基于GMOGSO的多目标流水车间调度问题

针对缓冲区有限的多目标流水车间调度问题, 提出一种基于Pareto 最优的广义多目标萤火虫算法. 通过引入交换子和交换序将基本萤火虫算法离散化, 并将算法拓展为全局搜索过程和局部搜索过程. 进化初期采用全局搜索将种群推向较优区域, 进化中后期采用捕食搜索策略使算法主体在全局搜索和局部搜索间智能切换, 从而保证全局与局部的平衡. 动态变步长策略进一步增强了算法搜索能力. 通过算例测试验证了所提出算法的有效性.

Multi-objective flow shop scheduling problem based on GMOGSO

To deal with the multi-objective flow shop scheduling with limited buffer, a Pareto-based general multi-objective glowworm swarm optimization(GMOGSO) algorithm is proposed. The concepts of the swap operator and swap sequence are introduced to make the continuous GSO discrete. To balance the convergence speed and accuracy, the GSO algorithm is developed into two different processes including the global optimization one and local optimization one. The global optimization process is used to improve the quality of the initial population, then the predatory search strategy is used to coordinate local and global exploration. The method of variant step length enhances the exploratory capability. The proposed GMOGSO algorithm is compared to other algorithms, and the results show the effectiveness of the proposed algorithm.