最优子种群遗传算法求解柔性流水车间调度问题

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。

Optimal sub-population genetic algorithm for flexible flow shop scheduling problem

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.