摘要
以内蒙古一露天煤矿为研究对象,对露天矿车辆调度过程中的关键时间参数进行统计分析,确定其随机性,建立车辆调度的不确定模型。在对不确定调度模型优化分析的过程中,训练神经网络逼近函数,对于粒子群算法容易陷入局部收敛的缺陷,结合模拟退火算法的局部搜索技术,得到模拟退火算法和粒子群算法相结合的混合智能算法。计算实验结果证明该算法的有效性和优越性。
Taking an open mine in Inner Mongolia as the research object, the randomness of the key time parameters in vehicle scheduling was determined through statistical analysis, and the uncertain model of vehicle scheduling was given. Neural network was trained to approximate function. Because the particle swarm algorithm was likely to fall into local convergence, combining the local search method of the simulated annealing algorithm, a hybrid intelligent algorithm which consisted of the simulated annealing algorithm and the particle swarm optimization was proposed to solve the uncertain model. The results show that the high performance and effectiveness of the proposed method is improved through the computational results.
出处
《中南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2011年第5期1354-1360,共7页
Journal of Central South University:Science and Technology
基金
国家高技术研究发展计划("863"计划)项目(2007AA06Z114)
中国矿业大学青年科研基金资助项目(0C080250)
关键词
不确定调度
车辆调度
粒子群算法
模拟退火算法
混合智能算法
uncertain scheduling
vehicle scheduling
particle swarm optimization
simulated annealing optimization
hybrid optimization algorithm
