Many and Multi-objective datasets used in MIP-DoM

Citation Author(s):: Claudio Lucio do Val Lopes (CEFET-MG)

Flávio Vínicius Cruzeiro Martins (CEFET-MG)

Elizabeth F. Wanner (Centro Federal de Educação Tecnológica de Minas Gerais and Aston University)

Kalyanmoy Deb (Michigan State University)
Submitted by:: Claudio Lopes
Last updated:: Thu, 11/12/2020 - 15:06
DOI:: 10.21227/vh2b-1803
Data Format:: *.csv; *.txt
Links:: Many and Multi-objective datasets used in MIP-DoM

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Categories:

Computational Intelligence

Keywords:

Multi-objective optimization

many-objective optimization

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Abstract

This dataset brings some problem sets and results from some classical algorithms from the evolutionary computational community.

We have used some tools: Pymoo, Platypus and Pagmo

Instructions:

We have some directories with the d_L_M mask. It means the following information:

L= number of solutions in the problem set

M = number of objectives in the problem set

For the directory d_50_3 each file represents the problem set = [DTLZ1,DTLZ2,DTLZ3,DTLZ7, WFG1,WFG2,WFG3,WFG9], the algorithm = [IBEA, MIOEA/D, NSGAII, NSGAIII, SPEA2].

All the other directories were genereated using the DTLZ1 problem set