Collective decision optimization algorithm: A new heuristic optimization method - Zhang, Qingyang; Wang, Ronggui; Yang, Juan; Ding, Kai; Li, Yongfu; Hu, Jiangen (Neurocomputing (Amsterdam), 2017)
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.