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针对无能力限制的Lot-sizing问题,提出一种改进的离散粒子群优化算法.设计粒子编码为生产设备的调整状态,通过有效的解码程序将粒子解释为生产计划.区别于传统的粒子群算法,算法采用单切点交叉算子来提高算法的局部求精能力,并引入变异算子和速度扰动策略保持种群的多样性,使算法在局部求精和空间探索间取得了较好的平衡.在随机生成的90组测试实例中对算法性能进行仿真实验,结果表明该算法具有良好的性能.
Aiming at the Lot-sizing problem with limited capacity, an improved discrete particle swarm optimization algorithm is proposed. Particle coding is designed to adjust the production equipment and the particle is interpreted as a production plan through an effective decoding process. Different from the traditional particle swarm optimization The algorithm uses a single tangent crossover operator to improve the local refinement ability of the algorithm. The mutation operator and velocity perturbation strategy are introduced to maintain the diversity of the population, and a good balance between local refinement and spatial exploration is achieved. Simulation results of the algorithm in 90 randomly generated test cases show that the proposed algorithm has good performance.