摘要
为满足煤炭行业的市场需求并节约成本,降低企业的运输费用,提高运输效率,通过建立煤炭运输系统的仿真模型,使用动态蚁群遗传算法,优化运输线路。通过与基本蚁群算法、遗传算法的比较,仿真结果表明,动态蚁群遗传算法能得到更好的初始解,更快的收敛,更优的最优解。
An optimization model by a dynamic ant algorithm-genetic algorithm based on coal industry under the condition of uncertain market demand was developed to satisfy the market demands of coal industry, and save the total cost, and to reduce the inventory. Compared with the basic ant colony algorithm and genetic algorithm, the simulation results show that the dynamic ant algorithm-genetic algorithm can get better initial solution, and faster convergence and better optimal solution.
作者
朱兴林
ZHU Xinglin(CCTEG Chongqing Engineering Co.,Ltd.,Chongqing,400039,China)
出处
《自动化与仪器仪表》
2018年第9期180-181,184,共3页
Automation & Instrumentation
关键词
运输
动态蚁群遗传算法
蚁群算法
遗传算法
优化
仿真
transportation
dynamic ant algorithm - genetic algorithm
ant algorithm
genetic algorithm
optimization
simulation