基于QPSO的单任务Agent联盟形成

智能群体搜索算法在求解单任务Agent联盟时稳定性较差、收敛速度慢、全局寻优能力不强，因此采用优化的量子粒子群优化算法解决上述问题。利用群体历史优质解，在最优粒子变异的基础上，采用多种群并行搜索，防止陷入局部极值，并对粒子群进行筛选以加快粒子群的收敛速度。对比实验结果表明，该算法可以快速、高效地找出合适的Agent联盟，在运行时间和解的质量方面优于同类算法。

Agent Coalition Formation for Single Task Based on Quantum-behaved Particle Swarm Optimization

There are some problems such as slow convergence, low stability and poor global optimization ability when intelligent search algorithms solve single task Agent coalition. This paper uses an improved Quantum-behaved Particle Swarm Optimization(QPSO) to solve the problems. By using the better recording locations of all particles and the mutation of the best behaved particle, and based on public history researching Parallel, the particle swarm is filtrated and the convergence speed is accelerated. Multiple particle swarms are used to research parallel, avoiding running into local optima. Comparative experimental results show that the algorithm can identify the Agent alliance quickly and efficiently. Its run-time performance is better than other algorithms.