求解作业车间调度问题的改进遗传算法

使用遗传算法求解作业车间调度问题时,为了获得最优解,提高算法的收敛速度,提出了改进遗传算法.算法以最小化最大完工时间为优化目标,初始化时将种群规模扩大为原来的两倍以增加种群多样性;迭代时使用新的适应度函数让染色体间更易区分;通过轮盘赌法完成染色体选择;用POX(Precedence Operation Crossove...

Improved Genetic Algorithm for Job Shop Scheduling Problem

When a genetic algorithm is used to solve job shop scheduling, in order to obtain the optimal solution and increase the convergence speed of the algorithm, we propose an improved genetic algorithm in this study. The goal of the algorithm is to minimize the maximum completion time. First, the population size is doubled during the initialization to increase the diversity of the population and a new fitness function is adopted to make chromosome distinguishing easier in the iteration. Then, chromosomes are selected via roulette. Furthermore, crossover is completed by Precedence Operation Crossover (POX) and mutation by Reciprocal Exchange Mutation (REM). Finally, the optimization ability and convergence speed of the proposed algorithm are improved by adjusting the crossover and mutation probability with self-regulation. The simulation results show that the improved genetic algorithm has faster convergence, stronger optimization ability, and better optimal solution than the traditional one and thus it is more suitable for the processing and production in job shops.