Using counter-proposal to optimize genetic algorithm based cooperative negotiation

Multiagent systems have been widely used in concurrent engineering. However, most multi-agent tasks have high complexity. Optimal solutions of them are nearly intractable due to the heavy load of communication and large complexity of knowledge exchanges. In this paper, we extend the agent structure in our previous work. Rather than using common multi-agent diagram, we use stochastic search techniques such as genetic algorithms (GAs) to explore the solutions. Moreover, we also add counter proposals to speed up the searching process. Experimental results about designing a gear pump are satisfying, which turn out that the combination of GAs (modified to adapt to counter proposals) and Multiagent Systems can considerably speed up the convergence process, and at the same time, guarantee the best globe solution