基于蚁群信息素的混合遗传算法

针对遗传算法无法利用系统中的反馈信息,求解到一定范围时出现的冗余迭代,求精确解效率低,局部搜索能力弱、易出现"早熟"现象等缺点,提出了采用蚁群信息素对均匀划分子空间进行标定,利用留存的信息素控制选择操作,采用双重选择算子、基于"杂交优势"思想的交叉算子和自适应变异算子的混合遗传算法.实验表明,采用该算法的分类系统的分类准确率、算法运行时间、算法收敛性等方面性能均有明显提高.

The Hybrid Genetic on the Pheromone Algorithm Based of Ant Colony

n the simple genetic algorithm, it is found that the feedback information is useless. Many redundant interactions are done in later stage. The low efficiency in accurate solving, poor ability in local searching and easy emergence of premature convergence is existed. Aim at the problems above, the Hybrid Genetic Algorithm is researched in this thesis. At first, the solution space of the optimization problem is divided evenly and each subspace is marked by ant colony pheromone which controls the selection operation. Secondly, double selection operator, crossover operator based on ＂heterosis＂ and adaptive mutation operator is designed. At last, the accuracy of classification, the time of the algorithm running and the convergence of algorithm is tested through the analysis of the data of the result in experiment. It is proved that the performance of the classification based on hybrid genetic algorithm is improved on the three aspects above.