A Multi-objective Genetic Algorithm Method to Support Multi-agent Negotiations

Negotiations are among the most common ways that agents in a multi-agent system use to reach agreements. Because negotiations commonly are multi-lateral and multi-issue, these processes become more difficult. In the real world applications this becomes more important where the autonomous agents involved in a negotiation should reach maximum payoff in minimum time. In this work a new negotiation mechanism is proposed that is based on the multi-objective genetic algorithms. Several measures are defined that can show fitness of an offer in the set of feasible offers that an agent can have in each round of negotiations. The results show that this method can be used in real applications and is competitive with existing approaches.