摘要
针对量子进化算法易陷入局部最优和求解精度不高的缺点,利用云模型具有随机性和稳定倾向性的特点,提出了一种基于云模型的实数编码量子进化算法。该算法利用单维云变异进行全局快速搜索,利用多维云进化增强算法局部搜索能力,探索全局最优解。依据算法的进化过程动态调整搜索范围并复位染色体,可以加提高敛速度,并防止陷入局部最优。仿真结果表明,该算法搜索精度和效率得到提高,适合求解复杂函数优化问题。
To deal with the problems of easily failing into local optimum and low accuracy in the quantum evolutionary algorithm, a real-coded quantum evolutionary algorithm based on cloud model （CRCQEA） was proposed by using the characteristics of cloud model randomness and stable disposition. The algorithm used a single-dimensional variation of cloud for rapid global search, and used a multi-dimensional cloud evolution for enhancing local search ability to explore the global optimal solution. Dynamic adjustment of search range and resetting of the chromosomes, on the basis of the evolutionary process of algorithm, can speed up the convergence and prevent falling into local optimum. The simulation results show that the algorithm improves search accuracy and efficiency, and the algorithm is well suitable for the complex function optimization.
出处
《计算机应用》
CSCD
北大核心
2013年第9期2550-2552,2569,共4页
journal of Computer Applications
基金
陕西省教育厅科学研究计划项目(12JK1111)
关键词
云模型
量子进化算法
实数编码
全局优化
函数优化
cloud model
quantum evolutionary algorithm
real-coded
global optimization
function optimization
