Research on the decision-making of multi-issue/attribute negotiation based on agent technology and the Genetic Algorithm

Nowadays, goods are no longer necessarily traded at a fixed price, and instead buyers and sellers negotiate among themselves to reach a deal that maximizes the payoffs of both parties. Genetic-agent-based automated negotiation system has become increasingly important since the advent of electronic commerce and the rapid development of agent technology and Genetic Algorithm (GA). In this paper, the performance features and working principle of Agent technology and GA are analyzed and summarized; a genetic-agent-based model that can efficiently carry out bilateral negotiations with multi-attributes and ensure the fairness in a negotiation. The model also considers the efficiency of a negotiation and the extensibility toward multilateral negotiations. Through the application of this model, we can find that the trading program is being optimized with the increase in the number of iterative, the satisfaction is continuously improved and the purpose of a win-win situation finally reaches.