HTML5提供了Canvas对象,为画图应用提供了便利.
Javascript可执行于浏览器中, 而不须要安装特定的编译器；
基于HTML5和Javascript语言, 可随时编写应用, 为算法測试带来便利.
针对TSP问题, 编写了Ant colony algorithm, 用于演示该算法, tsp_ant_colony_algorithm.html代码例如以下:
<html>
<head>
<meta charset = "utf-8" / >
<title>TSP_demo</title>
</head>
<body>
<div id="outText">
</div>
<canvas id="canvas" height="550px" width="1024px">
</canvas>
<script type="text/javascript">
//计时開始
t1 = new Date(); //创建"then"这个日期/时间对像
t1.setTime(t1.getTime()); //为这个对象赋值
var canvas = document.getElementById("canvas");
var canvasWidth = canvas.width;
var canvasHeight = canvas.height;
var context = canvas.getContext("2d");
var N = 30; //城市数量
var M = 120; //蚂蚁数量
var inittao = 1; //初始路径激素量
var tao; //[N][N]; //N*N矩阵——表示i和j间残留的激素信息量, 初始化为常熟C（各路径相等），以表示激素的挥发
var yita; //[N][N]; //N*N矩阵——表示i和j间由举例所决定的选择概率矩阵
var delta_tao; //[N][N]; //一轮循环后添加的信息素
var distant; //[N][N]; //全部城市间的距离
var tabu; //[M][N]; //禁忌表
var route; //[M][N]; //M仅仅蚂蚁所走过的路径
var solution; //[M]; //对M仅仅蚂蚁所走过路径的适应度评价值
var BestRoute; //[N]; //最忌路径
var BestSolution = 10000000000; //设置的极限最大路径
var alfa, beta, rou, Q; //路径激素更新数量
var NcMax; //蚁群最大迭代次数
function initMat(M, N, val) {
var x = new Array();
for(var i = 0; i < M; i++) {
x[i] = new Array();
for(var j = 0; j < N; j++)
x[i].push(val);
}
return x;
}
function initAllMats() {
tao = initMat(N, N, 0);
yita = initMat(N, N, 0);
delta_tao = initMat(N, N, 0);
distant = initMat(N, N, 0);
tabu = initMat(M, N, 0);
route = initMat(M, N, -1);
solution = new Array();
for(var i = 0; i < M; i++)
solution[i] = 0;
BestRoute = new Array();
for(var i = 0; i < N; i++)
BestRoute[i] = -1;
}
//初始化城市的位置
function InCityXY(x, y)
{
for(var i = 0; i < N; i++) {
x[i] = (Math.random() * 32767) % 980 + 20;
y[i] = (Math.random() * 32767) % 420 + 20;
}
}
//初始化算法參数
function initparameter()
{
alfa = 1; //积累的激素调节因子作用系数
beta = 5; //启示性调节因子作用系数
rou = 0.9;
Q = 100; //常量
NcMax = 200; //群蚂蚁进化代数
}
//取得某个路径的长度
function EvalueSolution(a)
{
var dist = 0;
for(var i = 0; i < N-1; i++)
dist += distant[a[i]][a[i+1]];
dist += distant[a[N-1]][a[0]];
return dist;
}
function drawCities(x, y) {
for(var i = 0; i < N; i++) {
context.beginPath();
context.fillStyle = "blue";
context.strokeStyle = "blue";
context.lineWidth = 1;
context.font = "normal 16px Arial";
context.arc(x[i], y[i], 3, (Math.PI / 180) * 0, (Math.PI / 180) * 360, false);
context.fill();
context.stroke();
context.closePath();
/*
context.fillStyle = "white";
context.textAlign = "center";
context.textBaseline = "middle";
context.fillText(String(i), x[i], y[i]);
*/
}
}
function drawPath(x1, y1, x2, y2, color, width) {
context.beginPath();
context.fillStyle = color;
context.strokeStyle = color;
context.lineWidth = width;
context.moveTo(x1, y1);
context.lineTo(x2, y2);
context.stroke();
}
//主函数
function ACA_TSP() {
var NC = 0;
//初始化算法參数
initparameter();
//初始化城市的位置
var x = new Array();
var y = new Array();
for(var i = 0; i < N; i++) {
x.push(0);
y.push(0);
}
//初始化城市位置
InCityXY( x, y );
//计算随意两城市间的距离
for(var i=0;i<N;i++)
for(var j=i+1;j<N;j++)
{
distant[j][i] = Math.sqrt((x[i]-x[j])*(x[i]-x[j])+(y[i]-y[j])*(y[i]-y[j]));
distant[i][j] = distant[j][i];
}
// calculate the heuristic parameters
var i, j, k;
//初始化随意两点间的选择可能性程度=1-p
//若i==j。则p=1
//否则。p=100/distant[i][j]
for(i=0;i<N;i++)
for(j=0;j<N;j++)
{
tao[i][j] = inittao;
if(j != i)
yita[i][j] = 100/distant[i][j]; //值越大，i到j被选择的路径概率越大; 或者说，i和j距离越近，被选择的概率越大
}
//初始化M个蚂蚁走全然部城市(N)的路径
//-1表示第k仅仅蚂蚁尚没有从当前位置走向i城市
/*
for(k=0;k<M;k++)
for(i=0;i<N;i++)
route[k][i] =- 1;
*/
//初始化全部蚂蚁的禁忌表
for(k=0;k<M;k++)
{
route[k][0] = k % N; //随机置放蚂蚁的第一站城市点---此代码实际上没有随机摆放
tabu[k][route[k][0]] = 1; //设置禁忌表的已訪问过的城市为1
}
//全部蚂蚁行走NcMax趟
do {
var s = 1;
var partsum;
var pper;
var drand;
//s循环N次，让每仅仅蚂蚁走N步，走全然程
while( s < N)
{
for(k=0;k<M;k++)
{
var jrand= (Math.random() * 32767) % 3000;
drand= jrand / 3001.0;
partsum = 0;
pper = 0;
for(j=0;j<N;j++)
{
if(tabu[k][j]==0)
partsum += Math.pow(tao[route[k][s-1]][j],alfa) * Math.pow(yita[route[k][s-1]][j],beta);
}
for(j=0;j<N;j++)
{
if(tabu[k][j]==0)
pper += Math.pow(tao[route[k][s-1]][j],alfa) * Math.pow(yita[route[k][s-1]][j],beta)/partsum;
if(pper > drand)
break;
}
tabu[k][j]=1;
route[k][s]=j;
}
s++;
}
// the pheromone is updated
for(i=0;i<N;i++)
for(var j=0;j<N;j++)
delta_tao[i][j]=0;
//记录最短路径及其长度
for(k=0;k<M;k++)
{
solution[k] = EvalueSolution(route[k]);
if(solution[k] < BestSolution)
{
BestSolution = solution[k];
for(s=0; s<N; s++)
BestRoute[s]=route[k][s];
}
}
//依据上一批次（M个蚂蚁）所求路径的长度信息，更新从i到j的选择概率
for(k=0;k<M;k++)
{
for(s=0;s<N-1;s++)
delta_tao[route[k][s]][route[k][s+1]] += Q/solution[k];
delta_tao[route[k][N-1]][route[k][0]] += Q/solution[k];
}
//计算NxN个节点间的转移概率。并设置最大与最小值
for(i=0;i<N;i++)
for(var j=0;j<N;j++)
{
tao[i][j]=rou*tao[i][j]+delta_tao[i][j];
if(tao[i][j] < 0.00001)
tao[i][j] = 0.00001;
if(tao[i][j] > 20)
tao[i][j] = 20;
}
//又一次设置全部蚂蚁的禁忌表和路径信息
for(k=0;k<M;k++)
for(var j=1;j<N;j++)
{
tabu[k][route[k][j]]=0;
route[k][j]=-1;
}
NC++;
} while(NC < NcMax);
//output the calculating outs
/*
print("*针对旅行商问题的蚂蚁克隆算法*");
print("初始參数：");
print("alfa=" + alfa + ", beta=" + beta + ", rou=" + rou + ", Q=" + Q);
print("蚁群探索循环次数:" + NcMax);
print("最短路径是:" + BestSolution);
print("最佳路径是:");
*/
for(i = 0; i < N; i++) {
if (i == N - 1)
j = 0;
else
j = i + 1;
var nodeA = BestRoute[i];
var nodeB = BestRoute[j];
var x1 = x[nodeA];
var y1 = y[nodeA];
var x2 = x[nodeB];
var y2 = y[nodeB];
drawPath(x1, y1, x2, y2, "black", 2);
}
drawCities(x, y);
var out = document.getElementById("outText");
out.innerHTML = "<h1>蚂蚁克隆算法求解旅行商问题: </h1>最佳路径:<br/>";
for(i=0;i<N;i++)
out.innerHTML = out.innerHTML + String(BestRoute[i]) + " ";
out.innerHTML = out.innerHTML + "<br/>最短路径长度:<br/>" + BestSolution;
}
//调用上述函数
initAllMats();
ACA_TSP();
//结束后,取得如今时间, 并计算时间差
t2 = new Date(); //创建"then"这个日期/时间对像
var ms = t2.getTime() - t1.getTime();
var out = document.getElementById("outText");
out.innerHTML = out.innerHTML + "<br/>用时(毫秒):<br/>" + ms;
</script>
</body>
</html>
刷新该页面, 可随机产生不同的城市点, 并计算最短路径.
实例效果例如以下:
须要说明的是, 算法仍需改善, 在有些其情况下,无法保障总能获得最短路径.