一起来学演化计算-实数空间变异算子
觉得有用的话,欢迎一起讨论相互学习~
参考文献
- [节选自] 文诗华. 多目标进化算法中变异算子的研究[D]. 湘潭大学. 这是来自郑金华教授的学生的硕士毕业论文。从入门演化计算时就读着郑老师的书走来的。在此对其表示最诚挚的敬意
实数搜索空间变异算子的实现
变异算子的设计原则
变异算子的研究概况
实数空间常用变异算子
均匀变异
非均匀变异
自适应变异
高斯变异
柯西变异
多项式变异
在Deb K , Goyal M . A Combined Genetic Adaptive Search (GeneAS) for Engineering Design[C]// 1996.原文中，其是这样描述的。
matlab实现多项式变异
% polynomial mutation
% 多项式突变
function object=mutate(object,p,dim,mum)
rnvec_temp=p.rnvec;
for i=1:dim
% 因为问题的维度一般比较大，所以rand(1)<1/dim的概率很小
if rand(1)<1/dim
u=rand(1);
if u <= 0.5
del=(2*u)^(1/(1+mum)) - 1;
rnvec_temp(i)=p.rnvec(i) + del*(p.rnvec(i));
else
del= 1 - (2*(1-u))^(1/(1+mum));
rnvec_temp(i)=p.rnvec(i) + del*(1-p.rnvec(i));
end
end
end
object.rnvec = rnvec_temp;
end
jmetal实现多项式变异
public void doMutation(double probability, Solution solution) throws JMException {
double rnd, delta1, delta2, mut_pow, deltaq;
double y, yl, yu, val, xy;
XReal x = new XReal(solution);
for (int var = 0; var < solution.numberOfVariables(); var++) {
if (PseudoRandom.randDouble() <= probability) {//如果小于变异概率即可以进行变异操作
y = x.getValue(var);
yl = x.getLowerBound(var);
yu = x.getUpperBound(var);
delta1 = (y - yl) / (yu - yl);
delta2 = (yu - y) / (yu - yl);
rnd = PseudoRandom.randDouble();
mut_pow = 1.0 / (eta_m_ + 1.0);
if (rnd <= 0.5) {
xy = 1.0 - delta1;
val = 2.0 * rnd + (1.0 - 2.0 * rnd) * (Math.pow(xy, (distributionIndex_ + 1.0)));
deltaq = java.lang.Math.pow(val, mut_pow) - 1.0;
} else {
xy = 1.0 - delta2;
val = 2.0 * (1.0 - rnd) + 2.0 * (rnd - 0.5) * (java.lang.Math.pow(xy, (distributionIndex_ + 1.0)));
deltaq = 1.0 - (java.lang.Math.pow(val, mut_pow));
}
y = y + deltaq * (yu - yl);
if (y < yl)
y = yl;
if (y > yu)
y = yu;
x.setValue(var, y);
}
} // for
} // doMutation
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