基于候鸟优化算法的阻塞流水车间调度问题

Blocking flow shop scheduling problem based on migrating birds optimization

针对以最小化总流程时间为目标的阻塞流水车间调度问题,提出一种有效的候鸟优化算法。采用最小最大算法产生初始鸟群中的领飞鸟,并以领飞鸟的邻域解作为初始鸟群中的其他个体,保证了初始鸟群的质量和多样性。通过最优插入+最优交换操作产生鸟群的邻域解,使算法能更快地搜索到高质量的解。基于迭代贪婪算法的毁坏和构造操作的局部搜索策略进一步增强了算法的局部寻优能力,使算法在集中搜索和分散搜索之间达到更合理的平衡。通过求解经典的Taillard基准算例验证了所提算法的高效性和鲁棒性。

Aiming at the blocking flow shop scheduling problem with total flow time minimization, an effective Migrating Birds Optimization （MBO） algorithm was proposed. In the proposed MBO algorithm, to guarantee the quality and diversity of the initial population, Combination of MinMax and Nawaz-Enscore-Ham（MME） algorithm was introduced to generate the leader and the neighbors of leader were regarded as the rest of population. Through the best-insert and the best-swap operator, the neighbors were constructed to easily find promising neighboring solutions. A local search procedure based on IG algorithm was added to enhance the MBO＇s intensification capability. To validate the performance of proposed MBO al- gorithm, computational experiments were conducted on the classic Taillard＇s benchmarks. The computational results dem- onstrated the effectiveness and robustness of the proposed algorithm.