一种用于车间调度的基于熵的混合遗传算法

为提高车间调度算法的寻优性能，通过对模拟退火遗传算法收敛图的研究，提出了评价算法种群有序性（差异性）的种群熵，基于种群熵，提出了改进的模拟退火遗传算法，该混合算法通过种群熵动态地改变算法的交叉和变异概率，使之适应种群的变化，提高种群的多样性，有效地克服算法的过早收敛，从而达到提高算法寻优性能的目的。仿真实例表明，所提出的算法的寻优性能有了显著的提高。

A Hybrid Genetic Algorithm Based on Entropy for Job-Shop Scheduling

To improve the searching performance of job-shop scheduling algorithm, by analyzing the convergent graph of the simulated annealing genetic algorithm, the population entropy is suggested to evaluate the order of the algorithm population, and based on which a modified simulated annealing genetic algorithm is proposed. The cross probability and mutation probability change dynamically with the population entropy in the proposed algorithm, such that the diversity of the population is increased, premature convergence overcome, and algorithm searching performance improved. Simulation results show that the proposed algorithm has great superiority in searching performance.