改进量子粒子群求解多目标柔性作业车间调度

Improved Quantum-behaved Particle Swarm Optimization for Solving Multi-objective Flexible Job-Shop Scheduling Problems

柔性作业车间调度问题(FJSP),由于其求解的复杂性,仍然是研究者们的研究热点。对基于不同的缩放系数选择策略的量子粒子群算法(QPSO)进行了比较研究,标准测试函数的仿真结果表明,自适应的缩放系数在单峰问题上优于其他选择策略;而余弦递减系数由于帮助粒子避免了陷入早熟而在多峰问题上表现比较好,故将其应用于求解多目标柔性作业车间调度问题(最大完工时间,最大机器工作时间,全部机器工作时间)。4个经典的仿真实例测试结果表明了算法的有效性和相较于其他算法的优越性。

Due to the complexity of flexible job-shop scheduling problem(FJSP), it is still the hot topic for research. FJSP was given deep insight into with three objectives to be minimized simultaneously: makespan, maximal machine workload and total workload. Quantum-behaved particle swarm optimization(QPSO) with different coefficient selection methods was compared. The benchmark function tests show that QPSO with adaptive coefficient outperforms other selection methods in unimodal functions, while QPSO with cosine coefficient performs better in multi-modal functions. Therefore, QPSO with cosine decreasing coefficient is adopted to solve the multi-objective FJSP, which is a complex multi-modal optimization problem. Simulation results of four representative FJSP examples indicate the effectiveness and efficiency of the proposed method.