A comparative analysis for binary search operators used in artificial bee colony

Atli, Ibrahim; Durgut, Rafet; Aydin, Mehmet Emin

Abstract

Metaheuristic optimization algorithms are developed to find the best or near-best solutions within a reasonable time frame utilizing various neighbourhood functions (i.e., operators). Variety of studies have been proposed for structural modifications on metaheuristic approaches or utilization of various operators. Some of these operators help fast convergence at the beginning but lose efficiency relatively or completely towards the end or vice versa. The individual and collective behaviors of operators in the search space plays crucial role in producing fruitful solutions to approximate the optimum and to devise useful adaptive selection schemes in the cases of using multiple operators. To the best of our knowledge, collective behaviour of binary operators has not been analysed comprehensively. In this study, the characteristics and collective behaviour of operators that can work on discrete decision variables within an artificial bee colony are investigated over solving OneMax and SUKP problems utilizing 9 different operators. The results conclude that disABC, GBABC and twoOptABC operators are more effective in solving OneMax problems, while GBABC and twoOptABC are more effective (especially towards end) in the SUKP problems.

Citation

Atli, I., Durgut, R., & Aydin, M. E. (2021). A comparative analysis for binary search operators used in artificial bee colony. In 2021 29th Signal Processing and Communications Applications Conference (SIU). https://doi.org/10.1109/SIU53274.2021.9477907

• Conference Name: SIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings
• Conference Location: Istanbul, Turkey
• Start Date: Jun 9, 2021
• End Date: Jun 11, 2021
• Acceptance Date: May 30, 2021
• Online Publication Date: Jul 19, 2021
• Publication Date: Jul 19, 2021
• Deposit Date: Aug 17, 2021
• Book Title: 2021 29th Signal Processing and Communications Applications Conference (SIU)
• ISBN: 9781665436502
• DOI: https://doi.org/10.1109/SIU53274.2021.9477907
• Public URL: https://uwe-repository.worktribe.com/output/7624758