摘要
针对多AGV(Automated Guided Vehicle)进行调度时,使用整数编码的大种群遗传算法收敛速度较慢的问题,提出一种改进适应度函数的遗传算法。该算法定义了一个适应度函数的改进模板,通过静态或动态调节选择压力的方法在模板中设计了三种修正函数,以对算法迭代过程中的选择压力进行控制,使算法的择优能力加强。提出的改进算法在固定节点多AGV调度问题上进行了仿真实验,实验结果表明改进后的遗传算法具有更好的优化结果,收敛代数分布更小,且算法收敛所需的迭代次数约仅为改进前的1/5。
Aimed at the slow convergence speed of the large population genetic algorithm(GA)using integer coding when scheduling multiple automated guided vehicles(AGV),a genetic algorithm with improved fitness function is proposed.To enhance the optimization ability of the improved algorithm,the algorithm defined an improving template of fitness function,and designed three correction functions in the template by static or dynamic adjusting selection pressure to control the selection pressure in the iterative process of the GA.The simulation results on the fixed node multi AGV scheduling problem show that the improved genetic algorithm has better optimization results,more concentrated distribution trend of convergence number,and the number of iterations required for convergence is only 1/5 of the original one.
作者
刘洋
曹立佳
杨旭
Liu Yang;Cao Lijia;Yang Xu(School of Automation and Engineering,Sichuan University of Science&Engineering,Yibin 644000,Sichuan,China;Artificial Intelligence Key Laboratory of Sichuan Province,Zigong 643000,Sichuan,China)
出处
《计算机应用与软件》
北大核心
2024年第4期86-89,105,共5页
Computer Applications and Software
基金
四川省科技计划项目(2018GZDZX0046,2020YJ0368)
自贡市重点科技计划项目(2019YYJC03)
四川理工学院人才引进项目(2018RCL18)。
关键词
适应度函数
遗传算法
收敛
AGV调度
Fitness function
Genetic algorithm
Convergence
AGV scheduling
