【摘要】 遗传算法具有快速随机的全局搜索能力 ,但对于系统中的反馈信息利用却无能为力 ,当求解到一定范围时往往做大量无为的冗余迭代 ,求精确解效率低 蚂蚁算法是通过信息素的累积和更新收敛于最优路径上 ,具有分布式并行全局搜索能力 但初期信息素匮乏 ,求解速度慢 算法是将遗传算法与蚂蚁算法融合 ,采用遗传算法生成信息素分布 ,利用蚂蚁算法求精确解 ,优势互补 仿真表明取得了非常好的效果更多还原

【Abstract】 Genetic algorithm has the ability of doing a global searching quickly and stochastically But it can’t make use of enough system output information It has to do a large redundancy repeat for the result when solving to certain scope So the efficiency to solve precision results is reduced Ant algorithm converges on the optimization path through information pheromone accumulation and renewal It has the ability of parallel processing and global searching The speed at which the ant algorithm gives the solution is slow, because there is little information pheromone on the path early The algorithm in this paper is based on the combination of genetic algorithm and ant algorithm First, it adopts genetic algorithm to give information pheromone to distribute Second, it makes use of the ant algorithm to give the precision of the solution Finally, it develops enough advantage of the two algorithms The simulation results show that very nice effects are obtained更多还原