基于蚁群算法的改进遗传算法

遗传算法具有快速全局搜索能力，但对于系统中的反馈信息却没有利用，往往导致无为的冗余迭代，求解效率低。根据这一缺陷提出一种将蚁群算法融合到遗传算法的新策略：为了弥补遗传算法中的变异算子变异过程中的盲目无原则性，将蚁群算法的正反馈思想引入到遗传算法中。利用蚁群算法信息素更新原则指导变异规则，有效地提高了算法的寻优效率，优化了解的质量。为了验证算法的有效性，对TSPLIB库中的两个公共实际事例eil51和gr202以及安徽省17个城市的数据进行了仿真实验，结果表明改进后的算法是有效的。

Improved Genetic Algorithm Based On Ant Colony Algorithm

Genetic algorithm has the advantage of fast overall searching ability, but it does not utilize feedback information in the system. Thus it often results in redundant iteration and low efficiency in solution. Because of this, a new strategy blending ant colony algorithm into genetic algorithm was put forward. In order to eliminate blindness in variation process of variation operator, the idea of ant colony algorithm＇s positive feedback was introduced into genetic algorithm. Variation rules were led by the rule of pheromone updating, which improves the algorithm efficiency and solution quality. The proposed algorithm was verified by the two public data gr202 and eil51 in TSPLIB library and the TSP data from 17 cities in Anhui Province. The results show that the improved genetic algorithm is valid.