It is one of the important study tasks for negotiation optimization to solve negotiation deadlocks.In order to get rid of such deadlocks in the time-limited bilateral and multi-issue autonomous negotiation

a multi-objective particle swarm optimization algorithm

called MOPSO

is put forward in this paper.MOPSO makes full use of the relationship among issues and first relaxes the reserved value of the issue dynamically which triggers the negotiation deadlock.Then the algorithm translates the problem of tightening the reserved values of the issues relevant to the deadlock issue into a multi-objective optimization one and turns up a Pareto-optimal set by a particle swarm.In this way

these reserved values are optimized in parallel and the algorithm lastly replaces the old reserved vector of the negotiation issues with a new one equivalently

which keeps the level of the integrated utility of the negotiant.The obtained results of experiments on E-commerce support the claim that MOPSO is valid and it is preferable to the existing method in solving the problem of the negotiation deadlocks.