求解车间作业调度问题的一种改进遗传算法

针对标准遗传算法收敛速度慢和易陷入局部最优的问题,在总结已有经验的基础上对标准遗传算法提出改进:采用基于工序的编码、解码方式,每一次遗传操作后对种群采用循环选择并保留最优个体,对交叉操作和变异概率的计算提出了一系列改进方法,避免遗传算法产生无用解或陷入局部优化,以提高效率。通过实验验证,改进后的算法具有可行性,并且可以得到十分满意的结果。

An Improved Genetic Algorithm in Job-Shop Scheduling Problem

The standard genetic algorithm is slow and easy to fall into a local optimum.Based on the experiences an improved genetic algorithm was proposed in job shop scheduling problem:acquired on the basis of the standard genetic algorithm proposed improvements: coding and decoding method based on woke order,use cycle selection after each operating and save the optimal individual,improve the crossover and calculation methods of mutation probability.This can avoid useless solution or local optimization,so it will be more efficient.Experiments on job shop benchmarks show that this improved algorithm has good performance.