【摘要】 基于云模型的随机性、模糊性和稳定性特征,通过正态云发生器对量子粒子群优化算法(QPSO)进行改进,提出了一种基于正态云模型的自适应量子粒子群优化算法(CMAQPSO).该算法将正态云模型引入到QPSO算法的研究,定义了收缩扩张系数的云调整策略和粒子云变异算子的构建公式,给出了量子势阱中心调整策略和边界修正策略.用5个标准测试函数对SPSO,OPSO,CVCPSO,CMAQPSO 4种算法进行对比测试,实验结果表明,CMAQPSO在5个测试函数上的平均寻优效果都明显优于其他3种算法.更多还原

【Abstract】 Based on the randomness, fuzziness and stability of cloud model, an adaptive quantum particle swarm optimization algorithm CMAQPSO based on normal cloud model is proposed. The algorithm uses X conditional cloud generator to control the contraction expansion coefficient of QPSO algorithm, and uses Y condition cloud generator to construct the mutation operator of QPSO algorithm. A quantum well center adjustment strategy and boundary correction strategy are proposed. The experimental results show that the average optimization effect of the CMAQPSO algorithm on the five test functions is significantly better than the other three algorithms(SPSO, OPSO,CVCPSO).更多还原