Public Project Portfolio Optimization under a Participatory Paradigm

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PROCEDURE NSGA-II (𝐾^′, Number_of_Generations)

Initialize Population 𝑃

Generate random population with size 𝐾^′

Evaluate objective values

Assign rank (level) based on Pareto dominance

Generate Child Population 𝑄 with size 𝐾^′

   Perform Binary Tournament Selection

   Perform Recombination and Mutation

FOR 𝐼=1 to Number_of_Generations DO

  Assign 𝑃^′=𝑃∪𝑄

  FOR each parent and child in 𝑃^′ DO

   Assign rank (level) based on Pareto dominance

   Loop (inside) by adding solutions to the

   next generation starting from the first front

   until 𝐾^′ individual found; determine crowding

   distance between points of each front

  End FOR

  Select points (elitist) of the lower front (with lower rank) and are outside

  a crowding distance

  Replace 𝑃 by the 𝐾^′ individual found

  Generate Child Population 𝑄 with size 𝐾^′

   Perform Binary Tournament Selection

   Perform Recombination and Mutation

End FOR

End PROCEDURE