作　　者： ; ; ;

机构地区： 华南理工大学自动化科学与工程学院

出　　处： 《华南理工大学学报（自然科学版）》 2009年第1期79-85,共7页

摘　　要： 为提高多目标数值优化问题解的收敛速度并保持解的多样性,基于多目标优化和量子计算原理,提出了一种量子演化算法.首先,根据多目标优化特点,使用多目标密度比较算子对量子种群进行排序和筛选;然后,应用非均匀变异算子对观测种群进行变异以保持解的收敛性并提高局部搜索的能力;最后,使用多样性保持算子对观测种群进行删减以保持解的多样性.实验结果表明,与NSGA-II算法相比,文中算法具有更高的收敛速度和更好的种群多样性. In order to improve the convergence rate and preserve the diversity of the solutions to multi-objective numerical optimization problems, a quantum-inspired evolutionary algorithm is presented based on the principles of quantum computation and multi-objective optimization. In this algorithm, first, a crowed comparison operator is used to sort and select individuals according to the characteristics of multi-objective optimization. Then, a non-uniform mutation operator is applied to the observation populations to preserve the convergency of the solutions and to improve the precision of local search. Finally, a diversity-preserving operator is employed to delete the observation populations for the purpose of preserving the solution diversity. Experimental results show that, as compared with the NSGA-Ⅱ algorithm, the proposed algorithm is of higher convergence rate and better population diversity.

关 键 词： 数值优化 演化算法 实值编码 非均匀变异 平方脉冲

领　　域： [自动化与计算机技术] [自动化与计算机技术]