【改进灰狼优化算法】贪婪的非分层灰狼优化算法（Matlab代码实现）
?作者簡介：熱愛科研的Matlab仿真開發者，修心和技術同步精進，matlab項目合作可私信。
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智能優化算法 ?神經網絡預測 雷達通信? 無線傳感器
信號處理 圖像處理 路徑規劃 元胞自動機 無人機? 電力系統
? 內容介紹
針對基本灰狼優化(GWO)算法存在求解精度低、收斂速度慢、局部搜索能力差的問題,提出一種貪婪的非分層灰狼優化算法(IGWO)算法用于求解約束優化問題.
? 部分代碼
function z=Cost(x, jj)%%%%%%%%M, A, o,a, b, Abenchmark_func
global NFE
NFE=NFE+1;
Di=length(x);
[ps,D]=size(x);
% % %%%%%%%%%CEC2005:
% % %%%Shifted Sphere Function
if jj==1? ? k=[-39.3119000000000,58.8999000000000,-46.3224000000000,-74.6515000000000,-16.7997000000000,-80.5441000000000,-10.5935000000000,24.9694000000000,89.8384000000000,9.11190000000000,-10.7443000000000,-27.8558000000000,-12.5806000000000,7.59300000000000,74.8127000000000,68.4959000000000,-53.4293000000000,78.8544000000000,-68.5957000000000,63.7432000000000,31.3470000000000,-37.5016000000000,33.8929000000000,-88.8045000000000,-78.7719000000000,-66.4944000000000,44.1972000000000,18.3836000000000,26.5212000000000,84.4723000000000,39.1769000000000,-61.4863000000000,-25.6038000000000,-81.1829000000000,58.6958000000000,-30.8386000000000,-72.6725000000000,89.9257000000000,-15.1934000000000,-4.33370000000000,5.34300000000000,10.5603000000000,-77.7268000000000,52.0859000000000,40.3944000000000,88.3328000000000,-55.8306000000000,1.31810000000000,36.0250000000000,-69.9271000000000,-8.62790000000000,-56.8944000000000,85.1296000000000,17.6736000000000,6.15290000000000,-17.6957000000000,-58.9537000000000,30.3564000000000,15.9207000000000,-18.0082000000000,80.6411000000000,-42.3912000000000,76.2776000000000,-50.1652000000000,-73.5736000000000,28.3369000000000,-57.9905000000000,-22.7327000000000,52.0269000000000,39.2599000000000,10.8679000000000,77.8207000000000,66.0395000000000,-50.0667000000000,55.7063000000000,73.7141000000000,38.5296000000000,-56.7865000000000,-89.6477000000000,37.9576000000000,29.4720000000000,-35.4641000000000,-31.7868000000000,77.3235000000000,54.7906000000000,-48.2794000000000,74.2714000000000,72.6103000000000,62.9640000000000,-14.1446000000000,20.4923000000000,46.5897000000000,-83.6021000000000,-46.4809000000000,83.7373000000000,-79.6611000000000,24.3479000000000,-17.2303000000000,72.3404000000000,-36.4022000000000];
? ? o=k(1:Di);
? ? x=x-repmat(o,ps,1);
? ? z=sum(x.^2,2);
end
%? 2.Shifted Schwefel's Problem 1.2 (f13)% % %? ?x=x-repmat(o,ps,1);%z=z+sum((x(:,1:i)-o(:,1:i)),2).^2;
if jj==2
? ? k=
? ? o=k(1:Di);
? ? % x(:,1:Di)=(x(:,1:Di)-o(:,1:Di));
? ? x=x-repmat(o,ps,1);
? ? z=0;
? ? for i=1:Di
? ? ? ? z=z+sum((x(:,1:i)),2).^2;
? ? ? ??
? ? end
? ??
end
% % %? 3.Shifted Rotated High Conditioned Elliptic Function
if jj==3
? ? k=[-32.2013000000000,64.9776000000000,-38.3000000000000,-23.2582000000000,-54.0088000000000,86.6286000000000,-6.30090000000000,-49.3644000000000,5.34990000000000,52.2418000000000,-13.3643000000000,73.1263000000000,-8.56910000000000,-20.4915000000000,-60.1487000000000,16.0884000000000,-78.3319000000000,70.0387000000000,-6.85210000000000,-64.7970000000000,65.4005000000000,-26.0233000000000,-33.8757000000000,51.5893000000000,27.6427000000000,-69.4485000000000,25.5123000000000,-59.0782000000000,-66.5481000000000,-51.2733000000000,-81.7760000000000,-71.6572000000000,37.0810000000000,-63.4248000000000,-64.7785000000000,31.5299000000000,18.5387000000000,9.83420000000000,-0.603700000000000,1.73460000000000,70.1605000000000,-82.0391000000000,-42.7368000000000,-83.5930000000000,-85.0255000000000,41.1773000000000,4.16490000000000,-13.4505000000000,-0.310000000000000,-38.7944000000000,71.2702000000000,65.5320000000000,8.77530000000000,-55.4691000000000,-20.6252000000000,22.2901000000000,13.6798000000000,65.6745000000000,75.8418000000000,27.8926000000000,-15.0616000000000,-17.3036000000000,57.9346000000000,-86.6326000000000,65.0596000000000,47.3884000000000,29.1660000000000,65.5435000000000,3.46430000000000,-39.8140000000000,18.2261000000000,77.0446000000000,62.1882000000000,-11.4000000000000,-10.6218000000000,70.1276000000000,-40.8673000000000,-24.4451000000000,52.1398000000000,-10.5136000000000,29.2399000000000,2.17050000000000,44.0863000000000,81.7943000000000,80.0466000000000,88.3266000000000,16.6098000000000,-50.2573000000000,-71.6993000000000,71.5368000000000,61.4273000000000,-3.67390000000000,77.9428000000000,-22.3294000000000,64.7634000000000,-74.2823000000000,14.1899000000000,37.8473000000000,-77.7129000000000,28.9959000000000;];
? ? o=k(1:Di);
? ? %%%%%%%%%%D=30
? ? 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? 運行結果
? 參考文獻
[1]張森. 灰狼優化算法及其應用[D]. 廣西民族大學.
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