摘要
针对露天矿生产临时出现变动,导致原最优生产作业计划失效的情况,提出一种能够提供备选方案的优化方法。采用一种新型的群智能优化算法——多元优化算法实现优化,算法的搜索元采用上三角数据结构体存储,利用该结构体实现有用信息的记忆和共享,充分利用寻优过程信息,实现搜索过程记忆,在找到最优解的同时,保留多个次优解。以某露天铁矿为例,通过与其他三种常用的群智能算法的优化结果进行比较,表明多元优化算法在露天矿生产作业计划优化中能够提供备选方案且最优解精度更高。
In view of the optimum plan failures caused by varying environments in open-pit mines, an optimization method of open-pit-mine production planning with various alternatives was proposed. A novel swarm intelligence algorithm,multivariant optimization algorithm, was adopted to optimize the production planning. Stored in an upper triangular data structure which memorized and shared useful information, the search atoms of the algorithm achieved the memory of search process by making full use of optimization search process information so that multiple suboptimal alternatives were retained while finding the optimal solution. Taking an open-pit iron mine as an engineering background, this paper compared the optimization results of this algorithm with those of other three swarm intelligence algorithms. The results indicate that multivariant optimization algorithm can provide various alternatives and that the optimization solution is more accurate in the optimization of open-pit-mining production planning.
出处
《山东科技大学学报（自然科学版）》
CAS
2015年第6期102-106,共5页
Journal of Shandong University of Science and Technology(Natural Science)
基金
国家自然科学基金项目(61261007)
关键词
露天矿
生产作业计划
多元优化算法
搜索元
过程记忆
open-pit-mining
production planning
multivariant optimization algorithm
search atom
process memory
