蛙跳优化算法求解多目标无等待流水线调度

Shuffled frog-leaping algorithm for multi-objective no-wait flowshop scheduling

提出了基于Pareto边界和档案集的改进蛙跳算法,解决以最大完工时间、最大拖后时间和总流经时间为目标值的无等待流水线调度问题.首先,采用NEH(Nawaz-Enscore-Ham)启发式与随机解相结合的初始化方法,保证了初始群体的质量和分布性;其次,采用两点交叉方法生成新解,使蛙跳算法能够直接用于解决调度问题;再次,利用非支配解集动态更新群体,改善了群体的质量和多样性;最后,将基于插入邻域的快速局部搜索算法嵌入到蛙跳算法中,增强了算法的开发能力和效率.仿真试验表明了所得蛙跳算法的有效性和高效性.

An enhanced shuffled frog-leaping algorithm（ESFLA） is presented based on the Pareto front and archive set for solving the no-wait flowshop scheduling problems with makespan, maximum tardiness and total flow time criteria. Firstly, an initialization method based on the NEH（Nawaz-Enscore-Ham） heuristic is designed. Secondly, a two-point crossover operator is used to produce a new individual. Thirdly, a part of the non-dominated solutions are added to the population to improve their diversity and quality. Finally, to further enhance the exploitation capability and efficiency of the algorithm, a fast local search algorithm based on the insert neighborhood is embedded in the proposed shuffled frogleaping algorithm. The computational results and comparisons show that the proposed ESFLA is effective and efficient in finding better solutions for the problem considered.