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TOPSIS is an algorithm to determine the best choice out of many using Positive Ideal Solution and Negative Ideal

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Source code for TOPSIS optimization algorithm in python.

TOPSIS is an algorithm to determine the best choice out of many using Positive Ideal Solution and Negative Ideal Solution.

For sample solutions visit: http://www.jiem.org/index.php/jiem/article/view/573/498 WikiPedia: https://en.wikipedia.org/wiki/TOPSIS

TOPSIS is an acronym that stands for ‘Technique of Order Preference Similarity to the Ideal Solution’ and is a pretty straightforward MCDA method. As the name implies, the method is based on finding an ideal and an anti-ideal solution

In Command Prompt

topsis data.csv "1,1,1,1" "+,+,-,+" final.csv Sample dataset The decision matrix (a) should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R2, Root Mean Squared Error, Correlation, and many more.

Model Correlation R2 RMSE Accuracy
M1 0.79 0.62 1.25 60.89
M2 0.66 0.44 2.89 63.07
M3 0.56 0.31 1.57 62.87
M4 0.82 0.67 2.68 70.19
M5 0.75 0.56 1.3 80.39

Weights (w) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in I.

Output Model | Score | Rank


1 | 0.77221 | 2 2 | 0.225599 | 5 3 | 0.438897 | 4 4 | 0.523878 | 3 5 | 0.811389 | 1

The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.

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