混合粒子群算法求解单机批调度问题

摘要

In order to minimize the maximum tardiness of the single machine batch scheduling based on dy-namic workpiece arriving,a hybrid particle swarm optimization(HPSO)was proposed.In the algorithm,hav-ing a sine adjustment of inertia weight designed to improve both convergence speed and global convergence, and then,having adaptive mutation global extremal algorithm used to enhance HPSO’s ability of jumping out of local optima.Solving problems in experimental design with the improved algorithm proves its effectiveness.%设计了一种混合粒子群算法（Hybrid Particle Swarm Optimization，HPSO）以求解基于工件动态到达的最小化最大拖期时间单机批调度问题。该算法在标准粒子群算法的基础上引入了惯性权重正弦调整，以改善标准粒子群算法的收敛速度和全局收敛性，然后采用自适应变异全局极值算法增强粒子群优化算法跳出局部最优解的能力，防止算法陷入局部最优。应用改进的算法对实验设计问题进行求解，证明了改进算法的有效性。