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Collective decision optimization algorithm: A new heuristic optimization method
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DOI：10.1016/j.neucom.2016.09.068.png)
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Abstract
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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.
Keywords:
Collective decision optimization algorithm
Artificial neural networks
Meta-heuristic
Decision-making
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