," Aerospace Science and Technology, vol. 122, Mar. 2022, Art. no. 107374. DOI: https://doi.org/10.1016/j.ast.2022.107374

D. Karaboga and B. Basturk, "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm," Journal of Global Optimization, vol. 39, no. 3, pp. 459–471, Nov. 2007. DOI: https://doi.org/10.1007/s10898-007-9149-x

S. ArunKumar, B. V. Kumar, and M. Pandi, "Artificial bee colony optimization based energy-efficient wireless network interface selection for industrial mobile devices," Computer Communications, vol. 154, pp. 1–10, Mar. 2020. DOI: https://doi.org/10.1016/j.comcom.2020.01.067

H. Gao, Z. Fu, C.-M. Pun, J. Zhang, and S. Kwong, "An Efficient Artificial Bee Colony Algorithm With an Improved Linkage Identification Method," IEEE Transactions on Cybernetics, vol. 52, no. 6, pp. 4400–4414, Jun. 2022. DOI: https://doi.org/10.1109/TCYB.2020.3026716

C. Twomey, T. Stützle, M. Dorigo, M. Manfrin, and M. Birattari, "An analysis of communication policies for homogeneous multi-colony ACO algorithms," Information Sciences, vol. 180, no. 12, pp. 2390–2404, Jun. 2010. DOI: https://doi.org/10.1016/j.ins.2010.02.017

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, Oct. 1996. DOI: https://doi.org/10.1109/3477.484436

S. S. Choong, L.-P. Wong, and C. P. Lim, "An artificial bee colony algorithm with a Modified Choice Function for the traveling salesman problem," Swarm and Evolutionary Computation, vol. 44, pp. 622–635, Feb. 2019. DOI: https://doi.org/10.1016/j.swevo.2018.08.004

M. S. Kıran, H. İşcan, and M. Gündüz, "The analysis of discrete artificial bee colony algorithm with neighborhood operator on traveling salesman problem," Neural Computing and Applications, vol. 23, no. 1, pp. 9–21, Jul. 2013. DOI: https://doi.org/10.1007/s00521-011-0794-0

G. Reinelt, "TSPLIB—A Traveling Salesman Problem Library," ORSA Journal on Computing, vol. 3, no. 4, pp. 376–384, Nov. 1991. DOI: https://doi.org/10.1287/ijoc.3.4.376

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[1]
A. H. Alaidi, C. Soong Der, and Y. Weng Leong, “Increased Efficiency of the Artificial Bee Colony Algorithm Using the Pheromone Technique”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 6, pp. 9732–9736, Dec. 2022.

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