求解物流配送中心选址问题的改进鸽群算法  

Improved pigeons algorithm for solving location problem of logistics distribution center

作　　者：韦修喜 魏超 黄华娟[1] WEI Xiuxi;WEI Chao;HUANG Huajuan(College of Artificial Intelligence,Guangxi,Nanning,Guangxi 530006,China;College of Electronic Information,Guangxi,Nanning,Guangxi 530006,China)

机构地区：[1]广西民族大学人工智能学院,广西南宁530006 [2]广西民族大学电子信息学院,广西南宁530006

出　　处：《燕山大学学报》2023年第2期175-188,共14页Journal of Yanshan University

基　　金：国家自然科学基金资助项目(62266007,61662005);广西自然科学基金资助项目(2021GXNSFAA220068,2018GXNSFAA294068)。

摘　　要：为了提高鸽群优化算法求解物流配送中心选址问题的优化效果,减少物流配送成本,提出了一种改进的鸽群优化算法。该算法在基础鸽群优化算法上,引入灰狼优化算法在寻优过程中的捕食策略,能够有效地提高鸽群优化算法的局部搜索能力、增强算法的寻优性能。由函数测试实验可得,该算法在求解测试函数最优值上具有寻优速度快、收敛精度高的特点。最后,将其应用到求解物流配送中心选址问题中,实验结果表明:改进的鸽群优化算法更适合求解高维物流配送中心选址问题。In order to improve the optimization effect of pigeon group optimization algorithm for solving the location problem of logistics distribution center and reduce the cost of logistics distribution,an improved pigeon group optimization algorithm was proposed.In addition to the basic flock optimization algorithm,the predation strategy of the grey wolf optimization algorithm is introduced,which can effectively improve the local search ability and enhance the search performance of the algorithm.According to the benchmark function test experiment,WPIO has the characteristics of fast optimization speed and high convergence accuracy in solving the optimal value of test function.Finally,WPIO is applied to solve the location problem of logistics distribution center.The experimental results show that the improved pigeon swarm optimization algorithm is more suitable for solving the location problem of high-dimensional logistics distribution center.

关 键 词：物流配送中心选址 局部最优 捕食策略 基础测试函数 寻优性能 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]