基于云模型的量子混合粒子群算法及其应用

Quantum Hybrid Particle Swarm Optimization Based on Cloud Model and Its Application

文章在云模型、量子算法以及PSO算法研究基础上,应用云模型对混合算法的参数进行有效更新,提出了CMQHPSO算法。新算法通过云模型特征及云发生器来调节原混合粒子群算法在性能方面寻优的效果和关键参数的优化。通过经典测试函数算例分析得出:CMQHPSO具有收敛速度快的优势;CMQHPSO在搜索过程中能寻到有效的全局最优值,避免陷入局部最优。通过实验进一步发现CMQHPSO的粒子聚集度以及收敛速度都优于其他算法且性能良好。由于新算法所具有的特征,因此该混合算法在工程项目及控制科学与工程等不同应用领域具有良好的实践价值。

Based on cloud model,quantum algorithm and PSO algorithm researches,this paper applies the cloud model to effectively update the parameters of the hybrid algorithm,and proposes the CMQHPSO algorithm.The new algorithm adjusts the performance of the original hybrid particle swarm optimization and optimizes the key parameters through cloud model features and cloud generators.The analysis of classical test function examples shows that CMQHPSO has the advantage of fast convergence performance,and that CMQHPSO can find the effective global optimum during search process,avoiding falling into local optimum.Further experiments concludes that CMQHPSO has better particle aggregation and convergence speed than other algorithms,and also has good performance.Because of the characteristics of the new algorithm,the hybrid algorithm has good practical value in various application fields,such as engineering project,control science and engineering.