?【路径规划】基于萤火虫算法求解障碍地形matlab源码
2021-10-21 23:08:48

clc;

clear;

close all;

%% Problem Definition

model=CreateModel();

model.n=3;  % 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

%% GSO Parameters

MaxIt=50;          % Maximum Number of Iterations

nPop=100;           % Population Size (Swarm Size)

w=1;                % Inertia Weight

wdamp=0.98;         % Inertia Weight Damping Ratio

c1=1.5;             % Personal Learning Coefficient

c2=1.5;             % Global Learning Coefficient

%RANGE

range_init = 5.0;

range_boundary = 50.2;

%LUCIFERIN

luciferin_init = 25;

luciferin_decay = 0.4;

luciferin_enhancement = 0.6;

%Neighbors

k_neigh = 20;

beta = 0.5;

step_size = 5;

%% Initialization

% Create Empty Glowworm Structure

empty_glowworm.Position=[];

empty_glowworm.range=[];

empty_glowworm.luciferin=[];

empty_glowworm.Cost=[];

empty_glowworm.Sol=[];

empty_glowworm.neighbors=[];

empty_glowworm.Best.Position=[];

empty_glowworm.Best.Cost=[];

empty_glowworm.Best.Sol=[];

% Initialize Global Best

GlobalBest.Cost=inf;

% Create glowworms Matrix

glowworm=repmat(empty_glowworm,nPop,1);

% Initialization Loop

for i=1:nPop

% Initialize Position

if i > 1

glowworm(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);

glowworm(i).Position.x = xx(2:end-1);

glowworm(i).Position.y = yy(2:end-1);

end

% Initialize luciferin

glowworm(i).luciferin.x=repmat( luciferin_init , 1 , nVar);

glowworm(i).luciferin.y=repmat( luciferin_init , 1 , nVar);

%Initialize range

glowworm(i).range.x = repmat( range_init , 1 , nVar);

glowworm(i).range.y = repmat( range_init , 1 , nVar);

neighbors = [];

% Evaluation

[glowworm(i).Cost, glowworm(i).Sol]=CostFunction(glowworm(i).Position);

% Update Personal Best

glowworm(i).Best.Position=glowworm(i).Position;

glowworm(i).Best.Cost=glowworm(i).Cost;

glowworm(i).Best.Sol=glowworm(i).Sol;

% Update Global Best

if glowworm(i).Best.Cost<GlobalBest.Cost

GlobalBest=glowworm(i).Best;

end

end

% Array to Hold Best Cost Values at Each Iteration

BestCost=zeros(MaxIt,1);

for it=1:MaxIt

for i=1:nPop

end

% Update Best Cost Ever Found

BestCost(it)=GlobalBest.Cost;

% 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]);

% Plot Solution

figure(1);

PlotSolution(GlobalBest.Sol,model);

pause(0.01);

end

img =gcf;  %获取当前画图的句柄

print(img, '-dpng', '-r600', './img2.png')         %即可得到对应格式和期望dpi的图像

%% Results

figure;

plot(BestCost,'LineWidth',2);

xlabel('Iteration');

ylabel('Best Cost');

grid on;

img =gcf;  %获取当前画图的句柄

print(img, '-dpng', '-r600', './img.png')         %即可得到对应格式和期望dpi的图像