摘要
传统蚁群算法的缺点在于搜索耗时较长,经常会出现局部最优解的问题。为解决该问题,引进了适应度函数,并在设计遗传算子时,重新优化适应度函数。为尽量规避出现局部最优解,在不改变种群参数的条件下,通过改进启发函数更新方式的方法,得到了一个比传统算法更加可靠、快速的蚁群算法,通过改进后的蚁群算法得到最短路径为31,搜索耗时均值为20.667 m/s;与之对比,经典遗传算法两项数据分别是37和24.667 m/s。因此,实验证明了新算法可在更短时间内给出最优解。
The disadvantage of traditional ant colony algorithm is that it takes a long time to search,and it often leads to the problem of local optimal solution.In order to solve this problem,the ftness function is introduced,and the ftness function is re-optimized when genetic operators are designed.In order to avoid the local optimal solution as far as possible,without changing the population parameters,obtains a more reliable and fast ant colony algorithm by improving the updating method of heuristic function.Through the improved ant colony algorithm,the shortest path is 31,and the average search time is 20.667 m/s.By comparison,the data of classical genetic algorithm are 37 m/s and 24.667 m/s,respectively.Therefore,experiments show that the new algorithm can give a better solution in a shorter time.
作者
黄方东
时康
雍家伟
陶富强
陆华才
HUANG Fangdong;SHI Kang;YONG Jiawei;TAO Fuqiang;LU Huacai(Anhui Key Laboratory of Electric Drive and Control,Anhui Polytechnic University,Wuhu 241000,China)
出处
《电气应用》
2019年第S01期171-174,共4页
Electrotechnical Application
基金
安徽工程大学大学生科研项目(2019DZ10、2019DZ16)
国家级创新创业训练计划项目(201810363018)
