📄 crowding_distance.html
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<title>function f = crowding_distance(x,problem)</title>
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<h1>function f = crowding_distance(x,problem)</h1><pre class="codeinput"><span class="comment">% This function calculates the crowding distance</span>
[N,M] = size(x);
<span class="keyword">switch</span> problem
<span class="keyword">case</span> 1
M = 2;
V = 6;
<span class="keyword">case</span> 2
M = 3;
V = 12;
<span class="keyword">end</span>
<span class="comment">% Crowding distance for each front</span>
<span class="keyword">for</span> i = 1 : length(F(front).f)
y(i,:) = x(F(front).f(i),:);
<span class="keyword">end</span>
<span class="keyword">for</span> i = 1 : M
[sorted(i).individual,sorted(i).index] = sort(y(:,V + i));
distance(sorted(i).index(1)).individual = Inf;
distance(sorted(i).index(length(sorted(i).index))).individual = Inf;
<span class="keyword">end</span>
[num,len] = size(y);
<span class="comment">% Initialize all the distance of individuals as zero.</span>
<span class="keyword">for</span> i = 1 : M
<span class="keyword">for</span> j = 2 : num - 1
distance(j).individual = 0;
<span class="keyword">end</span>
objective(i).range = <span class="keyword">...</span>
sorted(i).individual(length(sorted(i).individual)) - <span class="keyword">...</span>
sorted(i).individual(1);
<span class="comment">% Maximum and minimum objectives value for the ith objective</span>
<span class="keyword">end</span>
<span class="comment">% Caluclate the crowding distance for front one.</span>
<span class="keyword">for</span> i = 1 : M
<span class="keyword">for</span> j = 2 : num - 1
distance(j).individual = distance(j).individual + <span class="keyword">...</span>
(sorted(i).individual(j + 1) - sorted(i).individual(j - 1))/<span class="keyword">...</span>
objective(i).range;
y(sorted(i).index(j),M + V + 2) = distance(j).individual;
<span class="keyword">end</span>
<span class="keyword">end</span>
</pre<p class="footer"><br>
Published with MATLAB® 7.0<br></p>
<!--
##### SOURCE BEGIN #####
%% function f = crowding_distance(x,problem)
% This function calculates the crowding distance
[N,M] = size(x);
switch problem
case 1
M = 2;
V = 6;
case 2
M = 3;
V = 12;
end
% Crowding distance for each front
for i = 1 : length(F(front).f)
y(i,:) = x(F(front).f(i),:);
end
for i = 1 : M
[sorted(i).individual,sorted(i).index] = sort(y(:,V + i));
distance(sorted(i).index(1)).individual = Inf;
distance(sorted(i).index(length(sorted(i).index))).individual = Inf;
end
[num,len] = size(y);
% Initialize all the distance of individuals as zero.
for i = 1 : M
for j = 2 : num - 1
distance(j).individual = 0;
end
objective(i).range = ...
sorted(i).individual(length(sorted(i).individual)) - ...
sorted(i).individual(1);
% Maximum and minimum objectives value for the ith objective
end
% Caluclate the crowding distance for front one.
for i = 1 : M
for j = 2 : num - 1
distance(j).individual = distance(j).individual + ...
(sorted(i).individual(j + 1) - sorted(i).individual(j - 1))/...
objective(i).range;
y(sorted(i).index(j),M + V + 2) = distance(j).individual;
end
end
##### SOURCE END #####
-->
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