An adaptive dynamic neighborhood crow search algorithm for solving permutation flow shop scheduling problems

To effectively solve the permutation flow-shop scheduling problem (PFSP), an adaptive dynamic neighborhood crow search algorithm (AdnCSA) is proposed to minimize the makespan. Firstly, a modified heuristic algorithm based on nawaz-enscore-ham (NEH) was proposed to improve the quality and diversity of the initial population. Secondly, the smallest-position-value (SPV) rule is used to encode the population so that it can handle the discrete scheduling problem. Lastly, the top 20% of individuals with best fitness was selected to execute neighborhood search, and an adaptive dynamic neighborhood structure is introduced to balance the global and local search ability of the proposed algorithm. To evaluate the effectiveness of the proposed method, the Rec and Taillard benchmarks were used to test the performance. Compared with nine recent metaheuristic method for solving the PFSP, the numerical results produced by the proposed AdnCSA are promising and show great potential for solving the permutation flow-shop scheduling problem.