摘要：
采用粒子群优化(PSO)算法求解矿山企业动态配矿问题。依据开采条件圈定出可开采的矿块，用粒子的一位代表矿块，并用0或者1代表选择该矿块来开采，重新定义在约束条件下PSO粒子的运算与“飞行”规则，实现动态配矿优化的粒子群算法。该PSO算法实施简单，优化效果明显，通过2009年实际生产情况与优化结果的对比表明，该算法在生产成本几乎不变的情况下，明显提高了企业效率。
关键词:
配矿优化,
粒子群优化算法,
多目标优化
Abstract:
Particle Swarm Optimization(PSO) algorithm is proposed to solve the problem of mining enterprises dynamic allocation. The mineable ore blocks can be marked according to mining conditions delineated, with a bit of ore particles to represent a mine block, and the selected ore mining block is represented by 1, and the constrained PSO particles computing and “flight” rule is re-defined and the dynamic allocation ore particle swarm algorithm is realized. The PSO algorithm implementation is simple, the optimum effect is obvious, the comparison of actual production in 2009 and the optimization results shows that the algorithm improves enterprise efficiency significantly when the cost of production is almost unchanged.
Key words:
mine ore blending optimization,
Particle Swarm Optimization(PSO) algorithm,
multi-objective optimization
中图分类号:
黄启富, 陈建宏. 基于PSO的矿山企业动态配矿优化研究[J]. 计算机工程, 2011, 37(8): 175-176.
HUANG Qi-Fu, CHEN Jian-Hong. Research on Dynamic Mine Ore Blending Optimization Based on Particle Swarm Optimization in Mining Enterprises[J]. Computer Engineering, 2011, 37(8): 175-176.