Collective decision optimization algorithm: A new heuristic optimization method

• Published in: Neurocomputing (Amsterdam) Vol. 221; pp. 123 - 137
• Main Authors: Zhang, Qingyang, Wang, Ronggui, Yang, Juan, Ding, Kai, Li, Yongfu, Hu, Jiangen
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 19.01.2017
• Subjects: Artificial neural networks; Collective decision optimization algorithm; Decision-making; Meta-heuristic; Decision-making; Meta-heuristic; Collective decision optimization algorithm; Artificial neural networks
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2016.09.068

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.