矿井通风网络的多种群自适应粒子群算法优化研究
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摘要
针对矿井通风网络分支风量优化问题,以矿井通风网络的总功率最小为目标,结合矿井模型中风量平衡方程、风压平衡方程、分支阻力方程以及风机特性曲线方程等约束条件,提出一种多种群自适应粒子群优化算法(MA-PSO)对矿井通风网络实现寻优。首先对随机生成的种群进行初始化预处理,将适应值从高到低排序,然后以预处理后的局部最优解为圆心,以局部最优解与其他粒子的欧式距离的平均值为半径,将种群划分成五个子种群,接着在速度更新公式中引入拓扑项和种群交流因子,以种群为单位在求解空间中搜索,保障种群的多样性,从而加快种群进化和算法收敛速度;最后采用自适应权重和冗余粒子初始化淘汰策略,提高算法搜索能力和学习能力。仿真结果表明:该算法具有较好的多模态寻优率、更快的收敛速度和更高的收敛精度,优化后通风系统消耗的总功率较之前相比下降26. 78%,节能效果显著。
Aiming at the optimization of branch air volume in mine ventilation network,taking the minimum total power of mine ventilation network as the goal. Based on the balance equation of stroke volume,wind pressure balance equation,branch resistance equation and fan characteristic curve equation,a multi group adaptive particle swarm optimization algorithm( MAPSO) is proposed to optimize the mine ventilation network. Firstly,the random population is generated and pretreated,and the adaptive value is sorted from high to low. Secondly,taking local optimal solution as the center,the local optimal solution and the average value of the Euclidean distance of other particles as the radius, and the population is divided into five subpopulations,and then the topology item is introduced into the speed updating formula. With the population exchange factor,the particle is searched in the solution space in the subpopulation,and the diversity of the population is ensured,thus the speed of evolution and convergence is accelerated. Finally,the adaptive weight and redundant particles are used to initialize the elimination strategy to improve the searching ability and learning ability of the algorithm. The simulation results show that the algorithm has better multi-modal optimization rate,faster convergence speed and higher convergence precision. The total power consumed by the optimized ventilation system is 26. 78% lower than that of the previous,and the energy saving effect is remarkable.
Aiming at the optimization of branch air volume in mine ventilation network,taking the minimum total power of mine ventilation network as the goal. Based on the balance equation of stroke volume,wind pressure balance equation,branch resistance equation and fan characteristic curve equation,a multi group adaptive particle swarm optimization algorithm( MAPSO) is proposed to optimize the mine ventilation network. Firstly,the random population is generated and pretreated,and the adaptive value is sorted from high to low. Secondly,taking local optimal solution as the center,the local optimal solution and the average value of the Euclidean distance of other particles as the radius, and the population is divided into five subpopulations,and then the topology item is introduced into the speed updating formula. With the population exchange factor,the particle is searched in the solution space in the subpopulation,and the diversity of the population is ensured,thus the speed of evolution and convergence is accelerated. Finally,the adaptive weight and redundant particles are used to initialize the elimination strategy to improve the searching ability and learning ability of the algorithm. The simulation results show that the algorithm has better multi-modal optimization rate,faster convergence speed and higher convergence precision. The total power consumed by the optimized ventilation system is 26. 78% lower than that of the previous,and the energy saving effect is remarkable.
引文
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[11]贾善坡,伍国军,陈卫忠.基于粒子群算法与混合罚函数法的有限元优化反演模型及应用[J].岩土力学,2011(S2):598-603.
[12]陈丽安,张培铭.免疫遗传算法在MATLAB环境中的实现[J].福州大学学报(自然科学版),2004(5):554-559.
[2]Chen Kai-yan,Si Jun-hong,Zhou Fu-bao.Optimization of air quantity regulation in mine ventilation networks using the improved differential evolution algorithm and critical path method[J].International Journal of Mining Science and Technology,2015,25:79-84.
[3]李江,陈开岩,林柏泉.遗传算法在矿井通风网络优化中的应用[J].中国矿业大学学报,2007,36(6):789-793.
[4]Ge H,Ma X,Wu X,et al.Study on mine main fan switchover aiming at invariant ventilation based on non-linear constrained programming[J].International Journal of Digital Content Technology and its Applications,2012,6(17):496-505.
[5]De Souza E.Improving the energy efficiency of mine fan assemblages[J].APPL THERM ENG,2015,90:1092-1097.
[6]Wei G.Optimization of Mine Ventilation System Based on Bionics Algorithm[J].Procedia Engineering,2011,26:1614-1619.
[7]Oliveira M,Bastos-Filho C J A,Menezes R.Using network science to assess particle swarm optimizers[J].Social Network Analysis and Mining,2015,5(1):1-13.
[8]董明洪,李俊.轴流通风机喘振现象分析及预防措施[J].风机技术,2008(4):66-67.
[9]吴超.矿井通风与空气调节[M].湖南:中南大学出版社,2008.
[10]司俊鸿.矿井通风系统风流参数动态监测及风量调节优化[D].徐州:中国矿业大学,2012.
[11]贾善坡,伍国军,陈卫忠.基于粒子群算法与混合罚函数法的有限元优化反演模型及应用[J].岩土力学,2011(S2):598-603.
[12]陈丽安,张培铭.免疫遗传算法在MATLAB环境中的实现[J].福州大学学报(自然科学版),2004(5):554-559.