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
参考文献:
[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 1.1038557e+01 1.2396654e+01 7.9835160e+01 1.2060154e+01 1.4689974e+01 7.9703710e+01 1.3065298e+01 1.6882604e+01 7.9605399e+01 1.4054500e+01 1.8977186e+01 7.9539976e+01 1.5028266e+01 2.0976364e+01 7.9507189e+01 1.5987105e+01 2.2882782e+01 7.9506789e+01 1.6931527e+01 2.4699083e+01 7.9538522e+01 1.7862038e+01 2.6427910e+01 7.9602140e+01 1.8779149e+01 2.8071906e+01 7.9697390e+01 1.9683367e+01 2.9633716e+01 7.9824022e+01 2.0575201e+01 3.1115982e+01 7.9981784e+01 2.1455159e+01 3.2521348e+01 8.0170425e+01 2.2323749e+01 3.3852458e+01 8.0389696e+01 2.3181481e+01 3.5111953e+01 8.0639343e+01 2.4028863e+01 3.6302479e+01 8.0919117e+01 2.4866402e+01 3.7426679e+01 8.1228766e+01 2.5694608e+01 3.8487195e+01 8.1568040e+01 2.6513989e+01 3.9486671e+01 8.1936686e+01 2.7325054e+01 4.0427751e+01 8.2334455e+01 2.8128311e+01 4.1313077e+01 8.2761096e+01 2.8924268e+01 4.2145294e+01 8.3216356e+01 2.9713434e+01 4.2927045e+01 8.3699985e+01 3.0496317e+01 4.3660973e+01 8.4211733e+01 3.1273427e+01 4.4349722e+01 8.4751347e+01 3.2045270e+01 4.4995934e+01 8.5318578e+01 3.2812356e+01 4.5602253e+01 8.5913173e+01 3.3575194e+01 4.6171324e+01 8.6534883e+01 3.4334291e+01 4.6705788e+01 8.7183455e+01 3.5090157e+01 4.7208290e+01 8.7858639e+01 3.5843299e+01 4.7681472e+01 8.8560184e+01 3.6594226e+01 4.8127979e+01 8.9287839e+01 3.7343447e+01 4.8550453e+01 9.0041352e+01 3.8091470e+01 4.8951539e+01 9.0820473e+01 3.8838803e+01 4.9333879e+01 9.1624951e+01 3.9585956e+01 4.9700116e+01 9.2454534e+01 4.0333436e+01 5.0052895e+01 9.3308972e+01 4.1081751e+01 5.0394858e+01 9.4188014e+01 4.1831412e+01 5.0728649e+01 9.5091408e+01 4.2582925e+01 5.1056912e+01 9.6018903e+01 4.3336799e+01 5.1382289e+01 9.6970249e+01 4.4093543e+01 5.1707425e+01 9.7945194e+01 4.4853666e+01 5.2034961e+01 9.8943488e+01 4.5617675e+01 5.2367543e+01 9.9964879e+01 4.6386079e+01 5.2707813e+01 1.0100912e+02 4.7159387e+01 5.3058415e+01 1.0207595e+02 4.7938107e+01 5.3421991e+01 1.0316512e+02 4.8722748e+01 5.3801187e+01 1.0427639e+02 4.9513818e+01 5.4198643e+01 1.0540951e+02 5.0311825e+01 5.4617006e+01 1.0656421e+02 5.1117279e+01 5.5058916e+01 1.0774025e+02 5.1930687e+01 5.5527019e+01 1.0893738e+02 5.2752557e+01 5.6023957e+01 1.1015535e+02 5.3583399e+01 5.6552374e+01 1.1139391e+02 5.4423722e+01 5.7114913e+01 1.1265280e+02 5.5274032e+01 5.7714218e+01 1.1393178e+02 5.6134839e+01 5.8352931e+01 1.1523059e+02 5.7006652e+01 5.9033697e+01 1.1654899e+02 5.7889978e+01 5.9759159e+01 1.1788671e+02 5.8785326e+01 6.0531959e+01 1.1924352e+02 5.9693205e+01 6.1354742e+01 1.2061916e+02 6.0614124e+01 6.2230151e+01 1.2201337e+02 6.1548590e+01 6.3160830e+01 1.2342591e+02 6.2497112e+01 6.4149421e+01 1.2485653e+02 6.3460198e+01 6.5198567e+01 1.2630498e+02 6.4438358e+01 6.6310914e+01 1.2777100e+02 6.5432099e+01 6.7489103e+01 1.2925434e+02 6.6441930e+01 6.8735778e+01 1.3075475e+02 6.7468359e+01 7.0053583e+01 1.3227199e+02 6.8511895e+01 7.1445161e+01 1.3380580e+02 6.9573047e+01 7.2913156e+01 1.3535592e+02 7.0652322e+01 7.4460210e+01 1.3692212e+02 7.1750230e+01 7.6088967e+01 1.3850413e+02 7.2867279e+01 7.7802071e+01 1.4010170e+02 7.4003977e+01 7.9602164e+01 1.4171460e+02 7.5160832e+01 8.1491891e+01 1.4334255e+02 7.6338354e+01 8.3473895e+01 1.4498532e+02 7.7537050e+01 8.5550819e+01 1.4664265e+02 7.8757429e+01 8.7725306e+01 1.4831430e+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/134734435
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