DOI QR코드

DOI QR Code

Enhanced Hybrid XOR-based Artificial Bee Colony Using PSO Algorithm for Energy Efficient Binary Optimization

  • Baguda, Yakubu S. (Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University)
  • 투고 : 2021.11.05
  • 발행 : 2021.11.30

초록

Increase in computational cost and exhaustive search can lead to more complexity and computational energy. Thus, there is need for effective and efficient scheme to reduce the complexity to achieve optimal energy utilization. This will improve the energy efficiency and enhance the proficiency in terms of the resources needed to achieve convergence. This paper primarily focuses on the development of hybrid swarm intelligence scheme for reducing the computational complexity in binary optimization. In order to reduce the complexity, both artificial bee colony (ABC) and particle swarm optimization (PSO) have been employed to effectively minimize the exhaustive search and increase convergence. First, a new approach using ABC and PSO has been proposed and developed to solve the binary optimization problem. Second, the scout for good quality food sources is accomplished through the deployment of PSO in order to optimally search and explore the best source. Extensive experimental simulations conducted have demonstrate that the proposed scheme outperforms the ABC approaches for reducing complexity and energy consumption in terms of convergence, search and error minimization performance measures.

키워드

과제정보

The author would like to thank all those who contributed toward making this research successful. Also, we would like to thanks the reviewers for their insightful and valuable comment. This work was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant no. DF-802-830-1441. The author, therefore, gratefully acknowledge the DSR technical and financial support.

참고문헌

  1. M. Jain, V. Singh, and A. Rani, "A novel nature-inspired algorithm for optimization: Squirrel search algorithm," Swarm Evol. Comput., vol. 44, pp. 148-175, 2019. https://doi.org/10.1016/j.swevo.2018.02.013
  2. T. Milan and N. Bacanin, "Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems," Neurocomputing, vol. 143, pp. 197-207, 2014. https://doi.org/10.1016/j.neucom.2014.06.006
  3. A. Banharnsakun, T. Achalakul, and B. Sirinaovakul, "The best-so-far selection in artificial bee colony algorithm," Applied Soft Computing, 11(2): 2888 - 2901, 2011. https://doi.org/10.1016/j.asoc.2010.11.025
  4. X. H. Yan, et al., "A novel hybrid artificial bee colony algorithm with crossover operator for numerical optimization," Natural Computing, 14(1): 169 - 184, 2015. https://doi.org/10.1007/s11047-013-9405-6
  5. S. Y. Liu, P. Zhang, and M. M. Zhu, "Artificial bee colony algorithm based on local search," Control and Decision, 29(1): 123 - 128, 2014.
  6. X. Y. Zhou, et al., "Neighborhood search based artificial bee colony algorithm," Journal of Central South University, 46(2): 534 - 546, 2015.
  7. D. Karaboga, "An idea based on honey bee swarm for numerical Turkey, Tech. Rep. TR06, 2005.
  8. D. Karaboga and B. Basturk, "A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm," J. Global Optim., vol. 39, no. 3, pp. 459-471, 2007. https://doi.org/10.1007/s10898-007-9149-x
  9. D. Karaboga and B. Basturk, "On the performance of artificial bee colony (ABC) algorithm," Appl. Soft Comput., vol. 8, no. 1, pp. 687-697, 2008. https://doi.org/10.1016/j.asoc.2007.05.007
  10. C. Q. Zhang, J. G. Zhang, and X. Wang, "Overview of research on bee colony algorithms," (in Chinese), Appl. Res. Comput., vol. 28, no. 9, 2011.
  11. X. J. Bi and W. Y. J, "Improved artificial bee colony algorithm," (in Chinese), J. Harbin Univ. Eng., vol. 33, no. 1, pp. 117-123, 2012.
  12. X.-P. Liu, S.-B. Xuan, and F. Liu, "Artificial bee colony algorithm with good point set and turn process of monkey algorithm," (in Chinese), Pattern Recognit. Artif. Intell., vol. 7, no. 2, pp. 80-89, 2015.
  13. J. Qiu, M. Xu, M. Liu,W. Xu, J.Wang, and S. Su, "A novel search strategy based on gradient and distribution information for artificial bee colony algorithm," J. Comput. Methods Sci. Eng., vol. 17, no. 3, pp. 377-395, 2017. https://doi.org/10.3233/jcm-170724
  14. J. Kennedy, "Swarm intelligence," in Handbook of Nature-Inspired and Innovative Computing. Springer, pp. 187-219, 2006.
  15. C. Ozturk, E. Hancer, and D. Karaboga, "Dynamic clustering with improved binary artificial bee colony algorithm," Appl. Soft Comput., vol. 28, pp. 69-80, 2015. https://doi.org/10.1016/j.asoc.2014.11.040
  16. G. P. Zhu and S. Kwong, "Gbest-guided artificial bee colony algorithm for numerical function optimization," Appl. Math. Comput., vol. 217, no. 7, pp. 3166-3173, 2010. https://doi.org/10.1016/j.amc.2010.08.049
  17. Q. K. Pan, M. F. Tasgetiren, and P. N. Suganthan, and T. J. Chua, "A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem," Inf. Sci., vol. 181, no. 12, pp. 2455-2468, 2011. https://doi.org/10.1016/j.ins.2009.12.025
  18. M. S. Kimathran and O. Fimathndimathk, "A directed artificial bee colony algorithm," Appl. Soft Comput., vol. 26, pp. 454-462, 2015. https://doi.org/10.1016/j.asoc.2014.10.020
  19. D. Karaboga and B. Gorkemli, "A combinatorial artificial bee colony algorithm for traveling salesman problem," in Proc. Int. Symp. Innovations in Intelligent Systems and Applications, Istanbul, Turkey, pp. 50-53, 2011.
  20. J. K. Ji, S. B. Song, C. Tang, S. C. Gao, Z. Tang, and Y. Todo, "An artificial bee colony algorithm search guided by scale-free networks," Inf. Sci., vol. 473, pp. 142-165, 2019. https://doi.org/10.1016/j.ins.2018.09.034
  21. D. Wang, D. Tan, and L. Liu, "Particle swarm optimization algorithm: An overview," Soft Comput., vol. 22, no. 2, pp. 387-408, 2017. https://doi.org/10.1007/s00500-016-2474-6
  22. Y.-J. Gong, J.-J. Li, Y. Zhou, Y. Li, H. S.-H. Chung, Y.-H. Shi, and J. Zhang, "Genetic learning particle swarm optimization," IEEE Trans. Cybern., vol. 46, no. 10, pp. 2277-2290, 2016. https://doi.org/10.1109/TCYB.2015.2475174
  23. W. B. Du, Y. Gao, C. Liu, Z. Zheng, and Z. Zheng, "Adequate is better: Particle swarm optimization with limited-information," Appl. Math. Comput., vol. 268, no. 10, pp. 832-838, 2015. https://doi.org/10.1016/j.amc.2015.06.062
  24. M. S. Kiran, and M. Gunduz, "XOR-based artificial bee colony algorithm for binary optimization," Turkish Journal of Electrical Engineering & Computer Sciences, Vol. 21, 2013.