改进的蛙跳算法在库存匹配中的研究

The Research of Improved Forehead Leapfrog Algorithm in Inventory Matching

针对生产订单库存匹配问题,提出一种改进的混洗蛙跳算法(SFLA)进行求解.采用随机分组策略,平衡各子群的寻优能力,保持种群多样性;打破最差蛙只向最优蛙学习的模式,引入Minkowski距离,使最差蛙借助更多同伴信息选择进化方向,增强种群适应性;针对最优蛙进化机会少,引入精英策略和变异思想更新其位置,避免陷入局部极小,加快收敛速度.仿真实验表明所建立模型的正确性和改进后算法的有效性.

According to the strategy, an improved forehead 1 problem of production orders inventory matching, by using random grouping eapfrog algorithm is put forward, which can balance the optimization of each subgroup and maintain the diversity of the population. Through breaking the mode and introducing Minkowski distance, the poor frog can only learn from the good frog, and select the evolutionary direction by learning from more companions and enhance the adaptability of the population. In order to solve the problem that the good frog has fewer opportunities to evolve, the elite strategy and variation ideas are introduced to update their locations, which can avoid falling into the local minimum and speed up the convergence. The simulation experiments showed the correctness of the established model and the effectiveness of the improved algorithm.