Design of an Automatic Control Algorithm for Energy-Efficient ...

6 The optimiser: an evolutionary algorithm approach 55
6.3 Design of an incremental micro evolutionary
algorithm
While approaches of using evolutionary algorithms in online control have already been
made, the circumstances in these cases are not comparable to the ones in car climate
control. In the case of the building climate control [28] the population size was set to 100
and 500 generations were evaluated, which lead to a lot of function evaluations. This is
possible in a building where more computing power is available, but in a car it is not (yet).
The second example close to the planned control layout is the set-point optimisation for
an ethanol fermentation process [32]. Here however, the sample time was thirty minutes.
For an climate control application it should be not more than several seconds. Therefore,
an adapted algorithm is required.
6.3.1 Overview of IMEA
In the design of the evolutionary algorithm for the car climate control optimisation par-
ticular attention is paid to the computation demand. In comparison to usual applications
of EAs the simulated system is through the applied simplifications quite small and in the
range of a simple MPC-problem. Thus, the computational effort of the algorithm itself
cannot be neglected, as it is often done [34].
The main ideas of the developed incremental micro evolutionary algorithm are:
• Use a small population in order to reduce computing time.
• Have a fixed amount of function evaluations to give a deterministic run time.
• Ensure feasible solutions through a base population inserted at each run.
• Adapt mutation to the changes of the environment.
• Employ a special ranking scheme to penalise exceeded limits differently.
• Augment efficiency through elitism (storing the best individuals for the next run).
• Avoid double entries and keep diversity to be able to react to changes.