Improved genetic algorithm for flexible flow shop scheduling

Aiming at the flexible flow shop scheduling problem that minimizes the maximum completion time, this paper proposes an improved genetic algorithm bases on multiple target of selection(MTGA). A one-dimensional encoding and decoding method for this problem is designed, and an opposing method is used to initialize the population. For the genetic algorithm, the crossover operation of the whole process is closer to the optimal solution, which accelerates the convergence speed of the algorithm, the overall variation of the operation sequence of all processes in the mutation operation, and the selection operation divides the population into multiple parts to achieve multiple optimal solutions, which increases the search range of the algorithm and reduces the probability of falling into the local optimal. Two sets of crossover and variation probabilities are applied to increase the flexibility of the algorithm. The effectiveness of the algorithm is verified by comparison with multiple existing algorithms.