The COMOLA Results
This application analyses OPTAIN optimisation outputs and shall support decision making. While all solutions provided by the SWAT+ / COMOLA workflow are pareto-optimal (none of the objectives can be improved without losses in other objectives), choosing among a large number of solutions can be daunting.
To reduce complexity while minimising information loss, this application provides two ways to filter/reduce the pareto front:
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A clustering algorithm based on a Principal Component Analysis (PCA) and kmeans/kmedoids. The user can modify the clustering process, alter the number of tested clusters and the way outliers are handled or how much correlation is accepted across the considered variables. Finally, those optima representative for different clusters can be plotted and the measure implementation they recommend can be compared.
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An Analytical Hierarchy Process that can be run as standalone method as well as as additional feature on top of the clustered pareto front.
The application is structured the following way:
The second tab Visualising the Pareto Front provides an overview over the optimisation results. The user can gain insights into the relationships between the objectives and the pareto front by selecting and plotting preferred objective ranges.
The third tab Data Preparation is needed to produce the data required for the subsequent analyses. Several files need to be provided so the variables considered in the clustering can be calculated.
The fourth tab Configure Clustering allows to perform the clustering with default settings or to jump to the optional tabs for manual clustering.
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The tab Clustering Part 1 - Correlation Analysis can only be accessed if manual clustering is chosen in the Configure Clustering tab. It allows to assess and alter variables considered in the subsequent clustering.
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The tab Clustering Part 2 - PCA & kmeans/kmedoids provides the possibility to adapt, modify and finally perform the clustering process.
The Cluster Analysis tab lets the user plot the optima remaining after the clustering. Each of these optima is representative for one cluster.
The tab AHP - Analytical Hierarchy Process allows to determine priorities across the pareto front in a different way through assigning weights across the optima. It is possible to combine the clustering results with the AHP.
To ensure compatibility with algorithms (e.g. CoMOLA) designed for maximisation, some projects used negative numbers. Please note that, unless an objective uses mixed signs, this app omits the minus sign of these values. The interpretation however remains unchanged.