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首页> 外文期刊>International Journal of Computer Integrated Manufacturing >An improved particle swarm optimisation with a linearly decreasing disturbance term for flow shop scheduling with limited buffers
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An improved particle swarm optimisation with a linearly decreasing disturbance term for flow shop scheduling with limited buffers

机译：具有线性递减干扰项的改进粒子群优化算法，用于有限缓冲区的流水车间调度

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摘要

The flow shop scheduling problem with limited buffers is a typical combinational optimisation problem that is NP-hard. In this article, an improved particle swarm optimisation with a linearly decreasing disturbance term (LDPSO) is presented for permutation flow shop scheduling with limited buffers between consecutive machines to minimise the maximum completion time (i.e. the makespan). A linearly decreasing disturbance term was added to the velocity, updating formula of the standard particle swarm optimisation algorithm. The decision probability of the linearly decreasing disturbance term was used to control the utilisation of the global exploration operation and the local exploitation search based on problem-specific information so as to prevent premature convergence and concentrate computing efforts on promising neighbour solutions. Theoretical analysis based on previous studies showed that the improved algorithm converged to the global optimum at a probability of 1. The ranked-order-value encoded method transferred the continuous particle position of the LDPSO to the order sequence. Furthermore, the neighbour search strategy based on block guaranteed that the entire order sequence could be searched. Simulation results and comparisons based on benchmarks demonstrate the effectiveness of the LDPSO. The effects of buffer size and decision probability on optimisation performance are discussed in this article.
机译：具有有限缓冲区的流水车间调度问题是一个典型的NP优化组合优化问题。在本文中，提出了一种具有线性减少扰动项（LDPSO）的改进的粒子群优化算法，用于在连续机器之间使用有限缓冲区的置换流水车间调度，以最大程度地缩短了最大完成时间（即制造期）。将线性减小的扰动项添加到速度中，更新了标准粒子群优化算法的公式。线性减少扰动项的决策概率用于控制基于问题特定信息的全局勘探操作和局部开采搜索的利用率，从而防止过早收敛并将计算工作集中在有希望的邻居解决方案上。基于先前研究的理论分析表明，改进算法以1的概率收敛于全局最优。排序阶值编码方法将LDPSO的连续粒子位置转移到阶序列上。此外，基于块的邻居搜索策略保证了可以搜索整个订单序列。仿真结果和基于基准的比较证明了LDPSO的有效性。本文讨论了缓冲区大小和决策概率对优化性能的影响。

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