An Efficient Quantum Immune Algorithm for Hybrid Flow Shop to Minimize Mean Flow Time
This paper presents an efficient quantum immune algorithm (QIA) for HFSP.The objective is to find an optimal job sequence that minimize the mean flow time.Since HFSP has been proved to be NP-hard in a strong sense,immune algorithm (IA) and quantum algorithm (QA) are used to solve the problem,respectively.To improve the performance of IA,an effective IA,with new adaptive crossover and fractional parts mutation operators is proposed,which is called AIA.A randomly replacing strategy is employed to promote population diversity of QA,namely RRQA.In order to achieve better results,the paper proposes QIA,which combines IA with QA to optimize the performance of the HFSP.Furthermore,all the improvements are added into QIA to be ARRQIA.The simulation results show that the proposed AIA significantly enhances the performance of IA.RRQA also produces more efficient and more stable results than QA.So far as ARRQIA is concerned,it outperforms the other algorithms in the paper and the average solution quality has increased by 4.90％ and 12.50％ compared with IA and QA on the total 30 instances.
Qun Niu Taijin Zhou Minrui Fei
国际会议
北京
英文
2008-10-10（万方平台首次上网日期，不代表论文的发表时间）