摘要
将蚁群算法(ACO)应用于飞机定检人员均衡配置中。首先,根据均方差指标建立人员均衡配置模型;其次,运用3种精英策略并引入信息素限制和自适应机制对基本蚁群算法进行改进,同时提出一种新变异算子以进一步提高算法的性能;最后,运用改进蚁群算法求解模型。实例仿真表明,改进蚁群算法克服了基本蚁群算法搜索时间长、容易早熟的不足,均衡配置后人员工作时间均方差减小65.90%,验证了ACO在解决飞机定检人员均衡配置问题上的适用性。
Ant Colony Optimization（ACO） is used to balance people＇s distribution in plane＇s periodic maintenance.Firstly,the model of people＇s balance distribution is built according to the mean square deviation target.Secondly,the simple ACO is improved by three elite tactics,pheromone limits and mechanism,a new mutation operator is used to improve its function.At last,the improved ACO is used to solve the model.The simulation results demonstrate that,the improved ACO comes over the deficiency of being long in search and easy to precocity of the simple ACO,after the balanced distribution the mean square deviation is smaller 65.90% than before,and proves that ACO is good for the problem of people＇s balance distribution in plane＇s periodic maintenance.
出处
《计算机与现代化》
2011年第11期22-26,共5页
Computer and Modernization
关键词
蚁群算法
飞机定检
人员均衡配置
ACO
plane＇s periodic maintenance
people＇s balance distribution
