Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (19): 263-270.DOI: 10.3778/j.issn.1002-8331.1805-0522

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Improved Hybrid Particle Swarm Optimization for Scheduling Optimization of Stereo Garage

CHEN Guilan, XI Baohua, YANG Lanying   

  1. College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu 610059, China
  • Online:2019-10-01 Published:2019-09-30



  1. 成都理工大学 核技术与自动化工程学院,成都 610059

Abstract: To further improve the stereo garage access efficiency, the improved hybrid particle swarm optimization is proposed, which is applied to optimize the stereo garage access period and sequencing. The main purpose of algorithm is to introduce the genetic algorithm in the early stage of the particle swarm optimization algorithm to improve the algorithm’s global search capability, the late introduction of the simulated annealing algorithm to make up for the weakness of the local search ability of the algorithm. Compared with current genetic algorithm, the improved hybrid particle swarm optimization has shown an improved efficiency of 24.5%~36.07% in the stereo garage access, the calculation results also demonstrate that the improved hybrid particle swarm optimization is superior than other scheduling algorithms in the stereo garage operation efficiency.

Key words: stereo garage scheduling optimization, improved hybrid particle swarm optimization, genetic algorithm, annealing algorithm

摘要: 为了进一步提高立体车库存取效率,提出一种改进混合粒子群算法,应用于立体车库存取策略时间模型,寻找存取车最优时间和最优排序。该算法主要在粒子群算法前期引入遗传算法,改善全局搜索能力,后期引入模拟退火算法弥补其局部搜索能力弱的特点。与目前应用于立体车库存取车调度的遗传算法相比,改进混合粒子群算法存取效率提高了24.5%~36.07%,并优于其他车库调度算法,提高了车库运营效率。

关键词: 立体车库调度优化, 改进混合粒子群算法, 遗传算法, 模拟退火算法