面向柔性作业车间调度的多策略鲸鱼优化算法

以某大型家具企业的柔性生产制造过程中调度问题为研究对象,提出了一种主要用于求解柔性作业车间调度问题的多策略鲸鱼优化算法(multi-strategy whale optimization algorithm, MWOA),首先,为了提高初始种群的多样性,引入混沌理论来初始化种群;同时设计了非线性收敛因子和自适应惯性权重系数来平衡全局探索和局部开发能力;然后结合差分进化(differential evolution, DE)算子提高了WOA的利用和搜索能力,最后采取最优个体混沌搜索策略,减少WOA算法出现早熟收敛现象的概率.以最小化最大完工时间为求解目标,对基准测试问题与某家具企业的生产制造过程的调度优化问题进行了求解,结果表明提出来的多策略鲸鱼优化算法克服了基本鲸鱼优化算法寻优精度低、收敛速度慢及容易陷入局部最优等缺陷,与对比算法比较,取得了更好的寻优效果.

Multi-strategy Whale Optimization Algorithm for Flexible Job Shop Scheduling

Taking the scheduling problem in the flexible manufacturing of a large furniture enterprise as the research object, this study proposes a multi-strategy whale optimization algorithm (MWOA), which is mainly used to solve the flexible job shop scheduling problem. First, in order to improve the diversity of the initial population, chaos theory is introduced to initialize the population; at the same time, the nonlinear convergence factor and adaptive inertia weight coefficient are designed to balance the global exploration and local development capabilities; then the differential evolution (DE) operator is used to improve the utilization and search ability of WOA. Finally, the optimal individual chaotic search strategy is adopted to reduce the probability of premature convergence of WOA. With the objective of minimizing the maximum completion time, the benchmark test problem and the scheduling optimization problem of the manufacturing process of a furniture enterprise are solved. The results show that MWOA overcomes the shortcomings of the basic WOA, such as low optimization accuracy, slow convergence speed, and easy falling into local optimization. Compared with the comparison algorithm, MWOA achieves better optimization results.