鮣鱼优化算法(ROA)附matlab代码
鮣鱼优化算法(ROA)附matlab代码
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⛄ 内容介绍
鮣鱼优化算法（ROA）是一种基于自然界中鮣鱼捕食行为的启发式优化算法。该算法模拟了鮣鱼在寻找食物时的行为，通过不断调整自身位置和速度，以找到最优解。
ROA算法的核心思想是通过模拟鮣鱼的觅食行为，来寻找问题的最优解。在算法的开始阶段，鮣鱼个体会随机分布在搜索空间中，并根据其适应度值来决定其移动的方向和速度。在搜索过程中，鮣鱼个体会根据周围环境的信息进行调整，以更好地适应问题的解空间。
ROA算法的优势在于其简单性和高效性。由于算法模拟了自然界中的行为，它能够有效地避免陷入局部最优解，并且具有较高的全局搜索能力。此外，ROA算法还具有较强的鲁棒性和适应性，能够适应不同类型的问题。
然而，ROA算法也存在一些局限性。首先，算法的性能高度依赖于问题的特性和搜索空间的大小。对于复杂的问题，ROA算法可能需要较长的搜索时间才能找到最优解。其次，算法的参数设置对搜索性能有较大影响，需要经过一定的调优才能达到较好的效果。
总的来说，鮣鱼优化算法是一种基于自然界中鮣鱼觅食行为的启发式优化算法。它通过模拟鮣鱼的行为来寻找问题的最优解，并具有较好的全局搜索能力和适应性。虽然ROA算法存在一些局限性，但它仍然是一种有效的优化算法，可以用于解决各种实际问题。
⛄ 部分代码
function
[lowerbound,upperbound,dimension,fitness] = fun_info(F)
switch
F
case
’F1’
fitness
=
@F1;
lowerbound
=
-100;
upperbound
=
100;
dimension
=
30;
case
’F2’
fitness
=
@F2;
lowerbound
=
-10;
upperbound
=
10;
dimension
=
30;
case
’F3’
fitness
=
@F3;
lowerbound
=
-100;
upperbound
=
100;
dimension
=
30;
case
’F4’
fitness
=
@F4;
lowerbound
=
-100;
upperbound
=
100;
dimension
=
30;
case
’F5’
fitness
=
@F5;
lowerbound
=
-30;
upperbound
=
30;
dimension
=
30;
case
’F6’
fitness
=
@F6;
lowerbound
=
-100;
upperbound
=
100;
dimension
=
30;
case
’F7’
fitness
=
@F7;
lowerbound
=
-1.28;
upperbound
=
1.28;
dimension
=
30;
case
’F8’
fitness
=
@F8;
lowerbound
=
-500;
upperbound
=
500;
dimension
=
30;
case
’F9’
fitness
=
@F9;
lowerbound
=
-5.12;
upperbound
=
5.12;
dimension
=
30;
case
’F10’
fitness
=
@F10;
lowerbound
=
-32;
upperbound
=
32;
dimension
=
30;
case
’F11’
fitness
=
@F11;
lowerbound
=
-600;
upperbound
=
600;
dimension
=
30;
case
’F12’
fitness
=
@F12;
lowerbound
=
-50;
upperbound
=
50;
dimension
=
30;
case
’F13’
fitness
=
@F13;
lowerbound
=
-50;
upperbound
=
50;
dimension
=
30;
case
’F14’
fitness
=
@F14;
lowerbound
=
-65.536;
upperbound
=
65.536;
dimension
=
2;
case
’F15’
fitness
=
@F15;
lowerbound
=
-5;
upperbound
=
5;
dimension
=
4;
case
’F16’
fitness
=
@F16;
lowerbound
=
-5;
upperbound
=
5;
dimension
=
2;
case
’F17’
fitness
=
@F17;
lowerbound
=
[-5,0];
upperbound
=
[10,15];
dimension
=
2;
case
’F18’
fitness
=
@F18;
lowerbound
=
-2;
upperbound
=
2;
dimension
=
2;
case
’F19’
fitness
=
@F19;
lowerbound
=
0;
upperbound
=
1;
dimension
=
3;
case
’F20’
fitness
=
@F20;
lowerbound
=
0;
upperbound
=
1;
dimension
=
6;
case
’F21’
fitness
=
@F21;
lowerbound
=
0;
upperbound
=
10;
dimension
=
4;
case
’F22’
fitness
=
@F22;
lowerbound
=
0;
upperbound
=
10;
dimension
=
4;
case
’F23’
fitness
=
@F23;
lowerbound
=
0;
upperbound
=
10;
dimension
=
4;
end
end
%
F1
function
R = F1(x)
R
=
sum(x.^2);
end
%
F2
function
R = F2(x)
R
=
sum(abs(x))+prod(abs(x));
end
%
F3
function
R = F3(x)
dimension
=
size(x,2);
R
=
0;
for
i=1:dimension
R
=
R+sum(x(1:i))^2;
end
end
%
F4
function
R = F4(x)
R
=
min(abs(x));
end
%
F5
function
R = F5(x)
dimension
=
size(x,2);
R
=
sum(100*(x(2:dimension)-(x(1:dimension-1).^2)).^2+(x(1:dimension-1)-1).^2);
end
%
F6
function
R = F6(x)
R
=
sum(abs((x+.5)).^2);
end
%
F7
function
R = F7(x)
dimension
=
size(x,2);
R
=
sum([1:dimension].*(x.^4))+rand;
end
%
F8
function
R = F8(x)
R
=
sum(-x.*sin(sqrt(abs(x))));
end
%
F9
function
R = F9(x)
dimension
=
size(x,2);
R
=
sum(x.^2-10*cos(2*pi.*x))+10*dimension;
end
%
F10
function
R = F10(x)
dimension
=
size(x,2);
R
=
-20*exp(-.2*sqrt(sum(x.^2)/dimension))-exp(sum(cos(2*pi.*x))/dimension)+20+exp(1);
end
%
F11
function
R = F11(x)
dimension
=
size(x,2);
R
=
sum(x.^2)/4000-prod(cos(x./sqrt([1:dimension])))+1;
end
%
F12
function
R = F12(x)
dimension
=
size(x,2);
R
=
(pi/dimension)*(10*((sin(pi*(1+(x(1)+1)/4)))^2)+sum((((x(1:dimension-1)+1)./4).^2).*...
(1+10.*((sin(pi.*(1+(x(2
:
dimension)+1)./4)))).^2))+((x(dimension)+1)/4)^2)+sum(Ufun(x,10,100,4));
end
%
F13
function
R = F13(x)
dimension
=
size(x,2);
R
=
.1*((sin(3*pi*x(1)))^2+sum((x(1:dimension-1)-1).^2.*(1+(sin(3.*pi.*x(2:dimension))).^2))+...
((x(dimension)-1)^2)*(1+(sin(2*pi*x(dimension)))^2))+sum(Ufun(x,5,100,4));
end
%
F14
function
R = F14(x)
aS
=
[-32 -16 0 16 32 -32 -16 0 16 32 -32 -16 0 16 32 -32 -16 0 16 32 -32 -16 0 16 32;,...
-32
-32 -32 -32 -32 -16 -16 -16 -16 -16 0 0 0 0 0 16 16 16 16 16 32 32 32 32 32];
for
j=1:25
bS(j)
=
sum((x’-aS(:,j)).^6);
end
R
=
(1/500+sum(1./([1:25]+bS))).^(-1);
end
%
F15
function
R = F15(x)
aK
=
[.1957 .1947 .1735 .16 .0844 .0627 .0456 .0342 .0323 .0235 .0246];
bK
=
[.25 .5 1 2 4 6 8 10 12 14 16];bK=1./bK;
R
=
sum((aK-((x(1).*(bK.^2+x(2).*bK))./(bK.^2+x(3).*bK+x(4)))).^2);
end
%
F16
function
R = F16(x)
R
=
4*(x(1)^2)-2.1*(x(1)^4)+(x(1)^6)/3+x(1)*x(2)-4*(x(2)^2)+4*(x(2)^4);
end
%
F17
function
R = F17(x)
R
=
(x(2)-(x(1)^2)*5.1/(4*(pi^2))+5/pi*x(1)-6)^2+10*(1-1/(8*pi))*cos(x(1))+10;
end
%
F18
function
R = F18(x)
R
=
(1+(x(1)+x(2)+1)^2*(19-14*x(1)+3*(x(1)^2)-14*x(2)+6*x(1)*x(2)+3*x(2)^2))*...
(30+(2*x(1)-3*x(2))^2*(18-32*x(1)+12*(x(1)^2)+48*x(2)-36*x(1)*x(2)+27*(x(2)^2)));
end
%
F19
function
R = F19(x)
aH
=
[3 10 30;.1 10 35;3 10 30;.1 10 35];cH=[1 1.2 3 3.2];
pH
=
[.3689 .117 .2673;.4699 .4387 .747;.1091 .8732 .5547;.03815 .5743 .8828];
R
=
0;
for
i=1:4
R
=
R-cH(i)*exp(-(sum(aH(i,:).*((x-pH(i,:)).^2))));
end
end
%
F20
function
R = F20(x)
aH
=
[10 3 17 3.5 1.7 8;.05 10 17 .1 8 14;3 3.5 1.7 10 17 8;17 8 .05 10 .1 14];
cH
=
[1 1.2 3 3.2];
pH
=
[.1312 .1696 .5569 .0124 .8283 .5886;.2329 .4135 .8307 .3736 .1004 .9991;...
.2348
.1415 .3522 .2883 .3047 .6650;.4047 .8828 .8732 .5743 .1091 .0381];
R
=
0;
for
i=1:4
R
=
R-cH(i)*exp(-(sum(aH(i,:).*((x-pH(i,:)).^2))));
end
end
%
F21
function
R = F21(x)
aSH
=
[4 4 4 4;1 1 1 1;8 8 8 8;6 6 6 6;3 7 3 7;2 9 2 9;5 5 3 3;8 1 8 1;6 2 6 2;7 3.6 7 3.6];
cSH
=
[.1 .2 .2 .4 .4 .6 .3 .7 .5 .5];
R
=
0;
for
i=1:5
R
=
R-((x-aSH(i,:))*(x-aSH(i,:))’+cSH(i))^(-1);
end
end
%
F22
function
R = F22(x)
aSH
=
[4 4 4 4;1 1 1 1;8 8 8 8;6 6 6 6;3 7 3 7;2 9 2 9;5 5 3 3;8 1 8 1;6 2 6 2;7 3.6 7 3.6];
cSH
=
[.1 .2 .2 .4 .4 .6 .3 .7 .5 .5];
R
=
0;
for
i=1:7
R
=
R-((x-aSH(i,:))*(x-aSH(i,:))’+cSH(i))^(-1);
end
end
%
F23
function
R = F23(x)
aSH
=
[4 4 4 4;1 1 1 1;8 8 8 8;6 6 6 6;3 7 3 7;2 9 2 9;5 5 3 3;8 1 8 1;6 2 6 2;7 3.6 7 3.6];
cSH
=
[.1 .2 .2 .4 .4 .6 .3 .7 .5 .5];
R
=
0;
for
i=1:10
R
=
R-((x-aSH(i,:))*(x-aSH(i,:))’+cSH(i))^(-1);
end
end
function
R=Ufun(x,a,k,m)
R
=
k.*((x-a).^m).*(x>a)+k.*((-x-a).^m).*(x<(-a));
end
⛄ 运行结果