A Hybrid Crow Particle Optimization Algorithm to Solve Permutation Flow Shop Scheduling Problems

In this study, we proposed a memetic algorithm to solve permutation flow shop scheduling problems—the crow particle optimization (CPO) algorithm. The primary idea of CPO is to combine the crow search algorithm (CSA) and particle swarm optimization (PSO). To make the CPO can solve the permutation sequence encoding form. The smallest position value rule was used to convert a continuous sequence to a job sequence. To make the quality of the solutions, the Nawaz–Enscore–Ham heuristic was used for initializing an individual. Finally, a variable neighborhood search (VNS) was combined with the CPO algorithm to improve the quality of the solutions. Computational results revealed that CPO is better than PSO–VNS and CSA in terms of the makespan.