📄 initialize_variables.html
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<html xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd"> <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8"> <!--This HTML is auto-generated from an M-file.To make changes, update the M-file and republish this document. --> <title>initialize_variables</title> <meta name="generator" content="MATLAB 7.0"> <meta name="date" content="2006-03-16"> <meta name="m-file" content="initialize_variables"><style>body { background-color: white; margin:10px;}h1 { color: #990000; font-size: x-large;}h2 { color: #990000; font-size: medium;}p.footer { text-align: right; font-size: xx-small; font-weight: lighter; font-style: italic; color: gray;}pre.codeinput { margin-left: 30px;}span.keyword {color: #0000FF}span.comment {color: #228B22}span.string {color: #A020F0}span.untermstring {color: #B20000}span.syscmd {color: #B28C00}pre.showbuttons { margin-left: 30px; border: solid black 2px; padding: 4px; background: #EBEFF3;}pre.codeoutput { color: gray; font-style: italic;}pre.error { color: red;}/* Make the text shrink to fit narrow windows, but not stretch too far in wide windows. On Gecko-based browsers, the shrink-to-fit doesn't work. */ p,h1,h2,div { /* for MATLAB's browser */ width: 600px; /* for Mozilla, but the "width" tag overrides it anyway */ max-width: 600px; /* for IE */ width:expression(document.body.clientWidth > 620 ? "600px": "auto" );} </style></head> <body> <h2>Contents</h2> <div> <ul> <li><a href="#1">function f = initialize_variables(N, M, V, min_tange, max_range)</a></li> <li><a href="#2">Initialize each chromosome</a></li> </ul> </div> <h2>function f = initialize_variables(N, M, V, min_tange, max_range)<a name="1"></a></h2> <p>This function initializes the chromosomes. Each chromosome has the following at this stage * set of decision variables * objective function values </p> <p>where, N - Population size M - Number of objective functions V - Number of decision variables min_range - A vector of decimal values which indicate the minimum value for each decision variable. max_range - Vector of maximum possible values for decision variables. </p><pre class="codeinput">min = min_range;max = max_range;<span class="comment">% K is the total number of array elements. For ease of computation decision</span><span class="comment">% variables and objective functions are concatenated to form a single</span><span class="comment">% array. For crossover and mutation only the decision variables are used</span><span class="comment">% while for selection, only the objective variable are utilized.</span>K = M + V;</pre><h2>Initialize each chromosome<a name="2"></a></h2> <p>For each chromosome perform the following (N is the population size)</p><pre class="codeinput"><span class="keyword">for</span> i = 1 : N <span class="comment">% Initialize the decision variables based on the minimum and maximum</span> <span class="comment">% possible values. V is the number of decision variable. A random</span> <span class="comment">% number is picked between the minimum and maximum possible values for</span> <span class="comment">% the each decision variable.</span> <span class="keyword">for</span> j = 1 : V f(i,j) = min(j) + (max(j) - min(j))*rand(1); <span class="keyword">end</span> <span class="comment">% For ease of computation and handling data the chromosome also has the</span> <span class="comment">% vlaue of the objective function concatenated at the end. The elements</span> <span class="comment">% V + 1 to K has the objective function valued.</span> <span class="comment">% The function evaluate_objective takes one chromosome at a time,</span> <span class="comment">% infact only the decision variables are passed to the function along</span> <span class="comment">% with information about the number of objective functions which are</span> <span class="comment">% processed and returns the value for the objective functions. These</span> <span class="comment">% values are now stored at the end of the chromosome itself.</span> f(i,V + 1: K) = evaluate_objective(f(i,:), M, V);<span class="keyword">end</span></pre><p class="footer"><br> Published with MATLAB® 7.0<br></p> <!--##### SOURCE BEGIN #####
%% function f = initialize_variables(N, M, V, min_tange, max_range)
% This function initializes the chromosomes. Each chromosome has the
% following at this stage
% * set of decision variables
% * objective function values
%
% where,
% N - Population size
% M - Number of objective functions
% V - Number of decision variables
% min_range - A vector of decimal values which indicate the minimum value
% for each decision variable.
% max_range - Vector of maximum possible values for decision variables.
min = min_range;
max = max_range;
% K is the total number of array elements. For ease of computation decision
% variables and objective functions are concatenated to form a single
% array. For crossover and mutation only the decision variables are used
% while for selection, only the objective variable are utilized.
K = M + V;
%% Initialize each chromosome
% For each chromosome perform the following (N is the population size)
for i = 1 : N
% Initialize the decision variables based on the minimum and maximum
% possible values. V is the number of decision variable. A random
% number is picked between the minimum and maximum possible values for
% the each decision variable.
for j = 1 : V
f(i,j) = min(j) + (max(j) - min(j))*rand(1);
end
% For ease of computation and handling data the chromosome also has the
% vlaue of the objective function concatenated at the end. The elements
% V + 1 to K has the objective function valued.
% The function evaluate_objective takes one chromosome at a time,
% infact only the decision variables are passed to the function along
% with information about the number of objective functions which are
% processed and returns the value for the objective functions. These
% values are now stored at the end of the chromosome itself.
f(i,V + 1: K) = evaluate_objective(f(i,:), M, V);
end##### SOURCE END #####--> </body></html>⌨️ 快捷键说明
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