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Larger gene pool for genetic algorithm initial population |
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| MathiasTholey |
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Anmeldedatum: 28.06.11
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Verfasst am: 21.07.2015, 10:29
Titel: Larger gene pool for genetic algorithm initial population
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Dear goMatlab community,
I am currently optimising the size of a combination of a chp unit and an auxiliary boiler. I feed the algorithm with all sorts of information about investment costs, fuel efficiencies, o&m costs and so on. As a start, I am looking at 12 timesteps only, in which the combination of chp unit and the boiler jointly have to supply heat to a city quarter. So I have chp unit power from 1:12, boiler power from 13:24 and at 25 and 26 the rated power of the two. The rated power of both chp unit and boiler is the final result.
For instance the needed heat supply is 20kW in all 12 timesteps. So chp unit power and boiler power have to meet this request. There are billions of combinations possible but my ga goes for quite a narrow range as for instance:
CHP Power [kW] 19.5 19.4 19.6 19.7. 19.65 .... Boiler [kW] 0.5 0.6 0.4 0.3 0.35 ....
The ga creates an intial population from which he starts optimisation. The algorithm ends up in a local minimum that is not the global minimum. The reason for this is that the genetics of its initial population is simply not different enough. With other words my individuals are too similar to each other.
However if I provide the ga with an initial population with a combination of a optimal solution from linprog and of the initial population that it usually comes up with, it finds the same solution as linprog and also as fmincon. You might now wonder, why I want to use ga in the first place...the answer is that I want to do multiobjective programming next, so I need my ga to work first...
Do you have any suggestions as to how to spread the gene variation further?
Thanks in advance,
Mathias
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