基于多种优化算法的物联网无人机基站研究【布谷鸟搜索CS、大象群体优化EHO、灰狼优化GWO、帝王蝴蝶优化MBO、鲨鱼群算法SSA和粒子群优化PSO】(Matlab代码实现)

📅 2026/6/30 12:14:10
基于多种优化算法的物联网无人机基站研究【布谷鸟搜索CS、大象群体优化EHO、灰狼优化GWO、帝王蝴蝶优化MBO、鲨鱼群算法SSA和粒子群优化PSO】(Matlab代码实现)
欢迎来到本博客❤️❤️博主优势博客内容尽量做到思维缜密逻辑清晰为了方便读者。完整资源、论文复现、期刊合作、论文辅导及科研仿真定制事宜点击本文完整资源下载⛳️座右铭行百里者半于九十。⛳️赠与读者‍做科研涉及到一个深在的思想系统需要科研者逻辑缜密踏实认真但是不能只是努力很多时候借力比努力更重要然后还要有仰望星空的创新点和启发点。建议读者按目录次序逐一浏览免得骤然跌入幽暗的迷宫找不到来时的路它不足为你揭示全部问题的答案但若能解答你胸中升起的一朵朵疑云也未尝不会酿成晚霞斑斓的别一番景致万一它给你带来了一场精神世界的苦雨那就借机洗刷一下原来存放在那儿的“躺平”上的尘埃吧。或许雨过云收神驰的天地更清朗.......第一部分——内容介绍摘要下一代物联网NG-IoT应用的出现为第六代6G移动网络引入了诸多挑战如大规模连接、增加的网络容量和极低的延迟。为了应对上述挑战超密集网络已被广泛认为是一种可能的解决方案。然而基站BSs的密集部署并不总是可行或经济高效的。无人机基站DBSs可以促进网络扩展并有效应对NG-IoT的要求。此外由于其灵活性它们可以在紧急情况下提供按需连接或应对网络流量的临时增加。然而由于有限的能量储备和空地链路中信号质量降低DBS的最佳位置的确定并非易事。为此群体智能方法可能是在三维3D空间中确定DBS的最佳位置的吸引人解决方案。在这项工作中我们探讨了著名的群体智能方法包括布谷鸟搜索CS、大象群体优化EHO、灰狼优化GWO、帝王蝴蝶优化MBO、鲨鱼群算法SSA和粒子群优化PSO并研究它们在解决上述问题中的性能和效率。具体而言我们研究了在不同群体智能方法存在的情况下的三个场景的性能。此外我们进行了非参数统计测试即弗里德曼和威尔科克森测试以比较不同的方法。详细文章见第4部分。基于多种优化算法的物联网无人机基站DBSs研究是一个复杂且前沿的领域它结合了物联网技术、无人机技术和多种优化算法旨在解决下一代物联网NG-IoT应用中的挑战如大规模连接、增加的网络容量和极低的延迟。以下是对布谷鸟搜索CS、大象群体优化EHO、灰狼优化GWO、帝王蝴蝶优化MBO、鲨鱼群算法SSA和粒子群优化PSO在物联网无人机基站研究中的应用概述1. 布谷鸟搜索CS布谷鸟搜索算法通过模拟布谷鸟的寄生育雏行为来优化问题。在物联网无人机基站研究中CS算法可以用于确定无人机基站的最佳位置以最大化网络覆盖并最小化路径损耗。CS算法的随机游走特性有助于在搜索空间中寻找全局最优解。2. 大象群体优化EHO大象群体优化算法模拟了象群在寻找食物和迁徙时的行为。在无人机基站研究中EHO算法可以用于解决三维空间中的基站部署问题通过模拟象群的行为来快速有效地找到最优或次优的基站位置。此外EHO算法还可以应用于无人机路径规划特别是在复杂地形下的避障和三维航迹规划。3. 灰狼优化GWO灰狼优化算法通过模拟灰狼的社会等级和狩猎行为来优化问题。在无人机基站研究中GWO算法可以用于确定基站的最佳位置以应对网络容量的增加和延迟的降低。GWO算法的全局搜索和局部搜索能力使其能够在复杂的搜索空间中找到最优解。此外GWO算法的变种如多种群灰狼优化算法MP-GWO和灰狼-布谷鸟优化算法CS-GWO也在无人机路径规划中展现出强大的应用潜力。4. 帝王蝴蝶优化MBO帝王蝴蝶优化算法是一种仿生优化算法其灵感来源于帝王蝶的迁徙行为。在无人机基站研究中MBO算法可以用于解决三维航迹规划问题特别是在复杂地形下规划出满足安全性和效率要求的航迹。MBO算法通过模拟帝王蝶的迁徙行为将优化问题转化为搜索问题并在搜索空间中寻找最优解。5. 鲨鱼群算法SSA鲨鱼群算法是一种模拟鲨鱼群体行为的优化算法。在物联网无人机基站研究中SSA算法可以用于解决基站部署和路径规划问题。通过模拟鲨鱼群体的捕食和协作行为SSA算法能够在复杂的搜索空间中快速找到最优或次优解。6. 粒子群优化PSO粒子群优化算法是一种基于群体智能的优化算法通过模拟鸟群觅食行为来优化问题。在无人机基站研究中PSO算法可以用于确定基站的最佳位置以最大化网络覆盖并减少路径损耗。PSO算法通过不断更新粒子的位置和速度来逼近最优解具有结构简单、易于实现等优点。总结基于多种优化算法的物联网无人机基站研究是一个多学科交叉的领域它结合了物联网技术、无人机技术和多种优化算法的优势。通过应用这些优化算法可以更加高效地解决物联网无人机基站部署和路径规划中的复杂问题为下一代物联网应用提供更加可靠和高效的网络支持。未来随着算法的不断优化和无人机技术的不断发展这些方法将在更多领域得到广泛应用。第二部分——运行结果部分代码%Scenarios 2 and 3x[1:10]; %x-axis vectorurban_pathloss(1,:)CS_avg_pathloss(1,:);urban_pathloss(2,:)EHO_avg_pathloss(1,:);urban_pathloss(3,:)GWO_avg_pathloss(1,:);urban_pathloss(4,:)MBO_avg_pathloss(1,:);urban_pathloss(5,:)SSA_avg_pathloss(1,:);urban_pathloss(6,:)PSO_avg_pathloss(1,:);figureplot(x,urban_pathloss)legend(CS, EHO, GWO, MBO, SSA, PSO)xlabel(Number of DBSs)ylabel(Average Pathloss)title(Average pathloss as a function of the number of DBSs in urban environment)suburban_pathloss(1,:)CS_avg_pathloss(2,:);suburban_pathloss(2,:)EHO_avg_pathloss(2,:);suburban_pathloss(3,:)GWO_avg_pathloss(2,:);suburban_pathloss(4,:)MBO_avg_pathloss(2,:);suburban_pathloss(5,:)SSA_avg_pathloss(2,:);suburban_pathloss(6,:)PSO_avg_pathloss(2,:);figureplot(x,suburban_pathloss)legend(CS, EHO, GWO, MBO, SSA, PSO)xlabel(Number of DBSs)ylabel(Average Pathloss)title(Average pathloss as a function of the number of DBSs in suburban environment)dense_pathloss(1,:)CS_avg_pathloss(3,:);dense_pathloss(2,:)EHO_avg_pathloss(3,:);dense_pathloss(3,:)GWO_avg_pathloss(3,:);dense_pathloss(4,:)MBO_avg_pathloss(3,:);dense_pathloss(5,:)SSA_avg_pathloss(3,:);dense_pathloss(6,:)PSO_avg_pathloss(3,:);figureplot(x,dense_pathloss)legend(CS, EHO, GWO, MBO, SSA, PSO)xlabel(Number of DBSs)ylabel(Average Pathloss)title(Average pathloss as a function of the number of DBSs in dense-urban environment)highrise_pathloss(1,:)CS_avg_pathloss(4,:);highrise_pathloss(2,:)EHO_avg_pathloss(4,:);highrise_pathloss(3,:)GWO_avg_pathloss(4,:);highrise_pathloss(4,:)MBO_avg_pathloss(4,:);highrise_pathloss(5,:)SSA_avg_pathloss(4,:);highrise_pathloss(6,:)PSO_avg_pathloss(4,:);figureplot(x,highrise_pathloss)legend(CS, EHO, GWO, MBO, SSA, PSO)xlabel(Number of DBSs)ylabel(Average Pathloss)title(Average pathloss as a function of the number of DBSs in high-rise urban environment)% Coverage probability%Urban environment - 1 , 90dB - 1urban_coverage(1,:)CS_avg_coverage(1,1,:);urban_coverage(2,:)EHO_avg_coverage(1,1,:);urban_coverage(3,:)GWO_avg_coverage(1,1,:);urban_coverage(4,:)MBO_avg_coverage(1,1,:);urban_coverage(5,:)SSA_avg_coverage(1,1,:);urban_coverage(6,:)PSO_avg_coverage(1,1,:);figureplot(x,urban_coverage)legend(CS, EHO, GWO, MBO, SSA, PSO)xlabel(Number of DBSs)ylabel(Coverage Probability)title(Coverage probability as a function of the number of DBSs in urban environment (T90dB))%Urban environment - 1 , 100dB - 2urban_coverage(1,:)CS_avg_coverage(2,1,:);urban_coverage(2,:)EHO_avg_coverage(2,1,:);urban_coverage(3,:)GWO_avg_coverage(2,1,:);urban_coverage(4,:)MBO_avg_coverage(2,1,:);urban_coverage(5,:)SSA_avg_coverage(2,1,:);urban_coverage(6,:)PSO_avg_coverage(2,1,:);figureplot(x,urban_coverage)legend(CS, EHO, GWO, MBO, SSA, PSO)xlabel(Number of DBSs)ylabel(Coverage Probability)title(Coverage probability as a function of the number of DBSs in urban environment (T100dB))%Urban environment - 1 , 110dB - 3urban_coverage(1,:)CS_avg_coverage(3,1,:);urban_coverage(2,:)EHO_avg_coverage(3,1,:);urban_coverage(3,:)GWO_avg_coverage(3,1,:);urban_coverage(4,:)MBO_avg_coverage(3,1,:);urban_coverage(5,:)SSA_avg_coverage(3,1,:);urban_coverage(6,:)PSO_avg_coverage(3,1,:);figureplot(x,urban_coverage)legend(CS, EHO, GWO, MBO, SSA, PSO)xlabel(Number of DBSs)ylabel(Coverage Probability)title(Coverage probability as a function of the number of DBSs in urban environment (T110dB))%Urban environment - 1 , 120dB - 4urban_coverage(1,:)CS_avg_coverage(4,1,:);urban_coverage(2,:)EHO_avg_coverage(4,1,:);urban_coverage(3,:)GWO_avg_coverage(4,1,:);urban_coverage(4,:)MBO_avg_coverage(4,1,:);urban_coverage(5,:)SSA_avg_coverage(4,1,:);urban_coverage(6,:)PSO_avg_coverage(4,1,:);figureplot(x,urban_coverage)legend(CS, EHO, GWO, MBO, SSA, PSO)xlabel(Number of DBSs)ylabel(Coverage Probability)title(Coverage probability as a function of the number of DBSs in urban environment (T120dB))%Suburban environment - 1 , 90dB - 1suburban_coverage(1,:)CS_avg_coverage(1,2,:);suburban_coverage(2,:)EHO_avg_coverage(1,2,:);suburban_coverage(3,:)GWO_avg_coverage(1,2,:);suburban_coverage(4,:)MBO_avg_coverage(1,2,:);suburban_coverage(5,:)SSA_avg_coverage(1,2,:);suburban_coverage(6,:)PSO_avg_coverage(1,2,:);figureplot(x,suburban_coverage)legend(CS, EHO, GWO, MBO, SSA, PSO)xlabel(Number of DBSs)ylabel(Coverage Probability)title(Coverage probability as a function of the number of DBSs in suburban environment (T90dB))%Suburban environment - 1 , 100dB - 2suburban_coverage(1,:)CS_avg_coverage(2,2,:);suburban_coverage(2,:)EHO_avg_coverage(2,2,:);suburban_coverage(3,:)GWO_avg_coverage(2,2,:);suburban_coverage(4,:)MBO_avg_coverage(2,2,:);suburban_coverage(5,:)SSA_avg_coverage(2,2,:);suburban_coverage(6,:)PSO_avg_coverage(2,2,:);figureplot(x,suburban_coverage)legend(CS, EHO, GWO, MBO, SSA, PSO)xlabel(Number of DBSs)ylabel(Coverage Probability)title(Coverage probability as a function of the number of DBSs in suburban environment (T100dB))%Suburban environment - 1 , 110dB - 3suburban_coverage(1,:)CS_avg_coverage(3,2,:);suburban_coverage(2,:)EHO_avg_coverage(3,2,:);suburban_coverage(3,:)GWO_avg_coverage(3,2,:);suburban_coverage(4,:)MBO_avg_coverage(3,2,:);suburban_coverage(5,:)SSA_avg_coverage(3,2,:);suburban_coverage(6,:)PSO_avg_coverage(3,2,:);figureplot(x,suburban_coverage)legend(CS, EHO, GWO, MBO, SSA, PSO)xlabel(Number of DBSs)ylabel(Coverage Probability)title(Coverage probability as a function of the number of DBSs in suburban environment (T110dB))%Suburban environment - 1 , 120dB - 4suburban_coverage(1,:)CS_avg_coverage(4,2,:);suburban_coverage(2,:)EHO_avg_coverage(4,2,:);suburban_coverage(3,:)GWO_avg_coverage(4,2,:);suburban_coverage(4,:)MBO_avg_coverage(4,2,:);suburban_coverage(5,:)SSA_avg_coverage(4,2,:);suburban_coverage(6,:)PSO_avg_coverage(4,2,:);figureplot(x,suburban_coverage)legend(CS, EHO, GWO, MBO, SSA, PSO)xlabel(Number of DBSs)ylabel(Coverage Probability)title(Coverage probability as a function of the number of DBSs in suburban environment (T120dB))%Dense urban environment - 1 , 90dB - 1dense_coverage(1,:)CS_avg_coverage(1,3,:);dense_coverage(2,:)EHO_avg_coverage(1,3,:);dense_coverage(3,:)GWO_avg_coverage(1,3,:);dense_coverage(4,:)MBO_avg_coverage(1,3,:);dense_coverage(5,:)SSA_avg_coverage(1,3,:);dense_coverage(6,:)PSO_avg_coverage(1,3,:);figureplot(x,dense_coverage)legend(CS, EHO, GWO, MBO, SSA, PSO)xlabel(Number of DBSs)ylabel(Coverage Probability)title(Coverage probability as a function of the number of DBSs in dense urban environment (T90dB))%Dense urban environment - 1 , 100dB - 2dense_coverage(1,:)CS_avg_coverage(2,3,:);dense_coverage(2,:)EHO_avg_coverage(2,3,:);dense_coverage(3,:)GWO_avg_coverage(2,3,:);dense_coverage(4,:)MBO_avg_coverage(2,3,:);dense_coverage(5,:)SSA_avg_coverage(2,3,:);dense_coverage(6,:)PSO_avg_coverage(2,3,:);figureplot(x,dense_coverage)legend(CS, EHO, GWO, MBO, SSA, PSO)xlabel(Number of DBSs)ylabel(Coverage Probability)title(Coverage probability as a function of the number of DBSs in dense urban environment (T100dB))%Dense urban environment - 1 , 110dB - 3dense_coverage(1,:)CS_avg_coverage(3,3,:);dense_coverage(2,:)EHO_avg_coverage(3,3,:);dense_coverage(3,:)GWO_avg_coverage(3,3,:);dense_coverage(4,:)MBO_avg_coverage(3,3,:);dense_coverage(5,:)SSA_avg_coverage(3,3,:);dense_coverage(6,:)PSO_avg_coverage(3,3,:);figureplot(x,dense_coverage)legend(CS, EHO, GWO, MBO, SSA, PSO)xlabel(Number of DBSs)ylabel(Coverage Probability)title(Coverage probability as a function of the number of DBSs in dense urban environment (T110dB))%Dense urban environment - 1 , 120dB - 4dense_coverage(1,:)CS_avg_coverage(4,3,:);dense_coverage(2,:)EHO_avg_coverage(4,3,:);dense_coverage(3,:)GWO_avg_coverage(4,3,:);dense_coverage(4,:)MBO_avg_coverage(4,3,:);dense_coverage(5,:)SSA_avg_coverage(4,3,:);dense_coverage(6,:)PSO_avg_coverage(4,3,:);figureplot(x,dense_coverage)legend(CS, EHO, GWO, MBO, SSA, PSO)xlabel(Number of DBSs)ylabel(Coverage Probability)title(Coverage probability as a function of the number of DBSs in dense urban environment (T120dB))%High-rise urban environment - 1 , 90dB - 1highrise_coverage(1,:)CS_avg_coverage(1,4,:);highrise_coverage(2,:)EHO_avg_coverage(1,4,:);highrise_coverage(3,:)GWO_avg_coverage(1,4,:);highrise_coverage(4,:)MBO_avg_coverage(1,4,:);highrise_coverage(5,:)SSA_avg_coverage(1,4,:);highrise_coverage(6,:)PSO_avg_coverage(1,4,:);figureplot(x,highrise_coverage)legend(CS, EHO, GWO, MBO, SSA, PSO)xlabel(Number of DBSs)ylabel(Coverage Probability)title(Coverage probability as a function of the number of DBSs in high-rise urban environment (T90dB))%High-rise urban environment - 1 , 100dB - 2highrise_coverage(1,:)CS_avg_coverage(2,4,:);highrise_coverage(2,:)EHO_avg_coverage(2,4,:);highrise_coverage(3,:)GWO_avg_coverage(2,4,:);highrise_coverage(4,:)MBO_avg_coverage(2,4,:);highrise_coverage(5,:)SSA_avg_coverage(2,4,:);highrise_coverage(6,:)PSO_avg_coverage(2,4,:);figureplot(x,highrise_coverage)legend(CS, EHO, GWO, MBO, SSA, PSO)xlabel(Number of DBSs)ylabel(Coverage Probability)title(Coverage probability as a function of the number of DBSs in high-rise urban environment (T100dB))%High-rise urban environment - 1 , 110dB - 3highrise_coverage(1,:)CS_avg_coverage(3,4,:);highrise_coverage(2,:)EHO_avg_coverage(3,4,:);highrise_coverage(3,:)GWO_avg_coverage(3,4,:);highrise_coverage(4,:)MBO_avg_coverage(3,4,:);highrise_coverage(5,:)SSA_avg_coverage(3,4,:);highrise_coverage(6,:)PSO_avg_coverage(3,4,:);figureplot(x,highrise_coverage)legend(CS, EHO, GWO, MBO, SSA, PSO)xlabel(Number of DBSs)ylabel(Coverage Probability)title(Coverage probability as a function of the number of DBSs in high-rise urban environment (T110dB))%High-rise urban environment - 1 , 120dB - 4highrise_coverage(1,:)CS_avg_coverage(4,4,:);highrise_coverage(2,:)EHO_avg_coverage(4,4,:);highrise_coverage(3,:)GWO_avg_coverage(4,4,:);highrise_coverage(4,:)MBO_avg_coverage(4,4,:);highrise_coverage(5,:)SSA_avg_coverage(4,4,:);highrise_coverage(6,:)PSO_avg_coverage(4,4,:);figureplot(x,highrise_coverage)legend(CS, EHO, GWO, MBO, SSA, PSO)xlabel(Number of DBSs)ylabel(Coverage Probability)title(Coverage probability as a function of the number of DBSs in high-rise urban environment (T120dB))第三部分——参考文献文章中一些内容引自网络会注明出处或引用为参考文献难免有未尽之处如有不妥请随时联系删除。(文章内容仅供参考具体效果以运行结果为准)​​​​​​第四部分——本文完整资源下载资料获取更多粉丝福利MATLAB|Simulink|Python|数据|文档等完整资源获取本文完整资源下载