基于熵的混合粒子群算法在柔性调度中的应用

为了更好地求解大规模柔性车间调度问题,提高柔性车间调度算法的寻优性能,提出一种基于熵的混合粒子群算法.该算法把粒子群算法、遗传算法和模拟退火算法相结合,同时用种群熵自适应调节惯性系数和变异概率,以增强算法的寻优能力和克服算法的过早收敛.典型实例仿真结果表明,该算法能更好地求解柔性车间调度问题,与传统的优化算法相比,在优化精度上具有明显的优越性.

Hybrid Particle Swarm Optimization Based on Entropy for Flexible Job Shop Scheduling Problems

In order to better solve large-scale flexible shop scheduling problems and improve the searching performance of flexible shop scheduling algorithms,a hybrid particle swarm optimization(HPSO) algorithm based on entropy was proposed,which combines the particle swarm optimization,genetic algorithm with simulated annealing algorithm,and the inertia factor and mutation probability were adjusted adaptively according to population entropy in order to enhance the searching ability of the algorithm and overcome the premature convergence of the algorithm.Simulation results on benchmark instances have shown that the proposed algorithm can solve flexible shop scheduling problems,and has obvious advantages in the accuracy of optimization over traditional optimization algorithms.