基于改进PSO-GA的设备点检路径优化  

Equipment spot inspection route optimization based on improved PSO-GA

作　　者：曹现刚[1] 姜韦光 张鑫媛 CAO Xian-gang;JIANG Wei-guang;ZHANG Xin-yuan(School of Mechanical Engineering,Xi’an University of Science and Technology,Xi’an 710054,China)

机构地区：[1]西安科技大学机械工程学院

出　　处：《计算机工程与设计》2019年第9期2677-2683,共7页Computer Engineering and Design

基　　金：国家自然科学基金项目(51875451)

摘　　要：为解决传统点检路径优化无法兼顾工作分配、地理跨度和路径最优等因素的问题,建立多目标协同优化的点检路径优化模型,并提出一种改进的粒子群遗传算法(PSO-GA)。采用基于k-d树的k-means算法确定初始粒子群;以PSO作为选择算子嵌入到GA中,重构PSO中的位置速度更新公式;针对点检路径问题与MTSP的主要区别,设计一套基于组的顺序交叉算子;引入2-opt算法作为局部搜索算子,优化迭代结果。实验结果表明,改进的PSO-GA求解高效,最优解质量优良,能够应用于设备点检路径优化。To solve the problem that the traditional spot inspection route optimization fails to strike a balance among work assignment,geographic span and route optimization,a multi-objective spot inspection route optimization model was built.And an improved PSO-GA was put forward.The initial particle swarm was determined using k-means based on k-d tree.The PSO was embedded into the GA as a selection operator,and the position speed update formula was refactored.Aiming at the main difference between the spot inspection route problem and MTSP,a group-based sequential simplex operator was designed.2-opt was introduced as the local search operator to optimize iteration results.Experimental results show that the performance of the PSO-GA is efficient,the optimal solution has high quality,and it can be applied to the equipment spot inspection route optimization.

关 键 词：点检路径问题 多旅行商问题 多目标优化 粒子群遗传算法 选择算子 交叉算子 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP307[自动化与计算机技术—控制科学与工程]