基于全局层次的自适应QPSO算法

阐明了具有量子行为的粒子群优化算法理论（QPSO）,并提出了一种基于全局领域的参数控制方法。在QPSO中引入多样性控制模型,使PSO系统成为一个开放式的进化粒子群,从而提出了自适应具有量子行为的粒子群优化算法（AQPSO）。最后,用若干个标准函数进行测试,比较了AQPSO算法与标准PSO（SPSO）和传统QPSO算法的性能。实验结果表明,AQPSO算法具有强的全局搜索能力,其性能优于其它两个算法,尤其体现在解决高维的优化问题。

Adaptive Quantum-behaved Particle Swarm Optimization on global level

Formulates the philosophy of Quantum-behaved Particle Swarm Optimization(QPSO) algorithm,and suggests a parameter control method based on the population level.After that,introduces a diversity-guided model into the QPSO to make the PSO system an open evolutionary particle swarm and therefore propose the Adaptive Quantum-behaved Particle Swarm Optimization Algorithm(AQPSO).Finally,the performance of AQPSO algorithm is compared with those of Standard PSO(SPSO) and original QPSO by testing the algorithms on several benchmark functions.The experiments results show that AQPSO algorithm outperforms due to its strong global search ability,particularly in the optimization problems with high dimension.