Bu öğeden alıntı yapmak, öğeye bağlanmak için bu tanımlayıcıyı kullanınız: http://hdl.handle.net/11452/26908
Başlık: An adaptive artificial bee colony algorithm for global optimization
Yazarlar: Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü.
0000-0002-9220-7353
0000-0003-2978-2811
Yurtkuran, Alkın
Emel, Erdal
N-8691-2014
AAH-1410-2021
26031880400
6602919521
Anahtar kelimeler: Adaptive search
Artificial bee colony algorithm
Global optimization
Efficient
Evolutionary algorithms
Global optimization
Heuristic algorithms
Optimization
Adaptive search
Artificial bee colony algorithms
Artificial bee colony algorithms (ABC)
Bench-mark problems
Computational results
Information sharing
Meta heuristic algorithm
Selection probabilities
Algorithms
Yayın Tarihi: 15-Kas-2015
Yayıncı: Elsevier Science
Atıf: Yurtkuran, A. ve Emel, E. (2015). "An adaptive artificial bee colony algorithm for global optimization". Applied Mathematics and Computation, 271, 1004-1023.
Özet: Artificial bee colony algorithm (ABC) is a recently introduced swarm based meta heuristic algorithm. ABC mimics the foraging behavior of honey bee swarms. Original ABC algorithm is known to have a poor exploitation performance. To remedy this problem, this paper proposes an adaptive artificial bee colony algorithm (AABC), which employs six different search rules that have been successfully used in the literature. Therefore, the AABC benefits from the use of different search and information sharing techniques within an overall search process. A probabilistic selection is applied to deterinine the search rule to be used in generating a candidate solution. The probability of selecting a given search rule is further updated according to its prior performance using the roulette wheel technique. Moreover, a ineinoly length is introduced corresponding to the maximum number of moves to reset selection probabilities. Experiments are conducted using well-known benchmark problems with varying dimensionality to compare AABC with other efficient ABC variants. Computational results reveal that the proposed AABC outperforms other novel ABC variants.
URI: https://doi.org/10.1016/j.amc.2015.09.064
https://www.sciencedirect.com/science/article/pii/S0096300315013028
http://hdl.handle.net/11452/26908
ISSN: 0096-3003
Koleksiyonlarda Görünür:Scopus
Web of Science

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