1. 该资源是MATLAB代码，混沌博弈优化算法Chaos Game Optimization (CGO)，该算法基于混沌理论的原理，利用分形和混沌博弈的基本概念进行模型构建，该算法于2020年提出。Reference: Talatahari, S., Azizi, M., Optimization of Constrained Mathematical and Engineering Design Problems Using Chaos Game Optimization, Computers & Industrial Engineering (2020).
2. 该资源是混沌博弈优化算法的MATLAB代码，可直接运行，目标函数包括Sphere测试函数等23组基准测试函数。运行结果包括最优解和最佳适应度值，以及收敛曲线的图像。
3. 适合算法研发人群使用。
4.如果您对资源的使用有疑问，请在CSDN私信作者
2022-07-08 16:08:46
4KB
image-comments, 在代码注释中，允许嵌入图像的Visual Studio 扩展 ImageComments ( Visual Studio 扩展)概述这是 Visual Studio 代码编辑器的扩展，允许在代码之间显示图像，允许在视觉上丰富的注释。 例如... 用法信息序言免责声明：这个项目是一个 W
2022-07-02 02:37:01
239KB
1
import javax.swing.*;
import java.awt.*;
import java.awt.event.*;
public class HannoiWindow extends JFrame implements ActionListener{
Tower tower=null;
int amountOfDisc=3;
char []towerName={'A','B','C'};
JMenuBar bar;
JMenu menuGrade;
JMenuItem oneGradeItem,twoGradeItem,threeGradeItem;
JButton renew=null;
JButton autoButton=null;
JPanel center=new JPanel();
HannoiWindow(){
tower=new Tower(towerName);
tower.setAmountOfDisc(amountOfDisc);
tower.setMaxDiscWidth(120);
tower.setMinDiscWidth(50);
tower.setDiscHeight(16);
tower.putDiscOnTower();
add(tower,BorderLayout.CENTER);
bar=new JMenuBar();
menuGrade=new JMenu("选择级别");
oneGradeItem=new JMenuItem("初级");
twoGradeItem=new JMenuItem("中级");
threeGradeItem=new JMenuItem("高级");
menuGrade.add(oneGradeItem);
menuGrade.add(twoGradeItem);
menuGrade.add(threeGradeItem);
bar.add(menuGrade);
setJMenuBar(bar);
oneGradeItem.addActionListener(this);
twoGradeItem.addActionListener(this);
threeGradeItem.addActionListener(this);
renew=new JButton("重新开始");
renew.addActionListener(this);
autoButton=new JButton("自动演示");
autoButton.addActionListener(this);
JPanel north=new JPanel();
north.add(renew);
north.add(autoButton);
String mess="将全部盘子从"+towerName[0]+"座搬运到"+towerName[1]+
"座或"+towerName[2]+"座";
JLabel hintMess=new JLabel(mess,JLabel.CENTER);
north.add(hintMess);
add(north,BorderLayout.NORTH);
setResizable(false);
setVisible(true);
setBounds(60,60,460,410);
validate();
setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
}
public void actionPerformed(ActionEvent e){
if(e.getSource()==oneGradeItem){
amountOfDisc=3;
tower.setAmountOfDisc(amountOfDisc);
tower.putDiscOnTower();
}
else if(e.getSource()==twoGradeItem){
amountOfDisc=4;
tower.setAmountOfDisc(amountOfDisc);
tower.putDiscOnTower();
}
else if(e.getSource()==threeGradeItem){
amountOfDisc=5;
tower.setAmountOfDisc(amountOfDisc);
tower.putDiscOnTower();
}
else if(e.getSource()==renew){
tower.setAmountOfDisc(amountOfDisc);
tower.putDiscOnTower();
}
else if(e.getSource()==autoButton){
tower.setAmountOfDisc(amountOfDisc);
tower.putDiscOnTower();
int x=this.getBounds().x+this.getBounds().width;
int y=this.getBounds().y;
tower.getAutoMoveDisc().setLocation(x,y);
tower.getAutoMoveDisc().setSize(280,this.getBounds().height);
tower.getAutoMoveDisc().setVisible(true);
}
validate();
}
public static void main(String args[]){
new HannoiWindow();
}
}
2022-06-29 14:58:39
3KB
1
支持向量机，是常见的一种判别方法。在机器学习领域，是一个有监督的学习模型，通常用来进行模式识别、分类以及回归分析。
2022-06-18 20:13:14
3.19MB
1