一种求解多目标分布式置换流水车间调度的改进混合樽海鞘群算法

An improved hybrid slap swarm algorithm for solving multi-objective distributed permutation flow shop scheduling

针对多目标分布式置换流水车间调度问题,以最小化最大加工时间与拖延时间为优化目标,提出一种改进混合樽海鞘群算法。位置更新方式中引入螺旋搜索机制和惯性权重,既有利于提高算法的搜索效率,又兼顾了算法全局搜索和局部搜索能力的平衡;为提高种群的多样性与算法的寻优能力,将算法融合Pareto支配关系的精英选择策略,并且在选择阶段加入差分进化机制防止算法陷入局部最优。通过使用基准算例对改进算法进行测试,验证了所提算法能够有效地求解多目标分布式置换流水车间调度问题。

Considering the multi-objective distributed permutation flow shop scheduling problem,an improved hybrid salp swarm algorithm is proposed,whose optimization goal is to minimize the maximum processing time and delay time.The introduction of the spiral search mechanism and inertia weight into the position update method not only helps to improve the search efficiency of the algorithm,but also takes into account the balance between the global search and local search functions of the algorithm to improve the diversity.Due to the population and optimization ability of the algorithm,the integrated Pareto algorithm dominates the elite selection strategy,and adds a differential evolution mechanism in the selection phase to prevent the algorithm from falling into local optimality.By using benchmark examples to test the improved algorithm,it is confirmed that the proposed algorithm can effectively solve the multi-objective distributed permutation flow planning problem.