中文摘要：

为了能够找到更多真实的Pareto最优解和提高所求最优解的分布均匀性,提出了一种新型的基于量子行为特性粒子群优化和自适应网格的多目标量子粒子群优化算法.利用量子行为特性粒子群优化算法的寻优优势快速地接近真实的Pareto最优解,引入高斯变异算子增强搜索解的多样性.通过设置一个外部存储器保留搜索过程中找到的Pareto最优解,采用自适应网格法对外部存储器中最优解进行更新和维护操作,使得从中选择的领导粒子能够引导粒子群最终找到真实的Pareto最优解.仿真结果表明所提算法具有更好的收敛性能和更均匀的分布性能.

英文摘要：

In order to find more true Pareto optimal solutions and improve their uniformity of distribution,a multi-objective quantum particle swarm optimization algorithm based on quantum-behaved particle swarm optimization（QPSO） and adaptive grid（MOQPSO） is proposed.MOQPSO makes full use of the superiority of quantum-behaved particle swarm optimization to approximate the true Pareto optimal solutions quickly,and Gaussian mutation operator is introduced to enhance the diversity of searched solutions.MOQPSO reserves the found Pareto optimal solutions by setting an external memory,and then updates and maintains the optimal solutions based on adaptive grid,in order to guide the particle swarm finding the true Pareto optimal solutions finally by the leader particles from external memory.Simulation results denote that MOQPSO is of better convergence and more uniform distributing performance.