Aiming at the shortcomings of traditional swarm intelligent optimization algorithms for solving flexible job shop scheduling problems such as being prone to falling into local optimizations and insufficient optimiza-tion capabilities,a discrete carnivorous plant algorithm is proposed with the goal of minimizing the maximum completion time.Firstly,three initial population strategies are proposed in order to improve the diversity of the initial population.Secondly,an adaptive strategy for growth factors was designed in order to improve the search ability of the algorithm at each stage,and crossover and greedy mutation operations based on four neighborhood structures were performed on plants.Finally,the Brandimarte benchmark problem is simulated and compared with other literature algorithms.It is proved that the proposed algorithm has good performance in terms of convergence speed and solution quality.
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