超多目标优化算法是一类专门用于解决存在三个以上目标函数的最优化问题的算法。这类问题在现实世界中非常常见,例如在工程设计、资源管理、机器学习等领域。由于目标之间的冲突性,很难找到一个单一的解来同时优化所有目标,因此超多目标优化算法旨在找到一组解,这些解在目标之间提供了最佳的权衡,即所谓的Pareto最优解集。
在超多目标优化问题中,算法的设计和性能评估面临额外的挑战。例如,随着目标数量的增加,非支配解之间的区分变得更加困难,这可能导致算法的多样性和收敛性难以平衡。此外,高维目标空间中的解的分布和选择也更加复杂。超多目标优化算法的研究是一个活跃且不断发展的领域,随着计算能力的提高和新算法的提出,未来有望在更多实际问题中得到应用。
多模态无人机路径规划是一种复杂的优化问题,它要求为无人机规划出在多种目标条件下的最优路径。这些目标可能包括最短飞行时间、最短路径长度、最少能耗、避开障碍物等。解决这类问题通常需要采用高级的算法和策略。
dummy_output = CostFunction(struct('x', ones(1, model.n), 'y', ones(1, model.n), 'z', ones(1, model.n)));
nObj = numel(dummy_output); % Determine the number of objectives
MaxIt = 300; % Maximum Number of Iterations
nPop = 100; % Population Size (Swarm Size)
nRep = 50; % Repository Size
w = 1; % Inertia Weight
wdamp = 0.98; % Inertia Weight Damping Ratio
c1 = 1.5; % Personal Learning Coefficient
c2 = 1.5; % Global Learning Coefficient
nGrid = 5; % Number of Grids per Dimension
alpha = 0.1; % Inflation Rate
beta = 2; % Leader Selection Pressure
gamma = 2; % Deletion Selection Pressure
mu = 0.5; % Mutation Rate
delta = 20; % delta = num(rep)/10
%% Initialization
% Create Empty Particle Structure
empty_particle.Position=[];
empty_particle.Velocity=[];
empty_particle.Cost=[];
empty_particle.Best.Position=[];
empty_particle.Best.Cost=[];
empty_particle.IsDominated = [];
empty_particle.GridIndex = [];
empty_particle.GridSubIndex = [];
% Initialize Global Best
GlobalBest.Cost=Inf(nObj,1); % Minimization problem
% Create an empty Particles Matrix, each particle is a solution (searching path)
particle=repmat(empty_particle,nPop,1);
isInit = false;
% tic;
while (~isInit)
disp('Initialising...');
for i=1:nPop
% Initialize Position
particle(i).Position=CreateRandomSolution(VarSize,VarMin,VarMax);
% Initialize Velocity
particle(i).Velocity.r=zeros(VarSize);
particle(i).Velocity.psi=zeros(VarSize);
particle(i).Velocity.phi=zeros(VarSize);
% Evaluation
particle(i).Cost= CostFunction(SphericalToCart2(particle(i).Position,model));
% Update Personal Best
particle(i).Best.Position=particle(i).Position;
particle(i).Best.Cost=particle(i).Cost;
% Update Global Best
if Dominates(particle(i).Best.Cost,GlobalBest.Cost)
GlobalBest=particle(i).Best;
isInit = true;
end
end
end
场景一:
场景二:
参考文献:
[1]T.N. Duong, D.-N. Bui, M.D. Phung. Navigation Variable-based Multi-objective Particle Swarm Optimization for UAV Path Planning with Kinematic Constraints
原文链接:https://blog.csdn.net/weixin_46204734/article/details/142445703
标签:particle,超多,算法,Cost,empty,无人机,Position,Best,MATLAB From: https://blog.csdn.net/weixin_46204734/article/details/142465024