Abstract
An improved hybrid particle swarm optimization algorithm based on adaptive inertia weight is proposed for job shop scheduling problem(JSSP). In order to overcome the shortcomings of the standard particle swarm optimization algorithm(PSO) in the optimization process, such as slow iteration speed, low precision and easy to fall into local optimal, simulated annealing algorithm(SA) and PSO are combined(SAPSO); In order to balance the global search ability and local search ability of particles, the adaptive weight of each particle is dynamically adjusted in this paper. The improved hybrid algorithm utilizes the probabilistic mutation capability of SA, which can accept both the good solution and the bad solution with a certain probability when accepting the new solution, and can get rid of the local optimal solution of the algorithm, which not only improves the flexibility and diversity of the algorithm, but also improves the diversity of particles. Adding adaptive weight to improve inertia weight in PSO makes it possible to dynamically adjust parameter factors according to fitness. Finally, the feasibility and efficiency of the improved algorithm are verified by the standard examples of JSSP.
Published Version
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