摘要
针对作业车间调度中应用遗传算法求解存在的早熟问题,对其搜索速度、收敛效果和最优解等方面进行分析研究,给出一种新的混合遗传算法。首先对初始种群进行实数编码,增加解空间中可行解的个数;接着根据距离排列,增加种群的多样性;然后采用拉普拉斯交叉算子和逆转变异,改进算法的搜索效率;最后结合模拟退火算法,并在每一代遗传进化中引入局部搜索,提高了算法的全局寻优能力。通过与其他算法的仿真比较,结果表明新的混合算法能提高多目标车间调度问题的求解速度和质量,并能够找到最佳的调度方案。
Aiming at the premature problem of applying genetic algorithm in job shop scheduling, this paper analyzes and studies its search speed, convergence effect and optimal solution, and presents a new hybrid genetic algorithm. First, the initial population is real-coded to increase the number of feasible solutions in the solution space. Then arrange the population according to the distance to increase the diversity of the population. And the Laplacian crossover operator and inverse transformation are used to improve the search efficiency of the algorithm. Finally, the simulated annealing algorithm is combined, and local search is introduced in each generation of genetic evolution, which improves the global optimization ability of the algorithm. Compared with other algorithms, the results show that the new hybrid algorithm can improve the speed and quality of multi-objective shop scheduling problems and find the best scheduling solution.
引文
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