1 简介
2 部分代码
clc
clear all
close all
%-------------------------------------------------------------------------
% Sample Matlab Code for the Adaptive Wind Driven Optimization.
% The coefficients of the WDO are optimized by the CMEAS algorithm.
% No additional cost function calls are needed.
%
tic; clear; close all; clc;
format long g;
delete('WDOoutput.txt');
delete('WDOpressure.txt');
delete('WDOposition.txt');
fid=fopen('WDOoutput.txt','a');
%--------------------------------------------------------------
% User defined parameters:
param.popsize = 20; % population size.
param.npar = 10;% Dimension of the problem.
param.maxit = 100;% Maximum number of iterations.
maxV = 0.3; % maximum allowed speed.
dimMin = -100;% Lower dimension boundary.
dimMax = 100;% Upper dimension boundary.
% Unlike the classical WDO, Adaptive WDO does not need the coefficients to
% be predetermined. Coefficients; alpha, RT, g, and c, will be selected by
% the CMAES algorithm:
rec.arx = rand(4,param.popsize); %consistent with the CMAES indexing
%---------------------------------------------------------------
% Initialize WDO population, position and velocity:
% Randomize population in the range of [-1, 1]:
% Please note that WDO/AWDO always works in the range of [-1, 1], but is mapped
% to the upper/lower bounds of the problem before calling the pressure (cost,fitness) function.
pos = 2*(rand(param.popsize,param.npar)-0.5);
% Randomize velocity:
vel = maxV * 2 * (rand(param.popsize,param.npar)-0.5);
%---------------------------------------------------------------
% Call the pressure (cost,fitness) function
% Evaluate initial population one member at a time: (simple Sphere Function is utilized here)
for K=1:param.popsize,
% map the AWDO 'pos' vector to problem itself using the upper/lower boundaries of each dimension.
x = (dimMax - dimMin) * ((pos(K,:)+1)./2) + dimMin;
% call the pressure (cost,fitness) function:
pres(K,:) = fsphere(x); %here insert your own cost function and make sure the mapping above carried out properly.
end
%----------------------------------------------------------------
% Finding best air parcel in the initial population :
[globalpres,indx] = min(pres);
globalpos = pos(indx,:);
minpres(1) = min(pres);% minimum pressure
%-----------------------------------------------------------------
% Rank the air parcels:
[sorted_pres rank_ind] = sort(pres);
% Do not sort the air parcels! Sorting mixes up CMAES indexing.
% pos = pos(rank_ind,:);
keepglob(1) = globalpres;
%-----------------------------------------------------------------
% Start iterations :
iter = 1; % iteration counter
for ij = 2:param.maxit,
% Update the velocity:
for i=1:param.popsize
% choose random dimensions:
a = randperm(param.npar);
% choose velocity based on random dimension:
velot(i,:) = vel(i,a);
vel(i,:) = (1-rec.arx(1,i))*vel(i,:)-(rec.arx(2,i)*pos(i,:))+ ...
abs(1-1/rank_ind(i))*((globalpos-pos(i,:)).*rec.arx(3,i))+ ...
(rec.arx(4,i)*velot(i,:)/rank_ind(i));
end
% Check velocity:
vel = min(vel, maxV);
vel = max(vel, -maxV);
% Update air parcel positions:
pos = pos + vel;
pos = min(pos, 1.0);
pos = max(pos, -1.0);
% Call the pressure (cost,fitness) function
% Evaluate initial population one member at a time: (simple Sphere Function is utilized here)
for K=1:param.popsize,
% map the AWDO 'pos' vector to problem itself using the upper/lower boundaries of each dimension.
x = (dimMax - dimMin) * ((pos(K,:)+1)./2) + dimMin;
% call the pressure (cost,fitness) function:
pres(K,:) = fsphere(x); %here insert your own cost function and make sure the mapping above carried out properly.
end
% call CMAES with the coefficients and corresponding pressure, so
% that CMAES can return the new set of coefficient values for next
% iteration:
[rec] = purecmaes_wdo(ij,rec,param.popsize,pres);
%----------------------------------------------------
% Finding best particle in population
[minpres,indx] = min(pres);
minpos = pos(indx,:); % min location for this iteration
%----------------------------------------------------
% Rank the air parcels:
[sorted_pres rank_ind] = sort(pres);
% Do not sort the air parcels position, velocity and pressure!
% Instead use "rank_ind" if needed.
% pos = pos(rank_ind,:);
% vel = vel(rank_ind,:);
% pres = sorted_pres;
% Updating the global best:
better = minpres < globalpres;
if better
globalpres = minpres% global minimum pressure (cost,fitness) value
globalpos = minpos;% global minimum position vector, note that it is in the range of [-1, 1].
end
% Keep a record of the progress:
keepglob(ij) = globalpres;
% save WDOposition.txt pos -ascii -tabs;
end
%Save values to the final file.
pressure = transpose(keepglob);
filenamestr = ['WDOpressure.txt'];
save(filenamestr, 'pressure', '-ascii' , '-tabs');
% note that the 'globalpos' is the best solution vector found.
% 'globalpos' is in the range of [-1 1] and needs to be scaled with upper/lower bounds of the specific problem that you are trying to solve.
figure(1)
plot(keepglob)
xlabel('迭代次数')
ylabel('适应度函数')
% end-of-WDO.
%----------------------------------------------------------------------
%----------------------------------------------------------------------
%----------------------------------------------------------------------
% end-of-CMAES.
%----------------------------------------------------------------------
%----------------------------------------------------------------------
%----------------------------------------------------------------------
3 仿真结果
4 参考文献
[1]陈彬彬, 曹中清, 余胜威. 基于风驱动优化算法WDO的PID参数优化[J]. 计算机工程与应用, 2016, 52(014):250-253.
[2]寻琦, 赵惠玲, and 王肇平. "一种基于自适应风驱动优化算法的天线阵方向图综合方法.".
部分理论引用网络文献，若有侵权联系博主删除。
5 MATLAB代码与数据下载地址
见博客主页
