Design of an Automatic Control Algorithm for Energy-Efficient ...
Design of an Automatic Control Algorithm for Energy-Efficient ...
Design of an Automatic Control Algorithm for Energy-Efficient ...
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8 System <strong>an</strong>d functionality integration 92<br />
The base <strong>an</strong>d the elite population as well as the r<strong>an</strong>dom immigr<strong>an</strong>ts size have to be<br />
bal<strong>an</strong>ced in order to give a good reaction speed <strong>an</strong>d a proper solution quality.<br />
First a set <strong>of</strong> base settings or base population has to be identified, as described in<br />
Section 6.3.3. These should cover reasonable set-points <strong>for</strong> common situations. A defog-<br />
preset should be present as well as settings <strong>for</strong> heat-up <strong>an</strong>d cool-down. Other options<br />
like a low fresh air supply <strong>an</strong>d a st<strong>an</strong>dard steady state input c<strong>an</strong> be defined. Doing these<br />
definitions it is good to make them dependent on the boundaries (or other parameters)<br />
to allow the functioning <strong>of</strong> the modes described in Section 8.4. In this way it is also<br />
guar<strong>an</strong>teed that they are included in the design space. It is recommended to spread them<br />
across the design space, which happens automatically when defining set-points <strong>for</strong> different<br />
conditions. Then the base population c<strong>an</strong> fulfil its task to speed up the optimisation by<br />
providing good starting points <strong>for</strong> most situations.<br />
The second influence on the population size is the number <strong>of</strong> elite-individuals that<br />
are tr<strong>an</strong>sferred from the last run. A high number increases the probability <strong>of</strong> a suitable<br />
solution among them, but it also leads to mediocre individuals being evaluated in every<br />
run. It also reduces the number <strong>of</strong> new, r<strong>an</strong>dom individuals being introduced into the<br />
population.<br />
For the controller values around ����� ��, ������ ��<strong>an</strong>d ������� ��have been<br />
chosen <strong>an</strong>d gave good results.<br />
8.6.2 Other settings<br />
Another import<strong>an</strong>t setting is the number <strong>of</strong> children. A high number will give higher<br />
ch<strong>an</strong>ces to find a better solution, but it increases the computational ef<strong>for</strong>t, too. Connected<br />
to this setting is the number <strong>of</strong> generations, ����. Each generation a new <strong>of</strong>fspring is<br />
created, so the overall number <strong>of</strong> system <strong>an</strong>d objective evaluations ����� <strong>for</strong> one optimiser<br />
run (each sample) is<br />
����� � ���� � ����<br />
¡ ��<br />
Since in each generation the diversity is reduced, a high number <strong>of</strong> generations will lead<br />
to a more precise solution, while a reduced number (with the same number <strong>of</strong> function
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