【智能优化算法-闪电算法】基于闪电算法求解多目标优化问题附matlab代码

1 内容介绍

闪电连接过程算法（ Lightning Attachment Procedure Optimization，LAPO）是受自然界中闪电上迎先导与下行先导连接过程的启发，于提出的一种新型智能算法。该算法具有收敛速度快，精度高等特点。

2 仿真代码

close all

clear all

clc

format long

set(0,'DefaultAxesFontName', 'Times New Roman')

set(0,'DefaultAxesFontSize', 14)

set(0,'DefaultTextFontname', 'Times New Roman')

set(0,'DefaultTextFontSize', 26)

opengl('save', 'software')

% Optimizator Parameters

LB = zeros(1,10); %lower bounds

UB = ones(1,10); %upper bounds

pop = 100;%Population

n_iter = 100; %Max number os iterations/gerations

ref = 0.4;%if more than zero, a second LF is created with refinement % the size of the other

IntCon = [0]; %zero if there are no variables that must be integers. Ex.: IntCon = [1,2];

Np = 100000; %Number of Particles (If 3D, better more than 10000)

S_c = 1;%Stick Probability: Percentage of particles that can don´t stuck in the

%cluster. Between 0 and 1. Near 0 there are more aggregate, the density of

%cluster is bigger and difusity is low. Near 1 is the opposite.

Rc = 150;%Creation Radius (if 3D, better be less than 80, untill 150)

M = 0; %If M = 0, no lichtenberg figure is created (it is loaded a optimized figure); if 1, a single is created and used in all iterations; If 2, one is created for each iteration.(creating an LF figure takes about 2 min)

d = length(UB); %problem dimension

ngrid = 30; %Number of grids in each dimension

Nr = 100;%Maximum number of solutions in PF

[x,fval] = LA_optimization(@objectives,d,pop,LB,UB,ref,n_iter,Np,Rc,S_c,M,ngrid,Nr,IntCon,@constraint);

figure

plot(fval(:,1),fval(:,2),'ZDataSource','',...

'MarkerFaceColor',[1 0 0],...

'MarkerEdgeColor',[0 0 0],...

'MarkerSize',8,...

'Marker','o',...

'LineWidth',0.3,...

'LineStyle','none',...

'Color',[0 0 0]);

hold on

box on

set(0,'DefaultAxesFontSize', 10)

set(0,'DefaultTextFontSize', 10)

set(findall(gcf,'-property','FontName'),'FontName','Italic')

set(findall(gcf,'-property','FontAngle'),'FontAngle','italic')

set(gcf,'position',[200,200,600,320])

%title('Non-dominated solutions','fontweight','bold');

%axis([0 3000 0 30000 0 -1])

xlabel('f_1')

ylabel('f_2')

function y = objectives(x)

y(1)= x(1);

n = length(x);

g =1+(9/29)*sum(x(2:n));

h=1-sqrt(y(1)/g)-(y(1)/g)*sin(10*pi*y(1));

y(2)= g*h;

return

close all

clear all

clc

format long

set(0,'DefaultAxesFontName', 'Times New Roman')

set(0,'DefaultAxesFontSize', 14)

set(0,'DefaultTextFontname', 'Times New Roman')

set(0,'DefaultTextFontSize', 26)

opengl('save', 'software')

% Optimizator Parameters

LB = zeros(1,10); %lower bounds

UB = ones(1,10); %upper bounds

pop = 100;%Population

n_iter = 100; %Max number os iterations/gerations

ref = 0.4;%if more than zero, a second LF is created with refinement % the size of the other

IntCon = [0]; %zero if there are no variables that must be integers. Ex.: IntCon = [1,2];

Np = 100000; %Number of Particles (If 3D, better more than 10000)

S_c = 1;%Stick Probability: Percentage of particles that can don´t stuck in the

%cluster. Between 0 and 1. Near 0 there are more aggregate, the density of

%cluster is bigger and difusity is low. Near 1 is the opposite.

Rc = 150;%Creation Radius (if 3D, better be less than 80, untill 150)

M = 0; %If M = 0, no lichtenberg figure is created (it is loaded a optimized figure); if 1, a single is created and used in all iterations; If 2, one is created for each iteration.(creating an LF figure takes about 2 min)

d = length(UB); %problem dimension

ngrid = 30; %Number of grids in each dimension

Nr = 100;%Maximum number of solutions in PF

[x,fval] = LA_optimization(@objectives,d,pop,LB,UB,ref,n_iter,Np,Rc,S_c,M,ngrid,Nr,IntCon,@constraint);

figure

plot(fval(:,1),fval(:,2),'ZDataSource','',...

'MarkerFaceColor',[1 0 0],...

'MarkerEdgeColor',[0 0 0],...

'MarkerSize',8,...

'Marker','o',...

'LineWidth',0.3,...

'LineStyle','none',...

'Color',[0 0 0]);

hold on

box on

set(0,'DefaultAxesFontSize', 10)

set(0,'DefaultTextFontSize', 10)

set(findall(gcf,'-property','FontName'),'FontName','Italic')

set(findall(gcf,'-property','FontAngle'),'FontAngle','italic')

set(gcf,'position',[200,200,600,320])

%title('Non-dominated solutions','fontweight','bold');

%axis([0 3000 0 30000 0 -1])

xlabel('f_1')

ylabel('f_2')

function y = objectives(x)

y(1)= x(1);

n = length(x);

g =1+(9/29)*sum(x(2:n));

h=1-sqrt(y(1)/g)-(y(1)/g)*sin(10*pi*y(1));

y(2)= g*h;

return

3 运行结果

4 参考文献

[1]王春枝, 邢绍文, 严灵毓,等. 改进闪电连接过程优化算法的网络流量预测方法及系统:.​

博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，相关matlab代码问题可私信交流。

部分理论引用网络文献，若有侵权联系博主删除。