蜉蝣算法在供应链库存优化中的应用

Application of Mayfly Algorithm to Supply Chain Inventory Optimization

针对蜉蝣算法(mayfly algorithm,MA)全局搜索能力差、搜索精度不高和自适应能力弱等问题,提出一种多策略融合的蜉蝣算法。首先,提出吸引力增强因子,同时引入自适应动态调节的重力系数,来平衡搜索和开发能力;其次,提出中值位置作为群体位置的一部分,加强种群交流,避免陷入局部最优;最后,引入正弦余弦策略,增强全局搜索能力,提高收敛精度并增强稳定性。8种典型功能函数的仿真结果证明改进后的算法收敛能力提高、收敛精度加强。将改进后的蜉蝣算法应用于工程中,在供应链库存系统中调节比例-积分-微分(proportion-integration-differentiation,PID)参数,与其他算法相比,成本下降9.5%,证明该算法在工程上具有适用性。

Aiming at the problems of poor global search ability,low search accuracy and weak adaptive ability of mayfly algorithm(MA),a multi-strategy mayfly algorithm was proposed.Firstly,an adaptive and dynamically adjusted gravity coefficient was constructed and an attractive enhancement factor was introduced to balance the search and exploitation capabilities.Secondly,the median position was proposed as a part of the population position to strengthen population communication and avoid falling into the locally optimal solution.Finally,the sines and cosines strategy was introduced to enhance the global search ability,improve the convergence precision and enhance the stability.The simulation results of 8 typical functions show that the convergence ability and convergence accuracy of the improved algorithm are improved.When the modified mayflies algorithm is applied to engineering,the cost of adjusting the proportion-integration-differentiation(PID)parameter in the supply chain inventory system is reduced by 9.5%compared with other algorithms,which proves that the algorithm is applicable in engineering.