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??博主優(yōu)勢(shì)：??????博客內(nèi)容盡量做到思維縝密，邏輯清晰，為了方便讀者。
??座右銘：行百里者，半于九十。
??????本文目錄如下：??????
目錄
??1 概述
??2 運(yùn)行結(jié)果
??3?參考文獻(xiàn)
??4 Matlab代碼、數(shù)據(jù)、文章下載
??1 概述
摘要：
下一代物聯(lián)網(wǎng)（NG-IoT）應(yīng)用的出現(xiàn)為第六代（6G）移動(dòng)網(wǎng)絡(luò)引入了諸多挑戰(zhàn)，如大規(guī)模連接、增加的網(wǎng)絡(luò)容量和極低的延遲。為了應(yīng)對(duì)上述挑戰(zhàn)，超密集網(wǎng)絡(luò)已被廣泛認(rèn)為是一種可能的解決方案。然而，基站（BSs）的密集部署并不總是可行或經(jīng)濟(jì)高效的。無人機(jī)基站（DBSs）可以促進(jìn)網(wǎng)絡(luò)擴(kuò)展并有效應(yīng)對(duì)NG-IoT的要求。此外，由于其靈活性，它們可以在緊急情況下提供按需連接或應(yīng)對(duì)網(wǎng)絡(luò)流量的臨時(shí)增加。然而，由于有限的能量儲(chǔ)備和空地鏈路中信號(hào)質(zhì)量降低，DBS的最佳位置的確定并非易事。為此，群體智能方法可能是在三維（3D）空間中確定DBS的最佳位置的吸引人解決方案。在這項(xiàng)工作中，我們探討了著名的群體智能方法，包括布谷鳥搜索（CS）、大象群體優(yōu)化（EHO）、灰狼優(yōu)化（GWO）、帝王蝴蝶優(yōu)化（MBO）、鯊魚群算法（SSA）和粒子群優(yōu)化（PSO），并研究它們?cè)诮鉀Q上述問題中的性能和效率。具體而言，我們研究了在不同群體智能方法存在的情況下的三個(gè)場(chǎng)景的性能。此外，我們進(jìn)行了非參數(shù)統(tǒng)計(jì)測(cè)試，即弗里德曼和威爾科克森測(cè)試，以比較不同的方法。詳細(xì)文章見第4部分。
??2 運(yùn)行結(jié)果
部分代碼：
%Scenarios 2 and 3
x=[1:10]; %x-axis vector
urban_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,:);
figure
plot(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,:);
figure
plot(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,:);
figure
plot(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,:);
figure
plot(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 - 1
urban_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,:);
figure
plot(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 (T=90dB)")
%Urban environment - 1 , 100dB - 2
urban_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,:);
figure
plot(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 (T=100dB)")
%Urban environment - 1 , 110dB - 3
urban_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,:);
figure
plot(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 (T=110dB)")
%Urban environment - 1 , 120dB - 4
urban_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,:);
figure
plot(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 (T=120dB)")
%Suburban environment - 1 , 90dB - 1
suburban_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,:);
figure
plot(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 (T=90dB)")
%Suburban environment - 1 , 100dB - 2
suburban_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,:);
figure
plot(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 (T=100dB)")
%Suburban environment - 1 , 110dB - 3
suburban_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,:);
figure
plot(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 (T=110dB)")
%Suburban environment - 1 , 120dB - 4
suburban_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,:);
figure
plot(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 (T=120dB)")
%Dense urban environment - 1 , 90dB - 1
dense_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,:);
figure
plot(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 (T=90dB)")
%Dense urban environment - 1 , 100dB - 2
dense_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,:);
figure
plot(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 (T=100dB)")
%Dense urban environment - 1 , 110dB - 3
dense_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,:);
figure
plot(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 (T=110dB)")
%Dense urban environment - 1 , 120dB - 4
dense_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,:);
figure
plot(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 (T=120dB)")
%High-rise urban environment - 1 , 90dB - 1
highrise_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,:);
figure
plot(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 (T=90dB)")
%High-rise urban environment - 1 , 100dB - 2
highrise_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,:);
figure
plot(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 (T=100dB)")
%High-rise urban environment - 1 , 110dB - 3
highrise_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,:);
figure
plot(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 (T=110dB)")
%High-rise urban environment - 1 , 120dB - 4
highrise_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,:);
figure
plot(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 (T=120dB)")
??3?參考文獻(xiàn)
文章中一些內(nèi)容引自網(wǎng)絡(luò)，會(huì)注明出處或引用為參考文獻(xiàn)，難免有未盡之處，如有不妥，請(qǐng)隨時(shí)聯(lián)系刪除。文章來源：http://www.zghlxwxcb.cn/news/detail-842337.html
OSCAR LIJEN HSU 1 AND CHE-RUNG LEE2文章來源地址http://www.zghlxwxcb.cn/news/detail-842337.html
??4 Matlab代碼、數(shù)據(jù)、文章下載
到了這里，關(guān)于基于多種優(yōu)化算法的物聯(lián)網(wǎng)無人機(jī)基站研究【布谷鳥搜索CS、大象群體優(yōu)化EHO、灰狼優(yōu)化GWO、帝王蝴蝶優(yōu)化MBO、鯊魚群算法SSA和粒子群優(yōu)化PSO】（Matlab代碼實(shí)現(xiàn)）的文章就介紹完了。如果您還想了解更多內(nèi)容，請(qǐng)?jiān)谟疑辖撬阉鱐OY模板網(wǎng)以前的文章或繼續(xù)瀏覽下面的相關(guān)文章，希望大家以后多多支持TOY模板網(wǎng)！
