基于多色集合的改进DPSO求解进出库调度  

Improved DPSO Based on Polychromatic Sets Theory and Research on Loading/Unloading Scheduling

作　　者：杨玮[1] 党培[1] 傅卫平[2] 邱小红[1] 

机构地区：[1]陕西科技大学机电工程学院,陕西西安710021 [2]西安理工大学机械与精密仪器工程学院,陕西西安710048

出　　处：《计算机仿真》2015年第2期395-399,共5页Computer Simulation

基　　金：国家自然科学基金资助项目(11072192);陕西科技大学科研启动基金项目(BJ12-21);陕西省农业科技创新与攻关项目(014K01-29-01);基于物联网的猪肉冷链物流追溯系统研究(14JK1093)

摘　　要：在自动化立体仓库进出库调度问题的研究中,影响自动化立体仓库进出库调度的因素较多,具有一定的复杂性。为避免传统遗传算法在求解进出库调度问题中存在的"早熟"或收敛过慢等不足,提出基于多色集合理论的改进离散粒子群算法。在求解过程中用多色集合的围道矩阵来合理安排进出库货位在粒子中的位置,以提高粒子群算法初始化种群的质量,从而提高算法的搜索性能和优化结果,并在迭代过程中对部分粒子重新初始化,以保证粒子的多样性,避免结果陷入局部最优。通过与遗传算法和离散粒子群算法的实例比较,利用上述算法进出库调度不仅所用时间短,且算法收敛快、迭代次数少,从而验证了改进算法在解决自动化仓库进出库调度优化问题时的有效性和优越性。There are many factors affecting loading/unloading scheduling in Automated Storage and Retrieval Sys- tem（AS/RS） , so it is a complex problem. In order to avoid premature convergence of conventional genetic algo- rithm, an improved discrete particle swarm optimization algorithm based on polyehromatic sets theory （PST） was presented. During the solution process, polychromatic sets matrices were used to assign loading/unloading goods lo- cation reasonably in the particle position of particle swarm optimization （PSO） to increase the primary particles quali- ty, thus the search performance of the algorithm and optimize results can be improved. During the iterative process, parts of particles were reinitialized, so as to ensure the diversity of particles and avoid getting in local optimum. Com- pared with genetic algorithm and disperse particle swarm optimization （DPSO） through examples, the loading and unloading time is shorter, the convergence speed is faster and the iterative number is fewer, which verifies that the improved particle swarm optimization algorithm is feasible and efficacious in solving loading/unloading scheduling problem.

关 键 词：立体仓库 进出库调度 多色集合 离散粒子群 

分 类 号：TP278[自动化与计算机技术—检测技术与自动化装置] TP391[自动化与计算机技术—控制科学与工程]