基于云模型云滴机制的量子粒子群优化算法

摘要

利用云模型云滴的随机性和稳定倾向性的特点,提出了一种云模型云滴机制的量子粒子群优化算法,该算法在量子粒子群优化的基础上,由云模型的X,y条件发生器产生杂交操作,由基本云发生器产生变异操作,用于求解具有变量边界约束的非线性复杂函数最优化问题.仿真结果表明,该算法具有计算精度较高,搜索速度较快等特点,具有一定的参考和应用价值.%A novel Cloud model Cloud droplet strategy's Quantum Particle Swarm Optimization(CQPSO) is proposed by using the characteristics of cloud model cloud droplet randomness and stable disposition. This algorithm based on Quantum Particle Swarm Optimization, it produces hybrid operation by cloud model of X, Y generators and mutation operation by basic cloud generator. It is used to solve the complex functions optimization problem with variable boundary constrained nonlinearity. The simulation results show that the algorithm has higher calculation precision, faster search speed and so on. It has some references and application values.