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

作　　者：王金鹏[1] 朱洪俊[1] 周俊[1] 

机构地区：[1]西南科技大学制造科学与工程学院,四川绵阳621010

出　　处：《计算机应用研究》2012年第2期442-444,526,共4页Application Research of Computers

摘　　要：为了验证最优子种群遗传算法在解决柔性流水车间调度问题时相比于传统遗传算法的优越性,分析了柔性流水车间调度问题的特点,并运用一种新的编码方法和新的遗传算法求解了该问题。考虑到最优个体保护策略法对复杂问题容易使种群收敛陷入局部最优解,为了提高精度、加快较优个体的产生并避免陷入局部最优解,首先提出了一种合理、全面的编码方法,并运用最优子种群遗传算法来求解柔性流水车间调度问题。最后运用实例验证了最优子种群遗传算法的有效性、优越性和编码方式的合理性。In order to verify the optimal 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 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]