摘要
求解Job Shop调度问题是个NP完全问题,为了提高遗传算法的性能,提出一种新的自适应遗传算法(NSGA)以解决Job Shop调度问题。采用活动调度解码方法、过滤个体适应度相同的筛选策略、改进自适应交叉变异概率等改进策略来提高算法性能,最后通过仿真比较分析证明该算法的先进性。
It is well known that Job Shop scheduling problem is a Non-Polynomial （NP） complete problem. For improving the performance of Genetic Algorithm （GA）, a new self-adaptive genetic algorithm （NSGA） was proposed to solve Job Shop scheduling problem. The improved methods included decoding the active scheduling, filtering the individual of the same fitness value, and improving self adaptive probability. Finally, the NSGA was tested on 8 famous Car benchmarks. The simulation results show that the improved algorithm is more effective with comparison of the normal genetic algorithm.
出处
《计算机应用》
CSCD
北大核心
2009年第B12期161-164,188,共5页
journal of Computer Applications
关键词
自适应遗传算法
作业车间调度
算法改进
adaptive Genetic Algorithm （GA）
Job Shop scheduling
improvement algorithm
