【优化部署】基于matlab遗传算法求解移动传感器部署优化问题【含Matlab源码 1197期】

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⛄一、遗传算法简介

1 引言


2 遗传算法理论
2.1 遗传算法的生物学基础


2.2 遗传算法的理论基础




2.3 遗传算法的基本概念






2.4 标准的遗传算法


2.5 遗传算法的特点


2.6 遗传算法的改进方向

3 遗传算法流程



4 关键参数说明

⛄二、部分源代码

function mutation(pop_size, chromo_size, mutate_rate)
global pop;
global aim;
global q;

for i=1:pop_size
if rand < mutate_rate
mutate_pos = round(rand*chromo_size);
if mutate_pos == 0
continue;
end

pop_tem = pop;
vaild = 1;

pop_tem(i,mutate_pos) = round(rand()*50); %变异

if ( pop_tem(i,1)>=aim(1,1)&&pop_tem(i,4)>=aim(1,1)&&pop_tem(i,7)>=aim(1,1) ) || ( pop_tem(i,2)>=aim(1,2)&&pop_tem(i,5)>=aim(1,2)&&pop_tem(i,8)>=aim(1,2) ) || ( pop_tem(i,3)>=aim(1,3)&&pop_tem(i,6)>=aim(1,3)&&pop_tem(i,9)>=aim(1,3) )
vaild = 0;
elseif ( pop_tem(i,1)<=aim(1,1)&&pop_tem(i,4)<=aim(1,1)&&pop_tem(i,7)<=aim(1,1) ) || ( pop_tem(i,2)<=aim(1,2)&&pop_tem(i,5)<=aim(1,2)&&pop_tem(i,8)<=aim(1,2) ) || ( pop_tem(i,3)<=aim(1,3)&&pop_tem(i,6)<=aim(1,3)&&pop_tem(i,9)<=aim(1,3) )
vaild = 0;
elseif (sqrt((pop_tem(i,1)-aim(1,1))^{2+(pop_tem(i,2)-aim(1,2))}2+(pop_tem(i,3)-aim(1,3))^{2)+sqrt((pop_tem(i,4)-aim(1,1))}2+(pop_tem(i,5)-aim(1,2))^{2+(pop_tem(i,6)-aim(1,3))}2)+sqrt((pop_tem(i,7)-aim(1,1))^{2+(pop_tem(i,8)-aim(1,2))}2+(pop_tem(i,9)-aim(1,3))^2))/3 > q
vaild = 0;
end

if(vaild == 1)
pop = pop_tem;
end

end
end

clear i;
clear mutate_pos;
clear pop_tem;
function crossover(pop_size, chromo_size, cross_rate)
global pop;
global aim;
global q;

for i=2:2:pop_size
if(rand < cross_rate)
cross_pos = round(rand * chromo_size); %随机交叉位置

if or (cross_pos == 0, cross_pos == 1)
continue;
end

pop_tem = pop;

for j=cross_pos:chromo_size %进行交叉，对象是相邻两个个体
temp = pop_tem(i,j);
pop_tem(i,j) = pop_tem(i+1,j);
pop_tem(i+1,j) = temp;
end

vaild = 1;
if ( pop_tem(i,1)>=aim(1,1)&&pop_tem(i,4)>=aim(1,1)&&pop_tem(i,7)>=aim(1,1) ) || ( pop_tem(i,2)>=aim(1,2)&&pop_tem(i,5)>=aim(1,2)&&pop_tem(i,8)>=aim(1,2) ) || ( pop_tem(i,3)>=aim(1,3)&&pop_tem(i,6)>=aim(1,3)&&pop_tem(i,9)>=aim(1,3) )
vaild = 0;
elseif ( pop_tem(i,1)<=aim(1,1)&&pop_tem(i,4)<=aim(1,1)&&pop_tem(i,7)<=aim(1,1) ) || ( pop_tem(i,2)<=aim(1,2)&&pop_tem(i,5)<=aim(1,2)&&pop_tem(i,8)<=aim(1,2) ) || ( pop_tem(i,3)<=aim(1,3)&&pop_tem(i,6)<=aim(1,3)&&pop_tem(i,9)<=aim(1,3) )
vaild = 0;
elseif (sqrt((pop_tem(i,1)-aim(1,1))^{2+(pop_tem(i,2)-aim(1,2))}2+(pop_tem(i,3)-aim(1,3))^{2)+sqrt((pop_tem(i,4)-aim(1,1))}2+(pop_tem(i,5)-aim(1,2))^{2+(pop_tem(i,6)-aim(1,3))}2)+sqrt((pop_tem(i,7)-aim(1,1))^{2+(pop_tem(i,8)-aim(1,2))}2+(pop_tem(i,9)-aim(1,3))^2))/3 > q
vaild = 0;
elseif( pop_tem(i+1,1)>=aim(1,1)&&pop_tem(i+1,4)>=aim(1,1)&&pop_tem(i+1,7)>=aim(1,1) ) || ( pop_tem(i+1,2)>=aim(1,2)&&pop_tem(i+1,5)>=aim(1,2)&&pop_tem(i+1,8)>=aim(1,2) ) || ( pop_tem(i+1,3)>=aim(1,3)&&pop_tem(i+1,6)>=aim(1,3)&&pop_tem(i+1,9)>=aim(1,3) )
vaild = 0;
elseif ( pop_tem(i+1,1)<=aim(1,1)&&pop_tem(i+1,4)<=aim(1,1)&&pop_tem(i+1,7)<=aim(1,1) ) || ( pop_tem(i+1,2)<=aim(1,2)&&pop_tem(i+1,5)<=aim(1,2)&&pop_tem(i+1,8)<=aim(1,2) ) || ( pop_tem(i+1,3)<=aim(1,3)&&pop_tem(i+1,6)<=aim(1,3)&&pop_tem(i+1,9)<=aim(1,3) )
vaild = 0;
elseif (sqrt((pop_tem(i+1,1)-aim(1,1))^{2+(pop_tem(i+1,2)-aim(1,2))}2+(pop_tem(i+1,3)-aim(1,3))^{2)+sqrt((pop_tem(i+1,4)-aim(1,1))}2+(pop_tem(i+1,5)-aim(1,2))^{2+(pop_tem(i+1,6)-aim(1,3))}2)+sqrt((pop_tem(i+1,7)-aim(1,1))^{2+(pop_tem(i+1,8)-aim(1,2))}2+(pop_tem(i+1,9)-aim(1,3))^2))/3 > q
vaild = 0;
end

if(vaild == 1)
pop = pop_tem;
end

end
end

clear i;
clear j;
clear temp;
clear vaild;
clear cross_pos;
clear pop_temp;
function initilize(pop_size, chromo_size)
global pop;
global aim; %目标点坐标
global q;
vaild = 0;
for i=1:pop_size
for l = 1:10000000;
vaild = 1;
for j=1:chromo_size
pop(i,j) = round(rand()*50);
end
if ( pop(i,1)>=aim(1,1)&&pop(i,4)>=aim(1,1)&&pop(i,7)>=aim(1,1) ) || ( pop(i,2)>=aim(1,2)&&pop(i,5)>=aim(1,2)&&pop(i,8)>=aim(1,2) ) || ( pop(i,3)>=aim(1,3)&&pop(i,6)>=aim(1,3)&&pop(i,9)>=aim(1,3) )
vaild = 0;
elseif ( pop(i,1)<=aim(1,1)&&pop(i,4)<=aim(1,1)&&pop(i,7)<=aim(1,1) ) || ( pop(i,2)<=aim(1,2)&&pop(i,5)<=aim(1,2)&&pop(i,8)<=aim(1,2) ) || ( pop(i,3)<=aim(1,3)&&pop(i,6)<=aim(1,3)&&pop(i,9)<=aim(1,3) )
vaild = 0;
elseif (sqrt((pop(i,1)-aim(1,1))^{2+(pop(i,2)-aim(1,2))}2+(pop(i,3)-aim(1,3))^{2)+sqrt((pop(i,4)-aim(1,1))}2+(pop(i,5)-aim(1,2))^{2+(pop(i,6)-aim(1,3))}2)+sqrt((pop(i,7)-aim(1,1))^{2+(pop(i,8)-aim(1,2))}2+(pop(i,9)-aim(1,3))^2))/3 > q
vaild = 0;
end
if vaild == 1
break;
end;
end
end
clear i;
clear j;
clear l;
clear vaild;
function rank(pop_size, chromo_size)
global fitness_value;
global fitness_table;
global fitness_avg;
global best_fitness;
global best_individual;
global best_generation;
global pop;
global G;

for i=1:pop_size
fitness_table(i) = 0.;
end

min = 1;
temp = 1;
temp1(chromo_size)=0;
for i=1:pop_size
min = i;
for j = i+1:pop_size
if fitness_value(j)<fitness_value(min);
min = j;
end
end
if min~=i
temp = fitness_value(i);
fitness_value(i) = fitness_value(min);
fitness_value(min) = temp;
for k = 1:chromo_size
temp1(k) = pop(i,k);
pop(i,k) = pop(min,k);
pop(min,k) = temp1(k);
end
end

end

for i=1:pop_size
if i==1
fitness_table(i) = fitness_table(i) + fitness_value(i);
else
fitness_table(i) = fitness_table(i-1) + fitness_value(i);
end
end

fitness_avg(G) = fitness_table(pop_size)/pop_size;

if fitness_value(pop_size) > best_fitness
best_fitness = fitness_value(pop_size);
best_generation = G;
for j=1:chromo_size
best_individual(j) = pop(pop_size,j);
end
end

clear i;
clear j;
clear k;
clear min;
clear temp;
clear temp1;

⛄三、运行结果

⛄四、matlab版本及参考文献

1 matlab版本
2014a

2 参考文献
[1]刘军.一种混合异构传感网的覆盖洞修补算法[J].控制与决策，2015.

3 备注
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