计算机应用研究2012，Vol.29Issue(2)：442-444,526,4.DOI:10.3969/j.issn.1001-3695.2012.02.009
最优子种群遗传算法求解柔性流水车间调度问题
Optimal sub-population genetic algorithm for flexible flow shop scheduling problem
王金鹏 1朱洪俊 1周俊1
作者信息
- 1. 西南科技大学制造科学与工程学院,四川绵阳621010
- 折叠
摘要
Abstract
In order to verify the optima] sub-population genetic algorithm is better than traditional genetic algorithm in solving the flexible flow shop scheduling problem, this paper analyzed the characteristics of the flexible flow shop scheduling problem, and applied a new coding method and the new genetic algorithm to solve the problem. This paper considered the protection strategy method of optimal individual made populations easy to converge to a local optimal solution in solving complex issues. In order to improve accuracy, speed up the production of better individuals and avoid falling into local optimal solution, this paper proposed a rational, comprehensive encoding method and used optimal sub-population genetic algorithm to solve flexible flow shop scheduling problem. Finally, the use of examples verifies the effectiveness and superiority of optimal sub-population genetic algorithm and the rationality of coding.关键词
柔性流水车间/最优子种群遗传算法/最优个体保护策略法/编码方法Key words
flexible flow shop scheduling/ optimal sub-population genetic algorithm/ protection strategies method of optimal individual/ encoding method分类
信息技术与安全科学引用本文复制引用
王金鹏,朱洪俊,周俊..最优子种群遗传算法求解柔性流水车间调度问题[J].计算机应用研究,2012,29(2):442-444,526,4.
