Collective decision optimization algorithm: A new heuristic optimization method
Recently, inspired by nature, diversiform successful and effective optimization methods have been proposed for solving many complex and challenging applications in different domains. This paper proposes a new meta-heuristic technique, collective decision optimization algorithm (CDOA), for training a...
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| Published in | Neurocomputing (Amsterdam) Vol. 221; pp. 123 - 137 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
19.01.2017
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0925-2312 1872-8286 |
| DOI | 10.1016/j.neucom.2016.09.068 |
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| Abstract | Recently, inspired by nature, diversiform successful and effective optimization methods have been proposed for solving many complex and challenging applications in different domains. This paper proposes a new meta-heuristic technique, collective decision optimization algorithm (CDOA), for training artificial neural networks. It simulates the social behavior of human based on their decision-making characteristics including experience-based phase, others'-based phase, group thinking-based phase, leader-based phase and innovation-based phase. Different corresponding operators are designed in the methodology. Experimental results carried out on a comprehensive set of benchmark functions and two nonlinear function approximation examples demonstrate that CDOA is competitive with respect to other state-of-art optimization algorithms. |
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| AbstractList | Recently, inspired by nature, diversiform successful and effective optimization methods have been proposed for solving many complex and challenging applications in different domains. This paper proposes a new meta-heuristic technique, collective decision optimization algorithm (CDOA), for training artificial neural networks. It simulates the social behavior of human based on their decision-making characteristics including experience-based phase, others'-based phase, group thinking-based phase, leader-based phase and innovation-based phase. Different corresponding operators are designed in the methodology. Experimental results carried out on a comprehensive set of benchmark functions and two nonlinear function approximation examples demonstrate that CDOA is competitive with respect to other state-of-art optimization algorithms. |
| Author | Yang, Juan Hu, Jiangen Ding, Kai Zhang, Qingyang Wang, Ronggui Li, Yongfu |
| Author_xml | – sequence: 1 givenname: Qingyang surname: Zhang fullname: Zhang, Qingyang email: hgd_qy@126.com – sequence: 2 givenname: Ronggui surname: Wang fullname: Wang, Ronggui email: wangrgui@hfut.edu.cn – sequence: 3 givenname: Juan surname: Yang fullname: Yang, Juan email: yangjuan6985@163.com – sequence: 4 givenname: Kai surname: Ding fullname: Ding, Kai email: dingkai0422@126.com – sequence: 5 givenname: Yongfu surname: Li fullname: Li, Yongfu email: lyf_strawberry@sina.com – sequence: 6 givenname: Jiangen surname: Hu fullname: Hu, Jiangen email: hgdhujgen@163.com |
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