Improving Multi-agent Negotiations Using Multi-Objective PSO Algorithm

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Negotiation over limited resources, as a way for the agents to reach agreement, is one of the significant topics in Multi-Agent Systems (MASs). Most of the models proposed for negotiation suffer from different limitations in the number of the negotiation parties and issues as well as some constraining assumptions such as availability of unlimited computational resources and complete information about the participants. In this paper we make an attempt to ease the limitations specified above by means of a distributive agent based mechanism underpinned by Multi-Objective Swarm Optimization (MOPSO), as a fast and effective learning technique to handle the complexity and dynamics of the real-world negotiations. The experimental results of the proposed method reveal its effectiveness and high performance in presence of limited computational resources and tough deadlines.