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

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 NPhard 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) Communicated by V. Loia. B Hui Wang [email protected] Wenjun Wang [email protected] Hui Sun [email protected] Zhihua Cui [email protected] Shahryar Rahnamayan [email protected] Sanyou Zeng [email protected] 1 School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China 2 School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China 3 School of Business Administration, Nanchang Institute of Technology, Nanchang 330099, China 4 School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China 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.