Collective decision optimization algorithm: A new heuristic optimization method
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- 作者：Qingyang Zhang hgd_qy@126.comAuthor Vitae ; Ronggui Wang wangrgui@hfut.edu.cnAuthor Vitae ; Juan Yang ; yangjuan6985@163.comAuthor Vitae ; Kai Ding dingkai0422@126.comAuthor Vitae ; Yongfu Li lyf_strawberry@sina.comAuthor Vitae ; Jiangen Hu hgdhujgen@163.comAuthor Vitae
- 关键词：Collective decision optimization algorithm ; Artificial neural networks ; Meta-heuristic ; Decision-making
- 刊名：Neurocomputing
- 出版年：2017
- 出版时间：19 January 2017
- 年：2017
- 卷：221
- 期：Complete
- 页码：123-137
- 全文大小：1704 K
- 卷排序：221
文摘
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.