A new cuckoo search algorithm with hybrid strategies for flow shop scheduling problems

作者：Wang, Hui^*; Wang, Wenjun; Sun, Hui; Cui, Zhihua; Rahnamayan, Shahryar; Zeng, Sanyou

来源：Soft Computing, 2017, 21(15): 4297-4307.

DOI：10.1007/s00500-016-2062-9

• Cuckoo search (CS)
• Flow shop scheduling problem
• Makespan
• Discrete optimization

Cuckoo search (CS) is a recently developed meta-heuristic algorithm, which has shown good performance on many continuous optimization problems. In this paper, we present a new CS algorithm, called NCS, for solving flow shop scheduling problems (FSSP). The NCS hybridizes four strategies: (1) The FSSP is a typical NP-hard problem with discrete characteristics. To deal with the discrete variables, the smallest position value (SPV) rule is employed to convert continuous solutions into discrete job permutations; (2) To generate high quality initial solutions, a new method based on the Nawaz-Enscore-Ham (NEH) heuristic is used for population initialization; (3) A modified generalized opposition-based learning (GOBL) is utilized to accelerate the convergence speed; and (4) To enhance the exploitation, a local search strategy is proposed. Experimental study is conducted on a set of Taillard's benchmark instances. Results show that NCS obtains better performance than the standard CS and some other meta-heuristic algorithms.

• 出版日期2017-8
• 单位中国地质大学（北京）; 南昌理工学院; 南京信息工程大学; 太原科技大学; 南昌工程学院