A Comparative Analysis for Binary Search Operators used in Artificial Bee Colony
Küçük Resim Yok
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
29th IEEE Conference on Signal Processing and Communications Applications (SIU) -- JUN 09-11, 2021 -- ELECTR NETWORK
Anahtar Kelimeler
Binary Operators, search space, metaheuristics, operator characteristic, optimization
Kaynak
29th Ieee Conference On Signal Processing and Communications Applications (Siu 2021)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
