An Improved Adaptive Genetic Algorithm for Job-Shop Scheduling Problem
An Improved Adaptive Genetic Algorithm for Job-Shop Scheduling Problem
复制标题
DOI：
10.1109/icnc.2007.202
复制
发表时间：
2007-08
期刊：
影响因子：
--
通讯作者：
Y. Xing;Zhentong Chen;Jing Sun;Long Hu
中科院分区：
文献类型：
--
作者：
Y. Xing;Zhentong Chen;Jing Sun;Long Hu
为了更好地解决作业车间调度问题(JSSP)，提出了一种经过一定改进的自适应遗传算法。将改进的sigmoid函数应用到自适应遗传算法中得到的改进自适应遗传算法(IAGA)。而在JSSP的IAGA中，算法的适应度用作业的完成时间来表示。因此，该算法使交叉概率和变异概率随完成时间自适应、非线性地调整，可以避免早熟收敛、收敛速度慢、稳定性差等缺点。实验结果表明，该遗传算法不易陷入局部最优，收敛速度快，实现简单。几个例子证明了所提出的 JSSP 遗传算法的有效性。
An adaptive genetic algorithm with some improvement is proposed to solve the job-shop scheduling problem (JSSP) better. The improved adaptive genetic algorithm (IAGA) obtained by applying the improved sigmoid function to adaptive genetic algorithm. And in IAGA for JSSP, the fitness of algorithm is represented by completion time of jobs. Therefore, this algorithm making the crossover and mutation probability adjusted adaptively and nonlinearly with the completion time, can avoid such disadvantages as premature convergence, low convergence speed and low stability. Experimental results demonstrate that the proposed genetic algorithm does not get stuck at a local optimum easily, and it is fast in convergence, simple to be implemented. Several examples testify the effectiveness of the proposed genetic algorithm for JSSP.