一、无人机模型简介:
单个无人机三维路径规划问题及其建模_IT猿手的博客-CSDN博客
参考文献:
[1]胡观凯,钟建华,李永正,黎万洪.基于IPSO-GA算法的无人机三维路径规划[J].现代电子技术,2023,46(07):115-120
二、Tiki-taka算法(TTA)简介
极致攻守算法(Tiki-Taka Algorithm,TTA)由Mohd Fadzil Faisae Ab. Rashid于2020年提出,该算法受tiki-taka 足球风格的短传、球员定位和保持控球的特点所启发。其旨在控制控球权并利用其战术优势击败对手,TTA算法新颖高效。单目标应用:Tiki-taka算法(TTA)求解太阳能光伏模型MATLAB_IT猿手的博客-CSDN博客
[1]Ab. Rashid, M.F.F. (2021), “Tiki-taka algorithm: a novel metaheuristic inspired by football playing style“, Engineering Computations, Vol. 38 No. 1, pp. 313-343. Tiki-taka algorithm: a novel metaheuristic inspired by football playing style | Emerald Insight
[2]Zamli, Kamal Z , Kader, et al. Selective chaotic maps Tiki-Taka algorithm for the S-box generation and optimization.
三、TTA求解无人机路径规划
(1)部分代码
close all clear clc addpath('./Algorithm/')%添加算法路径 warning off; %% 三维路径规划模型定义 global startPos goalPos N N=2;%待优化点的个数(可以修改) startPos = [10, 10, 80]; %起点(可以修改) goalPos = [80, 90, 150]; %终点(可以修改) SearchAgents_no=30; % 种群大小(可以修改) Function_name='F2'; %F1:随机产生地图 F2:导入固定地图 Max_iteration=50; %最大迭代次数(可以修改) % Load details of the selected benchmark function [lb,ub,dim,fobj]=Get_Functions_details(Function_name); [Best_score,Best_pos,curve]=TTA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);%算法优化求解 AlgorithmName='TTA';%算法名字 figure semilogy(curve,'Color','g','linewidth',3) xlabel('迭代次数'); ylabel('飞行路径长度'); legend(AlgorithmName) display(['算法得到的最优适应度: ', num2str(Best_score)]); Position=[Best_pos(1:dim/3); Best_pos(1+dim/3:2*(dim/3)); Best_pos(1+(2*dim/3):end)]'; %优化点的XYZ坐标(每一行是一个点) plotFigure(Best_pos,AlgorithmName)%画最优路径
(2)部分结果
1.0000000e+01 1.0000000e+01 8.0000000e+01 9.8730326e+00 1.0493499e+01 8.0724276e+01 9.8013981e+00 1.1013766e+01 8.1472324e+01 9.7839525e+00 1.1560279e+01 8.2243381e+01 9.8195523e+00 1.2132512e+01 8.3036681e+01 9.9070534e+00 1.2729942e+01 8.3851461e+01 1.0045312e+01 1.3352043e+01 8.4686955e+01 1.0233185e+01 1.3998292e+01 8.5542399e+01 1.0469528e+01 1.4668164e+01 8.6417028e+01 1.0753197e+01 1.5361136e+01 8.7310079e+01 1.1083048e+01 1.6076682e+01 8.8220786e+01 1.1457938e+01 1.6814278e+01 8.9148384e+01 1.1876724e+01 1.7573400e+01 9.0092109e+01 1.2338260e+01 1.8353524e+01 9.1051197e+01 1.2841403e+01 1.9154126e+01 9.2024883e+01 1.3385010e+01 1.9974680e+01 9.3012403e+01 1.3967937e+01 2.0814664e+01 9.4012991e+01 1.4589040e+01 2.1673551e+01 9.5025883e+01 1.5247175e+01 2.2550819e+01 9.6050315e+01 1.5941198e+01 2.3445943e+01 9.7085522e+01 1.6669965e+01 2.4358399e+01 9.8130740e+01 1.7432334e+01 2.5287661e+01 9.9185203e+01 1.8227159e+01 2.6233207e+01 1.0024815e+02 1.9053298e+01 2.7194511e+01 1.0131881e+02 1.9909606e+01 2.8171049e+01 1.0239642e+02 2.0794939e+01 2.9162297e+01 1.0348022e+02 2.1708155e+01 3.0167731e+01 1.0456945e+02 2.2648108e+01 3.1186826e+01 1.0566333e+02 2.3613656e+01 3.2219058e+01 1.0676111e+02 2.4603654e+01 3.3263903e+01 1.0786201e+02 2.5616958e+01 3.4320837e+01 1.0896529e+02 2.6652426e+01 3.5389334e+01 1.1007016e+02 2.7708912e+01 3.6468871e+01 1.1117586e+02 2.8785274e+01 3.7558924e+01 1.1228164e+02 2.9880367e+01 3.8658968e+01 1.1338672e+02 3.0993048e+01 3.9768478e+01 1.1449035e+02 3.2122173e+01 4.0886931e+01 1.1559175e+02 3.3266597e+01 4.2013802e+01 1.1669017e+02 3.4425178e+01 4.3148567e+01 1.1778483e+02 3.5596772e+01 4.4290702e+01 1.1887498e+02 3.6780234e+01 4.5439682e+01 1.1995984e+02 3.7974421e+01 4.6594983e+01 1.2103867e+02 3.9178189e+01 4.7756080e+01 1.2211068e+02 4.0390395e+01 4.8922450e+01 1.2317511e+02 4.1609894e+01 5.0093568e+01 1.2423121e+02 4.2835542e+01 5.1268909e+01 1.2527820e+02 4.4066197e+01 5.2447950e+01 1.2631532e+02 4.5300714e+01 5.3630166e+01 1.2734181e+02 4.6537949e+01 5.4815032e+01 1.2835691e+02 4.7776759e+01 5.6002025e+01 1.2935984e+02 4.9015999e+01 5.7190620e+01 1.3034984e+02 5.0254526e+01 5.8380293e+01 1.3132615e+02 5.1491197e+01 5.9570519e+01 1.3228801e+02 5.2724867e+01 6.0760775e+01 1.3323464e+02 5.3954392e+01 6.1950535e+01 1.3416530e+02 5.5178630e+01 6.3139276e+01 1.3507920e+02 5.6396435e+01 6.4326473e+01 1.3597558e+02 5.7606664e+01 6.5511602e+01 1.3685369e+02 5.8808174e+01 6.6694139e+01 1.3771275e+02 5.9999821e+01 6.7873559e+01 1.3855201e+02 6.1180460e+01 6.9049337e+01 1.3937069e+02 6.2348949e+01 7.0220951e+01 1.4016804e+02 6.3504142e+01 7.1387875e+01 1.4094329e+02 6.4644897e+01 7.2549584e+01 1.4169566e+02 6.5770070e+01 7.3705556e+01 1.4242441e+02 6.6878517e+01 7.4855265e+01 1.4312876e+02 6.7969093e+01 7.5998187e+01 1.4380796e+02 6.9040656e+01 7.7133798e+01 1.4446122e+02 7.0092062e+01 7.8261573e+01 1.4508780e+02 7.1122166e+01 7.9380988e+01 1.4568692e+02 7.2129825e+01 8.0491519e+01 1.4625783e+02 7.3113895e+01 8.1592642e+01 1.4679975e+02 7.4073232e+01 8.2683832e+01 1.4731193e+02 7.5006693e+01 8.3764565e+01 1.4779359e+02 7.5913134e+01 8.4834316e+01 1.4824398e+02 7.6791410e+01 8.5892562e+01 1.4866232e+02 7.7640379e+01 8.6938777e+01 1.4904786e+02 7.8458896e+01 8.7972438e+01 1.4939983e+02 7.9245818e+01 8.8993021e+01 1.4971747e+02 8.0000000e+01 9.0000000e+01 1.5000000e+02
四、完整MATLAB代码
原文地址:https://blog.csdn.net/weixin_46204734/article/details/134734482
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