粒子群果蝇混合改进算法在基站选址优化问题中的应用  

Application of Improved Hybrid Algorithm Based on PSO&FOA in Base Station Location Planning Problem

作　　者：刘娟[1] 杨春花[1] LIU Juan;YANG Chunhua(School of Information Engineering,Urumqi Vocational University,Urumqi 832000)

机构地区：[1]乌鲁木齐职业大学信息工程学院,乌鲁木齐832000

出　　处：《计算机与数字工程》2021年第7期1341-1345,1356,共6页Computer & Digital Engineering

基　　金：新疆维吾尔自治区自然科学基金项目(编号:2020D01A34);2020年广东省普通高校特色创新类项目(自然科学类)(编号:2020KTSCX163);全国高等院校计算机基础教育研究会计算机基础教育教学研究项目(编号:2020-AFCEC-338)资助。

摘　　要：基站选址优化是网络通讯必须要考虑的重要问题。为了最大程度地提升网络覆盖率,论文提出了一种粒子群果蝇混合优化改进算法。针对粒子群算法和果蝇优化算法都容易陷入局部最优的缺点,结合粒子群算法和蚁群算法的优化方式,构造了一种新的个体更新方式,并在更新流程中引入了遗传进化机制以更好地保持种群多样性。通过基准测试函数仿真对比实验和基站选址优化的对比实验结果可知,论文提出的粒子群果蝇混合优化算法具有较高的寻优性能,网络覆盖率更高,计算速度更快。Base station location planning is an important issue in network communication.In order to promote the network cov⁃erage,an improved hybrid optimization algorithm is proposed based on particle swarm optimization(PSO)and fruit fly optimization algorithm(FOA)in this paper.Firstly,in order to solve the problem easy to fall into local convergence for PSO and FOA effective⁃ly,a new individual updating method is constructed by combined with particle swarm optimization and fruit fly optimization algo⁃rithm.Secondly,to maintain the diversity of population effectively,genetic evolutionary mechanism is incorporated.Through the benchmark test function simulation comparison experiment and the comparison experiment results of base station location optimiza⁃tion,it can be seen that the particle swarm fruit fly hybrid optimization algorithm proposed in the paper has higher optimization per⁃formance,higher network coverage,and faster calculation speed.

关 键 词：基站选址优化 网络覆盖率 粒子群算法 果蝇优化算法 遗传进化机制 

分 类 号：U268.6[机械工程—车辆工程]