In this code, we are implementing elite Multi Criteria Decision Making - Pareto Front (eMPF) for solving multi-objective optimization problem. In addition, we also provide code for Multi Criteria Decision Making - Pareto Front (MPF), Non-dominated Sorting Genetic Algorithm - II (NSGA-II), and Non-dominated Sorting Genetic Algorithm - III (NSGA-III) techniques. This repository also contains, data generated using code, Python code to perform the analysis, and graphs for the data. In addition, issues sections covers all the issues and testing performed on the code along with timeline.
In this code the following test functions are used. Please refer (paper) for the equations corresponding to the test function along with constraints and search domain in Appendix A.
- Binh and Korn Function
- Chancing and Haimes
- Fonseca-Fleming
- Test Function 4
- Kursawe
- Schaffer function N1
- Schaffer function N2
- Poloni’s two objectives
- Zitzler-Deb-Thiele’s N1
- Zitzler-Deb-Thiele’s N2
- Zitzler-Deb-Thiele’s N3
- Zitzler-Deb-Thiele’s N4
- Zitzler-Deb-Thiele’s N6
- Osyczka and Kundu
- Const-Ex
- VR_UC Test 1
- VR-UC Test 2
- MSGA Test 1
- MHHM1
- MHHM2
- Viennet Function
Please find the code for the following algorithms. In (paper), we do not use Simple EA provided in the code. It was used for testing to test functions, range of search domain, methodologies, and simulation runs. While the Simple EA can utilized a summation of objectives, this approach is not ideal for solving multi-objective optimization problems. The results from the simulation are presented in (paper) Appendix B.
- Simple EA
- NSGA - II
- NSGA - III
- MPF
- eMPF