摘要
针对利用启发式方法求解二阶段迭代模型的最优任务分布方案容易陷入局部最优解的问题,根据二阶段迭代模型中各任务的分布特点,引入具有自适应全局优化特性的遗传算法求解出二阶段迭代模型的最优任务分布方案,从而减少任务执行的时间成本。以某汽车发动机的开发为例,分别应用基于启发式方法和基于遗传算法方法进行求解寻优,通过比较说明了后者的有效性。
In solving the optimal task distribution scheme of two-stage iterative model, heuristic method is easy to fall into local optimal solution. According to the distribution characteristic of tasks in two-stage iterative model, the genetic algorithm with adaptive global optimization characteristic is adoptedto obtain the optimal task distribution scheme, and realize the less time cost. An automobile engine development process is taken as a sample,the method based on heuristic method and the method based on genetic algorithm are applied to solve the optimal scheme of task distribution,and the method based on genetic algorithm is proved more effective.
作者
田启华
董群梅
杜义贤
TIAN Qi-hua,DONG Qun-mei, DU Yi-xian(College of Mechanical and Power Engineering, China Three Gorges University, Yichang 44300)
出处
《机械设计》
CSCD
北大核心
2018年第3期86-91,共6页
Journal of Machine Design
基金
国家自然科学基金资助项目(51475265)
关键词
二阶段迭代模型
遗传算法
启发式算法
任务分布
方案寻优
two-stage iterative model
genetic algorithm
heurislic algorithm
task distribution
scheme optinlization
