Negotiation process for bi-objective multi-agent flexible neural tree model

The major issue of researchers in ANN field is the optimization of the training process including time cost and NN structure. In response to the long training time, Multi-Agent architecture of feed forward Flexible Neural Tree model (MAFNT) is introduced for parallelizing the NN training. Moreover, looking for the best topology of NN, for a given problem, accounts for the large feasible solutions provided. Agents manage different NN structures simultaneously for optimization using Evolutionary Computation algorithms. However, different agents need communications to produce cooperative work and to reach the near-optimum solution. For that, a negotiation process is designed for the multi-agent system. It distributes tasks and organizes the message traffic between agents. They followed negotiation strategy to ensure interactions between themselves, overcoming the difference of NN structures. This model was evaluated through real problem classification datasets. Compared to some existing classifiers, MAFNT shows better performance respecting NN structure complexity and classification rate.