IEICE TRANSACTIONS on Information
Umbrellalike Hierarchical Artificial Bee Colony Algorithm
Summary :
Many optimisation algorithms improve the algorithm from the perspective of population structure. However, most improvement methods simply add hierarchical structure to the original population structure, which fails to fundamentally change its structure. In this paper, we propose an umbrellalike hierarchical artificial bee colony algorithm (UHABC). For the first time, a historical information layer is added to the artificial bee colony algorithm (ABC), and this information layer is allowed to interact with other layers to generate information. To verify the effectiveness of the proposed algorithm, we compare it with the original artificial bee colony algorithm and five representative meta-heuristic algorithms on the IEEE CEC2017. The experimental results and statistical analysis show that the umbrellalike mechanism effectively improves the performance of ABC.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.3 pp.410-418
- Publication Date
- 2023/03/01
- Publicized
- 2022/12/05
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022EDP7130
- Type of Manuscript
- PAPER
- Category
- Biocybernetics, Neurocomputing
Authors
Tao ZHENG
University of Toyama
Han ZHANG
University of Toyama
Baohang ZHANG
University of Toyama
Zonghui CAI
University of Toyama
Kaiyu WANG
University of Toyama
Yuki TODO
Kanazawa University
Shangce GAO
University of Toyama
Keyword
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Tao ZHENG, Han ZHANG, Baohang ZHANG, Zonghui CAI, Kaiyu WANG, Yuki TODO, Shangce GAO, "Umbrellalike Hierarchical Artificial Bee Colony Algorithm" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 3, pp. 410-418, March 2023, doi: 10.1587/transinf.2022EDP7130.
Abstract: Many optimisation algorithms improve the algorithm from the perspective of population structure. However, most improvement methods simply add hierarchical structure to the original population structure, which fails to fundamentally change its structure. In this paper, we propose an umbrellalike hierarchical artificial bee colony algorithm (UHABC). For the first time, a historical information layer is added to the artificial bee colony algorithm (ABC), and this information layer is allowed to interact with other layers to generate information. To verify the effectiveness of the proposed algorithm, we compare it with the original artificial bee colony algorithm and five representative meta-heuristic algorithms on the IEEE CEC2017. The experimental results and statistical analysis show that the umbrellalike mechanism effectively improves the performance of ABC.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDP7130/_p
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@ARTICLE{e106-d_3_410,
author={Tao ZHENG, Han ZHANG, Baohang ZHANG, Zonghui CAI, Kaiyu WANG, Yuki TODO, Shangce GAO, },
journal={IEICE TRANSACTIONS on Information},
title={Umbrellalike Hierarchical Artificial Bee Colony Algorithm},
year={2023},
volume={E106-D},
number={3},
pages={410-418},
abstract={Many optimisation algorithms improve the algorithm from the perspective of population structure. However, most improvement methods simply add hierarchical structure to the original population structure, which fails to fundamentally change its structure. In this paper, we propose an umbrellalike hierarchical artificial bee colony algorithm (UHABC). For the first time, a historical information layer is added to the artificial bee colony algorithm (ABC), and this information layer is allowed to interact with other layers to generate information. To verify the effectiveness of the proposed algorithm, we compare it with the original artificial bee colony algorithm and five representative meta-heuristic algorithms on the IEEE CEC2017. The experimental results and statistical analysis show that the umbrellalike mechanism effectively improves the performance of ABC.},
keywords={},
doi={10.1587/transinf.2022EDP7130},
ISSN={1745-1361},
month={March},}
Copy
TY - JOUR
TI - Umbrellalike Hierarchical Artificial Bee Colony Algorithm
T2 - IEICE TRANSACTIONS on Information
SP - 410
EP - 418
AU - Tao ZHENG
AU - Han ZHANG
AU - Baohang ZHANG
AU - Zonghui CAI
AU - Kaiyu WANG
AU - Yuki TODO
AU - Shangce GAO
PY - 2023
DO - 10.1587/transinf.2022EDP7130
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2023
AB - Many optimisation algorithms improve the algorithm from the perspective of population structure. However, most improvement methods simply add hierarchical structure to the original population structure, which fails to fundamentally change its structure. In this paper, we propose an umbrellalike hierarchical artificial bee colony algorithm (UHABC). For the first time, a historical information layer is added to the artificial bee colony algorithm (ABC), and this information layer is allowed to interact with other layers to generate information. To verify the effectiveness of the proposed algorithm, we compare it with the original artificial bee colony algorithm and five representative meta-heuristic algorithms on the IEEE CEC2017. The experimental results and statistical analysis show that the umbrellalike mechanism effectively improves the performance of ABC.
ER -
English
