For the flexible job shop scheduling problem with the objective of minimizing the maximum make-time, an adaptive discrete particle swarm algorithm is proposed. The algorithm adopts an initialization method that combines random generation and process-based global load minimum selection machine initialization. At the same time, in order to improve the convergence speed of the algorithm, an adaptive inertia weight is added to the particle position update method, and crossover and mutation operations are introduced. Through comparative experiments and numerical analysis of benchmark examples, the effectiveness of the proposed adaptive hybrid discrete particle swarm optimization algorithm is verified.
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