基于量子粒子群算法的收敛性研究

Analyzing Convergent Ability of QPSO Algorithm

对基于量子行为的粒子群算法(QPSO)的收敛性进行分析。QPSO算法不仅参数个数少,随机性强,并且能覆盖所有解空间,保证算法的全局收敛性。通过四个经典的基准函数对算法进行测试,将QPSO算法与PSO算法进行深入比较。通过实验结果表明,QPSO算法在收敛性能上大大优于PSO算法。

An opening and practical solution, that is Quantum- behaved Particle Swarm Optimization algorithm. Not only parameters of QPSO are few and randomicity of QPSO is strong, but also QPSO cover with all the space of solution and guarantee global convergence of algorithms. Analyzing Convergent Ability of Quantum- behaved Particle Swarm Optimization Algorithm by four classical benchmark functions. Experiment results show that QPSO algorithm provides a much better effect than PSO algorithm on convergent Ability of algorithm.