一种多尺度协同变异的萤火虫粒子群混合算法及在车间调度中的应用

A Hybrid Firefly and Particle Swarm Optimization Algorithm with Multi-scale Cooperative Variation and Its Application in Shop Scheduling

摘要:

车间调度对于制造企业提高生产效率、降低生产成本具有重要的作用,针对单一优化算法在解决调度优化问题时存在的不足,探索求解速度和求解质量的均衡,提出了一种多尺度协同变异的萤火虫粒子群混合算法；引入动态自适应策略把种群分为两组,对两组族群平行进化,在保持种群多样性的同时提高求解速度；引入多尺度协同变异算子,利用不同大小方差的自适应高斯变异机制使种群以尽量分散的变异尺度来搜索解空间,通过混沌初始化种群进一步提高算法的局部检索能力；将提出的算法应用于函数优化和流水车间调度问题求解,实验结果显示,算法在求解效率、精度方面优于对比算法,具有较好的性能和应用价值。

Abstract:

Job shop scheduling plays an important role in improving production efficiency and reducing production cost for manufacturing enterprises, aiming at the shortcomings of a single opti-mization algorithm in solving scheduling optimization problems, this paper explores the balance be-tween solution speed and solution quality, and proposes a hybrid firefly and particle swarm optimi-zation algorithm with multi-scale cooperative variation; The dynamic adaptive strategy is introducedto divide the population into two groups, and the parallel evolution of the two groups can improv-e the solution speed while maintaining the diversity of the population; The multi-scale cooperative mutation operator is introduced, and the adaptive Gaussian mutation mechanism with different vari-ances is used to make the population search the solution space with the most dispersed mutation scale, and the local retrieval ability of the algorithm is further improved by initializing the popula-tion through chaos; The proposed algorithm is applied to function optimization and flow shop sch-eduling, the experimental results show that the algorithm is superior to the comparison algorithm in solving efficiency and accuracy, and has good performance and application value.