智能优化算法二维路径规划天牛须算法粒子群算法matlab源代码

clc; clear; close all; tic %% Problem Definition model=CreateModel(); model.n=2; % number of Handle Points CostFunction=@(x) MyCost(x,model); % Cost Function nVar=model.n; % Number of Decision Variables VarSize=[1 nVar]; % Size of Decision Variables Matrix VarMin.x=model.xmin; % Lower Bound of Variables VarMax.x=model.xmax; % Upper Bound of Variables VarMin.y=model.ymin; % Lower Bound of Variables VarMax.y=model.ymax; % Upper Bound of Variables %% PSO Parameters MaxIt=100; % Maximum Number of Iterations nPop=30; % Population Size (Swarm Size) w=1; % Inertia Weight wdamp=0.98; % Inertia Weight Damping Ratio c1=1; % Personal Learning Coefficient c2=2; % Global Learning Coefficient % % Constriction Coefficient % phi1=2.05; % phi2=2.05; % phi=phi1+phi2; % chi=2/(phi-2+sqrt(phi^2-4*phi)); % w=chi; % Inertia Weight % wdamp=1; % Inertia Weight Damping Ratio % c1=chi*phi1; % Personal Learning Coefficient % c2=chi*phi2; % Global Learning Coefficient alpha=0.1; VelMax.x=alpha*(VarMax.x-VarMin.x); % Maximum Velocity VelMin.x=-VelMax.x; % Minimum Velocity VelMax.y=alpha*(VarMax.y-VarMin.y); % Maximum Velocity VelMin.y=-VelMax.y; % Minimum Velocity %% Initialization % Create Empty Particle Structure empty_particle.Position=[]; empty_particle.Velocity=[]; empty_particle.Cost=[]; empty_particle.Sol=[]; empty_particle.Best.Position=[]; empty_particle.Best.Cost=[]; empty_particle.Best.Sol=[]; % Initialize Global Best GlobalBest.Cost=inf; % Create Particles Matrix particle=repmat(empty_particle,nPop,1); % Initialization Loop for i=1:nPop % Initialize Position if i > 1 particle(i).Position=CreateRandomSolution(model); else % Straight line from source to destination xx = linspace(model.xs, model.xt, model.n+2); yy = linspace(model.ys, model.yt, model.n+2); particle(i).Position.x = xx(2:end-1); particle(i).Position.y = yy(2:end-1); end % Initialize Velocity particle(i).Velocity.x=zeros(VarSize); particle(i).Velocity.y=zeros(VarSize); % Evaluation [particle(i).Cost, particle(i).Sol]=CostFunction(particle(i).Position); % Update Personal Best particle(i).Best.Position=particle(i).Position; particle(i).Best.Cost=particle(i).Cost; particle(i).Best.Sol=particle(i).Sol; % Update Global Best if particle(i).Best.Cost<GlobalBest.Cost GlobalBest=particle(i).Best; end end % Array to Hold Best Cost Values at Each Iteration BestCost=zeros(MaxIt,1); %% PSO Main Loop for it=1:MaxIt for i=1:nPop % x Part % Update Velocity particle(i).Velocity.x = w*particle(i).Velocity.x ... + c1*rand(VarSize).*(particle(i).Best.Position.x-particle(i).Position.x) ... + c2*rand(VarSize).*(GlobalBest.Position.x-particle(i).Position.x); % Update Velocity Bounds particle(i).Velocity.x = max(particle(i).Velocity.x,VelMin.x); particle(i).Velocity.x = min(particle(i).Velocity.x,VelMax.x); % Update Position particle(i).Position.x = particle(i).Position.x + particle(i).Velocity.x; % Velocity Mirroring OutOfTheRange=(particle(i).Position.x<VarMin.x | particle(i).Position.x>VarMax.x); particle(i).Velocity.x(OutOfTheRange)=-particle(i).Velocity.x(OutOfTheRange); % Update Position Bounds particle(i).Position.x = max(particle(i).Position.x,VarMin.x); particle(i).Position.x = min(particle(i).Position.x,VarMax.x); % y Part % Update Velocity particle(i).Velocity.y = w*particle(i).Velocity.y ... + c1*rand(VarSize).*(particle(i).Best.Position.y-particle(i).Position.y) ... + c2*rand(VarSize).*(GlobalBest.Position.y-particle(i).Position.y); % Update Velocity Bounds particle(i).Velocity.y = max(particle(i).Velocity.y,VelMin.y); particle(i).Velocity.y = min(particle(i).Velocity.y,VelMax.y); % Update Position particle(i).Position.y = particle(i).Position.y + particle(i).Velocity.y; % Velocity Mirroring OutOfTheRange=(particle(i).Position.y<VarMin.y | particle(i).Position.y>VarMax.y); particle(i).Velocity.y(OutOfTheRange)=-particle(i).Velocity.y(OutOfTheRange); % Update Position Bounds particle(i).Position.y = max(particle(i).Position.y,VarMin.y); particle(i).Position.y = min(particle(i).Position.y,VarMax.y); % Evaluation [particle(i).Cost, particle(i).Sol]=CostFunction(particle(i).Position); % Update Personal Best if particle(i).Cost<particle(i).Best.Cost particle(i).Best.Position=particle(i).Position; particle(i).Best.Cost=particle(i).Cost; particle(i).Best.Sol=particle(i).Sol; % Update Global Best if particle(i).Best.Cost<GlobalBest.Cost GlobalBest=particle(i).Best; end end end % Update Best Cost Ever Found BestCost(it)=GlobalBest.Cost; % Inertia Weight Damping w=w*wdamp; % Show Iteration Information if GlobalBest.Sol.IsFeasible Flag=' *'; else Flag=[', Violation = ' num2str(GlobalBest.Sol.Violation)]; end % disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it)) Flag]); % particle(i).Cost end toc BestCost disp(['运行时间: ',num2str(toc)]); % Plot Solution figure(1); PlotSolution(GlobalBest.Sol,model,2); % pause(0.01); hold on %% Results figure(2); plot(BestCost,'LineWidth',2,'color',[0 0 1]); xlabel('Iteration'); ylabel('Best Cost'); grid on; hold on %% 清空环境 % clc % clear % close all tic %% 初始化 model=CreateModel(); model.n=2; CostFunction=@(x) MyCost(x,model); eta=0.99; step=5; stepmax=step; stepmin=0.1; c=2; MaxIt=100; % Maximum Number of Iterations nPop=10; % Population Size (Swarm Size) eta0=1; eta1=1; eta2=2; eta3=0.98; nVar=model.n; % Number of Decision Variables VarSize=[1 nVar]; VarMin.x=model.xmin; % Lower Bound of Variables VarMax.x=model.xmax; % Upper Bound of Variables VarMin.y=model.ymin; % Lower Bound of Variables VarMax.y=model.ymax; % Upper Bound of Variables alpha=0.1; VelMax.x=alpha*(VarMax.x-VarMin.x); % Maximum Velocity VelMin.x=-VelMax.x; % Minimum Velocity VelMax.y=alpha*(VarMax.y-VarMin.y); % Maximum Velocity VelMin.y=-VelMax.y; % Minimum Velocity %% Initialization % Create Empty Particle Structure empty_particle.Position=[]; empty_particle.Velocity=[]; empty_particle.Cost=[]; empty_particle.Sol=[]; empty_particle.Best.Position=[]; empty_particle.Best.Cost=[]; empty_particle.Best.Sol=[]; empty_particle.left.Position=[]; empty_particle.right.Position=[]; empty_particle.left.Cost=[]; empty_particle.right.Cost=[]; empty_particle.left.Sol=[]; empty_particle.right.Sol=[]; % Initialize Global Best GlobalBest.Cost=inf; % Create Particles Matrix particle=repmat(empty_particle,nPop,1); %% 初始化种群 for i=1:nPop if i > 1 particle(i).Position=CreateRandomSolution(model); else % Straight line from source to destination xx = linspace(model.xs, model.xt, model.n+2); yy = linspace(model.ys, model.yt, model.n+2); particle(i).Positio