Improved Genetic Algorithm for Job Shop Scheduling Problems
一种求解Job Shop 调度问题的改进遗传算法

Traditional Genetic Algorithm for solving Job Shop Scheduling Problems has some shortcomings such as slow convergence and easy to bring immature convergence. On the basis of Virus Evolutionary Genetic Algorithm （VEGA） and Genetic Algorithm with Catastrophe factor, an improved Virus Evolutionary Genetic Algorithm with Catastrophe factor （IVEGA-C） was proposed. IVEGA-C adds virus infection operation and catastrophe operation to the basic structure of traditional Genetic Algorithm. Virus infection operation passes the evolutionary information between the populations in the same generation and an improved extinction operation was used as the strategy of catastrophe. The improved algorithm speeded up the convergence rate of the Genetic algorithm, avoided the premature phenomena and to fall into local optimal scheduling solution. The simulation results verify that IVEGA-C on solving the Job Shop Scheduling Problems is better than traditional Genetic Algorithm and VEGA. At last we give an example of using this algorithm to solve scheduling problems in our real-world.