One of the surface mining methods is open-pit mining,by which a pit is dug to extract ore or waste downwards from the earth’s surface.In the mining industry,one of the most significant difficulties is long-term produ...One of the surface mining methods is open-pit mining,by which a pit is dug to extract ore or waste downwards from the earth’s surface.In the mining industry,one of the most significant difficulties is long-term production scheduling(LTPS)of the open-pit mines.Deterministic and uncertainty-based approaches are identified as the main strategies,which have been widely used to cope with this problem.Within the last few years,many researchers have highly considered a new computational type,which is less costly,i.e.,meta-heuristic methods,so as to solve the mine design and production scheduling problem.Although the optimality of the final solution cannot be guaranteed,they are able to produce sufficiently good solutions with relatively less computational costs.In the present paper,two hybrid models between augmented Lagrangian relaxation(ALR)and a particle swarm optimization(PSO)and ALR and bat algorithm(BA)are suggested so that the LTPS problem is solved under the condition of grade uncertainty.It is suggested to carry out the ALR method on the LTPS problem to improve its performance and accelerate the convergence.Moreover,the Lagrangian coefficients are updated by using PSO and BA.The presented models have been compared with the outcomes of the ALR-genetic algorithm,the ALR-traditional sub-gradient method,and the conventional method without using the Lagrangian approach.The results indicated that the ALR is considered a more efficient approach which can solve a large-scale problem and make a valid solution.Hence,it is more effectual than the conventional method.Furthermore,the time and cost of computation are diminished by the proposed hybrid strategies.The CPU time using the ALR-BA method is about 7.4%higher than the ALR-PSO approach.展开更多
建立准确的滚动轴承性能退化预测模型对于轴承故障分类、寿命预测等后续处理有着至关重要的作用。为了解决轴承性能退化模型预测不准确的问题,提出了一种改进的蝙蝠算法(improvement bat algorithm,IBA)来提高退化模型预测的准确度。首...建立准确的滚动轴承性能退化预测模型对于轴承故障分类、寿命预测等后续处理有着至关重要的作用。为了解决轴承性能退化模型预测不准确的问题,提出了一种改进的蝙蝠算法(improvement bat algorithm,IBA)来提高退化模型预测的准确度。首先将Cat混沌映射应用到种群初始位置,增强种群的遍历性,提高初始解的质量;其次在迭代过程中加入类反正切控制因子,提高算法寻优精度;最后改进位置更新策略,防止陷入局部最优。通过与蝙蝠算法(bat algorithm,BA)优化的支持向量回归机(support vector regression,SVR)、粒子群优化算法优化的SVR和灰狼优化算法优化的SVR所得的结果做对比,结果表明:IBA所优化预测模型的均值绝对误差分别下降了70.60%、67.19%、55.56%,均方根误差分别下降了76.64%、76.12%、30.29%,进一步证明了改进后的预测模型的准确性。展开更多
在矿用空压机组智能巡检机器人中,传统算法用于智能巡检机器人路径规划时,面对复杂矿井环境存在寻优速度慢、易陷入局部最优解等问题。为提升巡检效率和精度,提出了改进的蝙蝠算法(Improved Bat Algorithm,IBA)。首先,采用均匀初始化策...在矿用空压机组智能巡检机器人中,传统算法用于智能巡检机器人路径规划时,面对复杂矿井环境存在寻优速度慢、易陷入局部最优解等问题。为提升巡检效率和精度,提出了改进的蝙蝠算法(Improved Bat Algorithm,IBA)。首先,采用均匀初始化策略确保初始位置能广泛覆盖决策空间;其次,在迭代更新过程中,引入黄金正弦算子对在适应度评价中表现优异的蝙蝠个体进行优化更新,同时运用种群平均位置引导部分个体,在缩小搜索范围的同时维持较快收敛速度;最后在全局搜索阶段引入动态惯性权重系数,并采用单维与全维相结合的搜索策略。试验表明:IBA算法在5维条件下,Sphere函数测试中的收敛迭代次数仅20次,远少于蝙蝠算法(Bat Algorithm,BA),50维条件下同样表现出色;在机器人路径规划效果上,IBA算法规划路径长度比BA、自适应蝙蝠算法(Adaptive BA,ABA)和全局混沌蝙蝠算法(Global Chaos BA,GCBA)规划的路径更短,且在多个场景中转折点数量更少、收敛迭代次数更少、适应度值更低。研究反映出,基于IBA算法的智能巡检机器人路径规划方法可使矿用空压机组巡检效率提升45.9%,故障检测准确率提高至98.9%。所提算法有助于实现矿用空压机组智能巡检机器人路径高效规划,助力矿山安全生产。展开更多
文摘One of the surface mining methods is open-pit mining,by which a pit is dug to extract ore or waste downwards from the earth’s surface.In the mining industry,one of the most significant difficulties is long-term production scheduling(LTPS)of the open-pit mines.Deterministic and uncertainty-based approaches are identified as the main strategies,which have been widely used to cope with this problem.Within the last few years,many researchers have highly considered a new computational type,which is less costly,i.e.,meta-heuristic methods,so as to solve the mine design and production scheduling problem.Although the optimality of the final solution cannot be guaranteed,they are able to produce sufficiently good solutions with relatively less computational costs.In the present paper,two hybrid models between augmented Lagrangian relaxation(ALR)and a particle swarm optimization(PSO)and ALR and bat algorithm(BA)are suggested so that the LTPS problem is solved under the condition of grade uncertainty.It is suggested to carry out the ALR method on the LTPS problem to improve its performance and accelerate the convergence.Moreover,the Lagrangian coefficients are updated by using PSO and BA.The presented models have been compared with the outcomes of the ALR-genetic algorithm,the ALR-traditional sub-gradient method,and the conventional method without using the Lagrangian approach.The results indicated that the ALR is considered a more efficient approach which can solve a large-scale problem and make a valid solution.Hence,it is more effectual than the conventional method.Furthermore,the time and cost of computation are diminished by the proposed hybrid strategies.The CPU time using the ALR-BA method is about 7.4%higher than the ALR-PSO approach.
