Multi-Agent Coalition Formation Based on Quantum-behaved Particle Swarm Optimization

摘要

Coalition formation has become a key topic in multi-agent research. It mainly researches on how to generate the optimal task-oriented coalition in dynamic manner. In this paper, the quantum-behaved particle swarm optimization (QPSO) is proposed for this problem. And the multi particle swarms based on public history researching parallel is presented for improving QPSO. On the base of using the better recording locations of all particles and the mutation of the best behaved particle, the particle swarm is filtrated, accelerating the convergence speed. Multi particle swarms are used to research parallel to avoid running into local optima at the same time. The result of experiments shows that the proposed algorithm can reduce the searching time and computing works effectively, and is valid and superior to other related methods as far as the stability and speed of convergence.