用双向收敛蚁群算法解作业车间调度问题

为了合理高效地调度资源，解决组合优化问题，在Job-Shop问题图形化定义的基础上，借鉴精英策略的思路，提出使用多种挥发方式的双向收敛蚁群算法，提高了算法的效率和可用性。最后，通过解决基准问题的实验，比较了双向收敛蚁群和蚁群算法的性能。实验结果表明，在不明显影响时间、空间复杂度的情况下，双向收敛蚁群算法可以加快收敛速度。

Bi-directional convergence ACO for job-shop scheduling

To properly and efficiently schedule resources and solve the combinatorial optimization problem, an improved algorithm named Bi-directional Convergence Ant Colony Optimization (ACO) algorithm was proposed. Using the graphic definition of Job-shop problem and the elitist strategy, the Bi-directional Convergence ACO algorithm was designed to improve efficiency and usability of original ACO by different evaporated means. Finally, the Bi-directional Convergence ACO algorithm was tested on a benchmark Job-shop scheduling problem. The performance of the Bi-directional Convergence ACO was also compared with that of the original ACO. The simulation result illustrates that the bi-directional convergence ACO algorithm accelerates the convergence without affecting the temporal and spatial complexity much.