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3.5 Run the Simulation(s)

The last step of the workflow comes once a
base case scenario has been modeled, and the daylight and thermal simulations
have been tested. This task is performed by the plugin Octopus, which is a
multi-objective optimization tool. As Robert Vierlinger puts it, “Octopus
introduces multiple fitness values to the optimization. The best trade-offs
between those objectives are searched, producing a set of possible optimum
solutions that ideally reach form one extreme trade-off to the other”
(Vierlinger 2014).

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One of the advantages of using octopus in
comparison with other similar optimization tools in Grasshopper is the ability
to define multiple objectives that can be evaluated simultaneously which
provides optimal results, presenting the best mode of objectives. This approach of

optimization enables the examination of
correlations between different objectives and provide a more comprehensive
arrangement of outcomes compared to single objective optimization analysis
(Deb, 2001).

The optimization studies included on running
three generations (sets) with a population of 30 elements each to determine the
best correlations between sUDI and EU. The result of multiple iterations
performed by the optimization analysis are scattered through a
three-dimensional graphic that presents the results obtained by the analysis.
After running several simulations, the graphic gets populated with the best
trade-offs between the different objectives, conforming a well-defined
arrangement of the boundaries in which all possible best trade-offs could
occur. Ordering the results in
the graph leads to the Pareto Front (Fig.4).
A point to be mentioned is that optimization
studies are not comprehensive and that means necessarily the most optimal
answers has not been achieved. The design variables (parameters) that is
intended to simulate were variables relating to the windows: window to wall
ratio, number of windows, window height, sill height. octopus through repeating
the various arrangements of the design variables, offers
the most efficient options.

In the graphical representation of the
optimization, one can find patterns within the multiplicity of results, because
of that, further speculations about the correlations between the different
objectives can be elaborated. For example, a direct correlation between
daylighting and energy consumption was found, Fig.4 describes how any increments
in UDI are accompanied by a linear reduction of EU. This directly occurs
because less energy is consumed for electric lighting with any increment in
daylighting.